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Quantifying iron levels in the YAC128 mouse model of Huntington's Disease Muller, Michelle 2014

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       QUANTIFYING IRON LEVELS IN THE YAC128 MOUSE MODEL OF HUNTINGTON¶6 DISEASE    by  Michelle Muller  B.App.Tech., Fanshawe College, 2011     A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF SCIENCE  in  THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Neuroscience)    THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   April 2014           © Michelle Muller, 2014   ii ABSTRACT +XQWLQJWRQ¶V'LVHDVH+'LVRQHRIPDQ\QHXURGHJHQHUDWLYHGLVHDVHVwith reported alterations in brain iron homeostasis. Many neurodegenerative diseases exist which are characterized by brain iron accumulation. Whether elevated brain iron occurs in HD, and whether it plays a significant contributory role in pathogenesis or is a secondary effect is currently unclear. Iron accumulation in specific brain areas of neurodegeneration in HD has been proposed based on observations in post-mortem tissue and magnetic resonance imaging (MRI) studies. Altered MRI signal within specific brain regions undergoing neurodegeneration has been consistently reported and interpreted as altered levels of brain iron. Biochemical studies using various techniques to measure iron species in human samples, mouse tissue, or in vitro has generated equivocal data to support such an association. I have reviewed previous and current published literature reporting iron alterations and summarized the findings in this dissertation. Current consensus remains unclear if iron plays a contributing role, and further studies that modulate iron levels in HD models to assess the effects of iron are required.   The experimental aim of this thesis was to measure iron-related changes in the YAC128 HD mouse model with the hypothesis that this mouse model will develop elevated striatal iron levels compared with wild-type littermates. Using analytical techniques to measure levels of elemental brain iron, no significant differences were observed in various brain regions of aged HD mice and in post-mortem HD samples.    iii PREFACE Part of this thesis dissertation is in press as a review enWLWOHG³,URQDysregulation in +XQWLQJWRQ¶V'LVHDVH´WRWKH-RXUQDORI1HXURFKHPLVWU\ (Manuscript: JNC-2014-0097). I am the first and main author of the review, with peer review assistance mainly from Dr. Blair Leavitt, and contributions from Dr. Terri Petkau. This review is included as a major part of the introduction and discussion.   Experiments in this thesis were performed in accordance with animal care guidelines from the UBC Animal Care Committee and the Canadian Council on Animal Care (certificate number: A09-0673). Human striatal and cortical post-mortem samples were obtained from the Huntington Disease Biobank at the University of British Columbia (UBC) with approval from UBC Ethics.     iv   TABLE OF CONTENTS ABSTRACT ....................................................................................................................................... ii PREFACE ......................................................................................................................................... iii LIST OF TABLES ............................................................................................................................ vi LIST OF FIGURES .......................................................................................................................... vii LIST OF ABBREVIATIONS ........................................................................................................... ix ACKNOWLEDGEMENTS ............................................................................................................. xii DEDICATION ................................................................................................................................ xiii &+$37(5,1752'8&7,2172+817,1*721¶V',6($6( ..................................................... 1 1.1  Clinical and Pathological Features ........................................................................................... 1 1. 2  Genetics ................................................................................................................................... 3 1.3 Wild-type Huntingtin Protein Function ..................................................................................... 4 1.4 Proposed Mechanisms of Mutant Huntingtin Toxicity .............................................................. 6 1.5 Mouse Models of HD .............................................................................................................. 11 CHAPTER 2 : INTRODUCTION TO IRON....................................................................................... 15 2.1  The Role of Iron in Biological Systems .................................................................................. 15 2.2 *HWWLQJ³5XVW\´ZLWK$JH ........................................................................................................ 17 2.3 Altered Iron Levels in Brain Diseases ..................................................................................... 18 2.4  Detection of Iron Levels in the Brain ..................................................................................... 20 CHAPTER 3 : ALTERED IRON LEVELS IN HD ............................................................................. 23 3.1 Clinical Evidence ..................................................................................................................... 23 3.2 Neuroimaging Evidence .......................................................................................................... 24 3.3 Biochemical and Histochemical Evidence ............................................................................... 27 3.4 Iron Chelation Studies in HD Mouse Models .......................................................................... 34   v 3.5 Wildtype Huntingtin Has a Functional Role in Iron Regulation ............................................. 35 EXPERIMENTAL AIM & HYPOTHESIS ......................................................................................... 37 CHAPTER 4 : METHODS .................................................................................................................. 38 4.1 Animal Husbandry & Surgery ................................................................................................ 38 4.2 Magnetic Resonance Imaging ................................................................................................. 39 4.3 Magnetic Resonance Spectroscopy ......................................................................................... 39 4.4 Immunohistochemistry ............................................................................................................ 40 4.5 Graphite Furnace Atomic Absorption Spectroscopy .............................................................. 41 4.6 Iron Loading Studies in Mice .................................................................................................. 44 4.7 Colorimetric Ferrous Iron Assay ............................................................................................. 44 4.8 Statistics .................................................................................................................................. 44 CHAPTER 5 : RESULTS .................................................................................................................... 46 5.1 Regional Brain Iron Levels Measured by MRI are Similar in YAC128 and WT Mice .......... 46 5.2 Brain Metabolite Concentrations and FWHM Quantification via MRS Showed No Significant Differences in 19-Month Old YAC128 and WT Mice .................................................. 51 5.3 Immunohistochemistry Shows No Significant Differences in Ferric Iron Staining in YAC128 versus WT Mice ............................................................................................................................... 56 5.4 Total Elemental Brain Iron Levels Showed No Significant Differences in Aged YAC128 versus WT Mice ............................................................................................................................... 58 5.5 Post-mortem HD Striatal and Cortical Brain Tissue Show No Trend of Increased Iron Levels 62 5.6 Intraperitoneal Injections of Iron in Mice Does Not Affect Striatal Iron Uptake and Does Not Show Differential Accumulation in YAC128 versus WT Mice ....................................................... 64 5.7 Fe2+ Levels Did Not Differ in Brain Regions of Aged YAC128 versus WT Mice ................. 67 CHAPTER 6 : DISCUSSION .............................................................................................................. 69 BIBLIOGRAPHY ................................................................................................................................ 76    vi LIST OF TABLES  Table 3.2-1: Magnetic resonance imaging studies quantifying iron levels in HD brain ........ 26 Table 3.3-2: Histochemical and biochemical studies assessing iron accumulation in human HD ........................................................................................................................................... 29 Table 4.5-1: Characteristics of post-mortem brain tissue. ..................................................... 44     vii LIST OF FIGURES Figure 5-1: (A) Representative SWI images from an 18 month-old mouse; Overlaid with anatomical C57BL/6  mouse brain atlas using FSLview software: (B) Coronal view; (C) Axial view; (D) Sagittal view. ................................................................................................ 48 Figure 5-2: Magnitude of relaxation maps (R2*) calculated from various brain regions of interest: thalamus, pallidum, hippocampus, and caudate/putamen. Measured in 18 month-old YAC128 (n=4) and WT littermates (n=6). Box plot central mark express the median, the edges of the box are the 25th and 75th percentiles, and the whiskers extend to the most extreme data points. Statistics used was unpaired t-test to calculate p-values. ...................... 50 Figure 5-3: Quantified R2* values for brain regions of interest compared to the average across all 18 month-old YAC128 (n=4) and WT (n=6) mice. Note: positive control mouse #160 (left). x-axis labeled by mouse-identification number. The connected dots are of a particular brain region indicated by color. .............................................................................. 51 Figure 5-4: Representative magnetic resonance spectral graph with labeled detectable brain metabolites (left). Voxel 2x2x2mm3 placed over left caudate/putamen (right). ..................... 53 Figure 5-5: Relative FWHM values calculated from magnetic resonance spectra graphs from the left putamen and frontal cortex of  HD patients (pre-HD (n=25) and early HD (n=30)) and controls (n=30). This data was produced from Sturrock  et al. (unpublished). Statistics XVHGLQFOXGH6WXGHQW¶VW-test between genotypes in each group, and two-way analysis of variance for group differences between brain regions and genotype. .................................... 54 Figure 5-6: (Left) Relative FWHM values calculated from MRS in 19 month-old YAC128 (n=4) and WT (n=6) mice. (Right) Concentration of various brain metabolites measured by MRS. Statistics used include unpaired t-test to detect difference between genotypes of FWHM data and for each brain metabolite............................................................................. 56 Figure 5-7: (A) Representative figures of WT and YAC128 Perls perfused striatum at 100x magnification.  DAB enhancement is used to identify Fe3+-positive cells and counterstained with cresyl violet. (B) The density of iron-positive cells in the striatum of 1 year-old <$&DQG:7OLWWHUPDWHVQ PLFHSHUJHQRW\SH6WDWLVWLFXVHGZDV6WXGHQW¶VW-test to compare genotypes.................................................................................................................. 58 Figure 5-8: Total elemental iron levels in red blood cells from 18 month-old and 24 month-old mice, respectively (n=3 and n=5 YAC128; n=2 and n=3 WT), measured by GFAAS. Statistics used was an unpaired t-test and two way analysis of variance. ............................... 60 Figure 5-9: Total elemental iron levels in 5 different brain regions of two year-old mice (n=14 YAC128; n = 9 WT) measured by GFAAS. Statistics used was unpaired t-test between genotypes for each brain region. ............................................................................................. 62   viii Figure 5-10: (A) Total elemental iron levels in post-mortem striatum and cortex tissue from HD patients and controls (n=10 per genotype) measured by GFAAS. (B) Non-heme iron levels in post-mortem striatum and cortex tissue from HD patients and controls (n=10 per genotype, new samples used between A and B). Statistics used was unpaired t-test between controls and HD for each brain region. ................................................................................... 64 Figure 5-11: Total iron levels in striatum of 2 year-old mice after IP injections (70 mg/kg) iron-dextran or saline control. White bar = saline treated WT (n=4); Gray bar= iron-dextran treated WT (n=7); Black bar = iron-dextran injected YAC128 (n=3). Statistics used was unpaired students t-test between groups. ................................................................................ 66 Figure 5-12: Total iron levels in liver of 2 year-old mice after IP injections (70 mg/kg) iron-dextran or saline control. White bar = saline treated WT (n=4); Gray bar= iron-dextran treated WT (n=7); Black bar = iron-dextran injected YAC128 (n=3). Statistics used was unpaired students t-test between groups. ................................................................................ 67 Figure 6-1: Iron and mHtt-induced oxidative stress in HD. .................................................. 72 Figure 6-2: Potential interactions of Iron and mHtt-induced oxidative stress in HD ............ 75     ix LIST OF ABBREVIATIONS 3-NP  3-nitroproprionic acid 7T  7 Tesla  AAS  atomic absorption spectroscopy ACBD3 Acyl-coenzyme A binding domain containing 3 protein AD  $O]KHLPHU¶V disease BDNF  brain-derived neurotrophic factor BAC  bacterial artificial chromosome C-terminal  carboxy-terminal C(II)  mitochondrial complex II  CAG  cytosine-adenosine-guanine (trinucleotide repeat) CAPON carboxyl-terminal PDZ ligand of neuronal nitric oxide synthase protein CNS   central nervous system CSF  cerebrospinal fluid D1/D2  dopamine receptors 1 and 2 Dexras1 dexamethasone-induced Ras-related protein 1 DMT1  divalent metal transporter 1 DTI  diffusion tensor imaging FA  fractional ansiotropy FDRI  field-dependent relativity increase Ferrous iron  Fe2+ Ferric iron  Fe3+ Fe-S  iron-sulfur  G protein guanosine nucleotide-binding proteins GABA  Ȗ-Aminobutyric acid GABAergic a neuron that outputs GABA GAPDH glyceraldehyde 3-phosphate dehydrogenase GFAAS graphite furnace atomic absorption spectroscopy GP  globus pallidus H2O2  hydrogen peroxide HD   Huntington¶V disease Hdh  huntingtin (gene/protein) in mouse HIF-Į hypoxia inducible factor-Į HTT  huntingtin (gene) in human Htt  huntingtin (protein) in human ICP-MS inductively-coupled plasma mass spectroscopy   x IRE  iron-responsive element IRP  iron-response protein LIP  labile iron pool LfR  lactoferrin receptor MD  mean diffusivity MFC  magnetic field correlation MFI  magnetic field inhomogeneities mHtt  mutant huntingtin protein MRI  magnetic resonance imaging MRS  magnetic resonance spectroscopy MS  Multiple sclerosis MSN  medium spiny neuron mTOR  mammalian target of rapamycin N-terminal  amino-terminal NBIA  neurodegeneration with brain iron accumulation  NMDA  N-methyl-D-aspartate NMDAR  N-methyl-D-aspartate receptor nNOS  neuronal nitric oxide synthase NO  nitric oxide O2*-  superoxides ONOO- peroxynitrite radicals OH*/OH*- hydroxyl radicals PD  3DUNLQVRQ¶VGLVHDVH polyQ  polyglutamine PMI  post-mortem interval PTMs  post-translational modifications QA  quinolinic acid Rhes  ras homolog enriched in striatum RNS  reactive nitrogen species  ROS   reactive oxygen species SOD  superoxide dismutase SN  substantia nigra SWI  susceptibility-weighted imaging T1  longitudinal relaxation time  T2  transverse relaxation time TfR  transferrin receptor TRiC   chaperonin TCP1 ring complex  UBC  University of British Columbia UHDRS Unified Huntington's Disease Rating Scale WT   wild-type   xi YAC128  yeast artificial chromosome with 128 CAG   xii ACKNOWLEDGEMENTS Thank you to various collaborators at the UBC MRI Research Centre: Dr. Andrew Yung and Dr. Piotr Kozlowski for conducting MRI and MRS on the mice utilized in these experiments; and Dr. Alex Rauscher and his PhD student Vanessa Wiggermann for analyzing the MRI data.   Thanks to Dr. Kevin Soulsbury for providing me with the opportunity to utilize a new graphite furnace atomic absorption spectrophotometer at British Columbia Institute of Technology (BCIT). This instrument was used to gather total elemental iron data under Dr. 6RXOVEXU\¶Vsupervision. Bob Nicholson, the BCIT lab technician was also very helpful in providing me with supplies and advice during my experiments at BCIT.   I would also like to acknowledge Dr. Lynn Raymond and technician Lily Zhang for providing space to house some of the mice used in experiments in the Animal Research Unit. As well as Austin Hill from the Leavitt Lab, for aiding in obtaining mice from the Transgenic Unit at the Centre for Molecular Medicine & Therapeutics (CMMT). This also leads me to acknowledge the transgenic Mus musculus that were bred and sacrificed for experimental use.  I am very thankful to the very wonderful Leavitt lab technician, Ge Lu, who performed all iron injections, perfusions, and surgery/microdissections to the mice. The Leavitt Lab/Hayden Lab research associate Dr. Sonia Franciosi, helped prepare post-mortem brain samples from the HD BioBank at CMMT and provided assistance on the stereology microscope. Thank you to my supervisor Dr. Blair Leavitt, for his bravery to take me on as a student and providing me with this amazing opportunity for academic experience in the +XQWLQJWRQ¶VGLVHDVHUHVHDUFKILHOG  Most importantly, I want to thank my amazing family for supporting me through graduate school and providing me with the ambition and pursuit to follow my dreams. I am grateful for the many great role models I have crossed paths with in my life who have inspired me, including C Ketola, C Brooks, and many many more.      xiii DEDICATION I would like to dedicate this dissertation to myself, my family, including deceased ones, my friends, and everyone else who has to suffer ± directly or indirectly - from this interesting transcript 15 genetic disease, also known as ³+XQWLQJWRQ¶V disease´. However, I prefer not to describe this disease as belonging to Dr.. George Huntington, despite being the first to clearly describe it.  Rather, to emphasize an evolutionary perspective on the condition, I prefer to describe it as inherting a very interesting transcript 15. One good friend in Paris playfully refers to the hard-to-SURQRXQFH+XQWLQJWRQ¶V DV µ'LQJ'DQJ'RQJ¶ -- sounds of a joyful ringing bell (Frère Jacques). [Image below taken with permission from dingdingdong.org]         1 CHAPTER 1: INTRODUCTION 72+817,1*721¶6DISEASE  1.1  Clinical and Pathological Features Huntington¶V Disease (HD) is a dominantly inherited progressive neurodegenerative disease. A recent Canadian study in British Columbia found the prevalence to be approximately 14 per 100,000 people in the general population, and 17 per 100,000 in the Caucasian population (Fisher and Hayden 2013). The insidious onset of the disease presents with subtle and progressive cognitive deficits in attention, working memory, and executive functioning, along with behavioral changes and/or psychiatric symptoms (e.g. depression, irritability, apathy, aggression) (Martin and Gusella 1986). The other prominent symptom in HD is the development of chorea (Greek translation: dance), manifesting as dystonia and eventually developing into bradykinesia. There is an overall worsening of symptoms over a period of 10 to 20 years until death (Novak and Tabrizi 2010). A distinctive pattern of neuropathology in HD that occurs early in the disease process targets the GABAergic medium spiny neurons (MSNs) that comprise 95% of neurons in the caudate and putamen (known collectively as the striatum). Neuronal loss and astrogliosis proceed in a dorsomedial to ventrolateral direction resulting in striatal atrophy and an enlargement of lateral ventricles that associates with subclinical progression of disease (Albin and Tagle 1995; Pavese et al. 2006). The Vonsattel grading system describes the gradual severity of HD degeneration from grades 0-4 and, to the naked eye, gross pathology of grade 0 is nearly identical to a normal brain but there is in fact up to 30-40% of neuronal loss in the head of the caudate (Vonsattel et al. 1985). However, by grade 4 of the disease progression there is up to 95% loss of neurons. During initial stages of disease there is gradual progressive degeneration of the striatopallidal white matter projections, resulting in compromised connectivity between the striatum, globus pallidus (GP), and substantia nigra (SN). Two populations of MSNs exist in the striatum, constituting different striatal   2 GABAergic output pathways that project to different segments of the GP, either externally or internally (GPe/GPi). These MSNs are differentially affected in HD neuropathology, with the indirect pathway projecting to the GPe and being relatively more susceptible to degeneration than MSNs of the direct pathway (Surmeier et al. 1996; Figueredo-Cardenas et al. 1998; Menalled et al. 2000; Deng et al. 2004). The large aspiny cholinergic interneurons and other intraneuronal GABAergic populations are less affected in HD pathology (Cicchetti et al. 2000). Elevated numbers of oligodendrocytes appear years before the appearance of symptoms and may represent attempted repair and remyelination by these cells (Myers et al. 1991; Gómez-Tortosa et al. 2001).  Neurons also progressively degenerate in the cortex, hypothalamus, and hippocampus; specifically the large pyramidal projection neurons are selectively lost, resulting in atrophy throughout the entire brain by late-stage HD (Albin and Tagle 1995; Douaud et al. 2009). It is believed neuronal loss in specific areas of cortex correlates with symptoms--cell loss in the motor cortex results in motor dysfunction and cell loss in the cingulate cortex tends to correlate with onset of affective disorders (Thu et al. 2010). Various neuroimaging techniques have been applied to assess neuropathology in HD patients. MRI-based morphometric analysis is used to detect significant volume reduction in the caudate, putamen, and cortex that can occur up to ten years before the onset of symptoms (Rosas et al. 2001; Rosas et al. 2003; Rosas et al. 2006; Jurgens et al. 2008; Douaud et al. 2009; Henley et al. 2009; Tabrizi et al. 2009; Paulsen et al. 2010; Nopoulos et al. 2011; Tabrizi et al. 2011).  Other pathogenic features of HD immune system activation and peripheral manifestations have been detected up to 16 years prior to predicted disease onset in plasma, cerebrospinal fluid (CSF) and the striatum of HD patients (Björkqvist et al. 2008). HD is mainly thought of as a CNS disorder, but abnormal cellular phenotypes have been described in fibroblasts, red and white blood cells, pancreatic, liver, muscle, cardiac cells, bones and testes, in culture or patient samples (Sassone et al. 2009). Interestingly, the testes are an organ of severe degeneration with HD progression (Van Raamsdonk et al. 2007), and have similar higher gene expression pattern of Htt as the brain (Guo et al. 2003). The testes also express multiple glutamate transporters, including unusually spliced forms, suggesting that glutamate homeostasis is critical in this organ (Lee et al. 2011). Occurrence of abnormal   3 energy metabolism, muscle wasting, and weight loss despite increased caloric intake are also invariable features of HD (Lodi et al. 2000; Aziz et al. 2008).  1. 2  Genetics The genetic basis of HD is a dominant mutation in exon 1 of the HTT gene (previously referred to as the IT15 JHQH IRU ³Interesting Transcript 15´ coding for the huntingtin (Htt) protein (Rubinsztein et al. 1993). Each offspring of a parent with HD has a 50% chance of inheriting the disorder. The mutation is a cysteine-adenosine-guanine (CAG) trinucleotide repeat expansion that results in the production of an abnormally long polyglutamine (polyQ) tract containing 37 glutamines or more in the Htt protein, which is then referred to as mutant Htt (mHtt).  The gene for Huntingtin first appeared ~800 million years ago in the amoeba, and the polyQ tract located in exon 1 of the Htt gene evolved ~450 million years ago, at the split of the protostome and deuterostome species (Tartari et al. 2008). These are main lineages of Bilateria, animals having bilateral symmetry and contain three germ layers in embryogenesis. The protostome branch (insects, nematode, etc.) remained without a polyQ tract, but the deuterostome branch developed the first polyQ of 2 CAG repeats in one of the oldest living deuterostome species, the sea urchin.  The length of the polyQ tract then gradually increased with the evolution of higher vertebrates and mammals in the deuterostome branch: 4 Qs in fish, birds, and amphibians, 7 Qs in mice, 8 Qs in rats, and the most variable and polymorphic polyQ tract in human species (Tartari et al. 2008). The HTT CAG tract in human populations normally varies from ~7 to 26 glutamines, but repeat lengths of up to 35 can occur without any known adverse effects (The +XQWLQJWRQ¶V 'LVHase Collaborative Research Group, 1993). The length of the expanded polyQ in HD inversely correlates with the age of onset (Duyao et al. 1993), accounting for  ~70% of the variation, with a mean age onset at 40 years (Gusella and MacDonald 2009). When the CAG tract has a range of 27-35 repeats in unaffected individuals, this is known as DQ µLQWHUPHGLDWH DOOHOH¶ (Semaka et al. 2013a). About 6% of the general population carry intermediate alleles  and, within this range, there is the potential for the CAG tract to expand into disease range in future offspring (Semaka et al. 2013a). About ~10% of patients tend to   4 inherit a chromosome that underwent CAG expansion into disease range without a prior history of HD in the family, and this is more common in the European haplogroup which has a higher prevalence of intermediate alleles and CAG instability (due to the presence of cis- elements) (Warby et al. 2009). Anticipation is a term that describes an earlier onset of disease or more severe disease presentation in successive generations, and in HD this correlates with paternal transmission (Ranen et al. 1995). This is thought to be due to the instability of the HD CAG repeat during male gametogenesis, the mechanism of which is not yet fully understood (Duyao et al. 1993). However, the probability of the offspring polyQ tract to expand in length significantly increases as the parental polyQ tracts become longer than average (Semaka et al. 2013b). Somatic expansion of the CAG repeat in the HTT gene can also occur during DNA replication, transcription, and repair (De Rooij et al. 1995). Somatic CAG repeat expansions are most extreme in the striatum of HD brain, and can occur in the cortex of HD subjects that have an earlier disease onset. Somatic expansion has been postulated to accelerate the disease process (Gonitel et al. 2008; Goula et al. 2009). 1.3 Wild-type Huntingtin Protein Function  The 3144 amino acid 350 kDa Htt protein is ubiquitously expressed, albeit at relatively low expression levels peripherally (Ferrante et al. 1997) and much higher expression levels in the central nervous system (CNS) and testes (Ferrante et al. 1997). During embryonic development, Htt is required for gastrulation and formation of the nervous system, and Htt has been implicated in the transport of nutrients across the visceral endoderm from maternal to embryonic tissues (Dragatsis et al. 1998). Homozygous deletion of Htt in mice results in embryonic lethality, and hemizygous null mice expressing 50% of WT Htt levels develop a mild phenotype as adult-onset neurodegeneration in the basal ganglia (Nasir et al. 1995; O¶Kusky et al. 1999). Htt plays an anti-apoptotic role in both in vitro culture systems (Rigamonti et al. 2000; Ho et al. 2001; Rigamonti et al. 2001) and in vivo HD mice models (Dragatsis et al. 2000; Leavitt et al. 2001; Van Raamsdonk et al. 2005; Leavitt et al. 2006). Htt expression can block the formation of functional apoptosome complexes and consequent activation of caspases (Rigamonti et al. 2000; Rigamonti et al. 2001; Zhang et al. 2006). Furthermore, it appears that WT Htt is required for generation and expansion of   5 hematopoietic cells from mouse embryonic stem cells (Metzler et al. 2000), but interestingly is not required for ES cells to differentiate into mature post-mitotic neurons, to develop functional ion channels, or to establish functional synapses (Metzler et al. 1999). The N-terminal region of Htt is the most active region of the protein, comprised of a coiled alpha helical domain permitting interactions with mitochondria, Golgi, and ER (Atwal etal. 2007; Kim et al. 2009). Following this domain is the polyQ region (where expansion can occur) that can adopt multiple conformations depending on the local protein environment and post-translational modifications (PTM) (Harjes and Wanker 2003; Kim et al. 2009; Davranche et al. 2011). There are many PTM sites on Htt associated with activation of prosurvival pathways (Rangone et al. 2004; Luo et al. 2005; Warby et al. 2005; Schilling et al. 2006; Thompson et al. 2009). There are also many PTM sites at which proteolytic enzymes cleave Htt into multiple different fragment lengths, including caspases (Wellington et al. 2002; Hermel et al. 2004), calpains (Kim et al. 2001), and aspartyl proteases (Lunkes et al. 2002). Htt is also targeted to subcellular compartments by its nuclear export signal at the C-terminal end, and a less active nuclear localization signal (Xia et al. 2003). Htt is involved in assembly of vesicle trafficking components through sumoylation (Steffan et al. 2004), and can also be targeted for degradation when ubiquitinated and palmitoylated (Kalchman et al. 1996; Yanai et al. 2006). Flanking the polyQ region is a polyproline helix that has only evolved in higher vertebrates and is believed to enhance stability and solubility of the long polyQ tracts (Tartari et al. 2008). These regions associate to create a flexible domain essential for proper intramolecular proximity, conformations, and dynamics with various functional partners (Caron et al. 2013). A remarkable number of Htt-associated proteins are involved in apoptosis, cell signaling, morphogenesis, transcriptional regulation and clathrin-mediated endocytosis and exocytosis of vesicles at neurites and synapses (DiFiglia et al. 1995; Li et al. 2003). Htt is found in close association with vesicles and microtubules and tends to co-purify with membranes (Velier et al. 1998). Htt also promotes neurotrophic support through inducing brain-derived neurotrophic factor (BDNF) expression and vesicular transport along microtubules (Gauthier et al. 2004; Shimojo 2008). Glyceraldehyde 3-phosphate   6 dehydrogenase (GAPDH) is localized on vesicles in a Htt-dependent manner for fast-moving transport on axons (Zala et al. 2013). 1.4 Proposed Mechanisms of Mutant Huntingtin Toxicity PolyQ repeat expansion has been implicated in the pathogenesis of at least nine neurodegenerative diseases and the mechanisms of toxicity are not fully understood. Transgenic mice with long inserts of CAG expansions into the mouse Hdh homolog reveal that mHtt does not affect embryonic development, but instead initiates a time-dependent disease process selectively affecting striatal neurons resulting in neuronal dysfunction due to a toxic gain of function (MacDonald and Gusella 1996; Auerbach et al. 2001). The expanded mHtt polyQ tract results in a dysfunctional conformational flexibility of the N-terminal region of the protein, disrupting important protein-protein interactions (Caron et al. 2013; Metzler et al. 2001; Sun et al. 2001; Goehler et al. 2004; Giorgini and Muchowski 2005; Zuccato et al. 2010).  Gain of toxic functions for mHtt include transcriptional dysregulation (Moumné et al. 2013), altered calcium homeostasis and metabolism (Bezprozvanny 2009), activation of proteolytic enzymes (Lunkes et al. 2002; Wellington et al. 2002; Gafni et al. 2004; Hermel et al. 2004) and microglial and immune system activation (Pavese et al. 2006). Aggregates of mHtt (intracellular inclusions) and soluble mHtt species can be found throughout the brain (Davies et al. 1997). The presence of soluble mHtt leads to an increased production of reactive oxygen species (ROS) (Wyttenbach et al. 2002). The relationship between mHtt aggregates and HD pathology is not well understood, but the process of inclusion body formation may be a broad coping response to the presence of mHtt. However, inclusion bodies have also been reported to be centers of iron-dependent oxidative stress (Firdaus et al. 2006; Hands et al. 2011).  The binding of mHtt to calcium channels on the ER and mitochondria lead to sensitization of receptors and increased calcium storage release (Tang et al. 2003; Tang et al. 2005; Zeron et al. 2002; Choo et al. 2004). Mitochondrial dysfunction is an important contributor to striatal-specific degeneration that can be modeled by selective environmental   7 toxins or genetic manipulation of mitochondria (Browne et al. 1997). Mitochondrial DNA is vulnerable to oxidative stress due to lack of histones, high density of genetic information, limited repair mechanisms, and abundant free radicals created in the inner mitochondrial membrane. Supportive evidence for mitochondrial dysfunction in HD comes from established mitochondrial neurotoxins in HD rodent models that target electron chain complexes, resulting in neuropathology similar to that seen in human HD (Brouillet et al. 1999). Within the mitochondria, other protein levels increase pathologically, causing excessive fragmentation and abnormal distribution of mitochondria within the cell(Reddy and Shirendeb 2012). The loss of neurotrophic support coincides with loss of connectivity between the cortex and striatum, affecting the anterograde transport of BDNF from the cortex to MSNs (Cepeda et al. 2007). This is observed as BDNF expression is decreased in the cortex and its receptor (TrkB) is decreased in the striatum (Zuccato et al. 2008). Conversely, increased expression of striatal BDNF is protective. Grafting BDNF expressing cell lines into the striatum prevents cell loss induced by excitotoxins (Canals et al. 2001). However, it is still unclear why MSNs in the striatum are selectively targeted by mHtt.   One hypothesis is that excess glutamate released from cortical afferents leads to excitotoxicity of MSNs, leading to cell death (Albin et al. 1990). This is considered an early mechanism of compensation, as it is observed in both pre- and early-HD patients (Feigin et al. 2006). Other established neurotoxin models, in which antagonists selectively target MSNs and NMDARs resulting in HD-like neuropathology, support this hypothesis (Beal et al. 1991; Ferrante et al. 1993; Schwarcz et al. 1977). MHtt is known to pathologically bind to PSD95 at the synapses of NMDARs, promoting altered activation and impairment of clathrin-mediated endocytic complex trafficking (Sun et al. 2001; Song et al. 2003; Metzler et al. 2007). A recent addition to the hypotheses of selective MSN targeting revolves around two brain-enriched small GTPases belonging to the Ras superfamily. In 2006, dexamethasone-induced Ras-related protein 1 (Dexras1) was discovered to mediate neuronal iron homeostasis via NMDAR-NO transmission (Cheah et al. 2006). The NMDAR activation leads to increased calcium levels that activate calcium-sensitive neuronal nitric oxide   8 synthase (nNOS), producing excess NO that signals through the S-nitrosylation of cysteines. It is known nNOS binds to PSD-95, which in turn binds to NMDARs, enabling the release of NO to S-nitrosylate NMDARs thereby altering their signaling (Brenman and Bredt 1996). More recently, it was shown NO S-nitrosylation activates Dexras1, which itself is linked to nNOS via carrier protein carboxyl-terminal PDZ ligand of neuronal nitric oxide synthase protein (CAPON) (Cheah et al. 2006). This ternary complex of nNOS-CAPON-Dexras1 was found to interact with the Golgi resident protein GCP60, also known as Acyl-coenzyme A binding domain containing 3 protein (ACBD3, previously known as PAP7). ACBD3 then binds to the divalent metal transporter (DMT1) and physiologically induces iron uptake into the cell (Cheah et al. 2006). This was found to occur both at the membrane  and  endosomal level, equally affecting regulated transferrin receptor (TfR)-mediated iron uptake as well as non-transferrin bound iron uptake (Cheah et al. 2006). More recently, it was discovered Dexras1 is also required for NMDA-elicited neuronal toxicity via NO and iron influx (Chen et al. 2013).  Dexras1 shares closest homology (67%) to the ras homolog enriched in striatum (Rhes) protein (Vargiu et al. 2004). The function of Rhes is not fully understood, but is known to be involved in phosphatidylinositide 3-kinases (PI3K) activation (Vargiu et al. 2004), regulatory actions on AKT (Protein Kinase B) pathway (Harrison et al. 2013), and modulation of dopamine receptor-mediated behavior (Quintero et al. 2008). Rhes is selectively localized to the striatum, with detectable levels in the cortex and negligible levels in the cerebellum - expression that coincides with HD pathology (Falk et al. 1999). Rhes has been found to directly interact with mHtt, resulting in sumoylation of mHtt ²attachment of a small ubiquitin-like modifier (SUMO) that decreases the ability of mHtt to aggregate, thus increasing presence of the soluble toxic form (Subramaniam et al. 2009). This increases the potential of mHtt to travel to the nucleus to repress nuclear transcription or pathologically bind to other proteins (Steffan et al. 2004). Rhes also tends to bind mHtt with much higher affinity than WT Htt, which may be sequestering Rhes from performing other vital functions, such as activation of the classical autophagy inhibitor, mammalian target of rapamycin (mTOR) (Subramaniam et al. 2009).    9 Okamoto et al. discovered in 2009 that NMDAR synaptic activity influences inclusion formation and neuronal survival. Synaptic activity controls expression of chaperonin TCP1 ring complex (TRiC), which in turn modulates inclusion formation, rendering neurons more resistant to mHtt-mediated cell death (Okamoto et al. 2009). On the contrary, extrasynaptic activity of NMDARs tends to increase levels of Rhes, which increasingly binds to mHtt, and thus which interferes with the neuroprotective cyclic AMP response element±binding protein (CREB)±peroxisome proliferator±activated receptor-Ȗ coactivator-1Į (PGC-1Į) activity, thereby decreasing regulation of genes involved in energy metabolism (Okamoto et al. 2009). This study highlights the importance of balancing synaptic and extrasynaptic NMDAR activity. Using a low-dose extrasynaptic NMDAR antagonist, memantine, in HD mice models leads to restoration of excitatory balance, but when using high doses of memantine, there is decreased inclusion formation and increase soluble mHtt levels (Okamoto et al. 2009). Thus, it may be that mtHtt increases the vulnerability of neurons to relatively low concentrations of exogenous glutamate and therefore balancing healthy synaptic activity will be important to avoid cell death (Okamoto et al. 2009). Additionally, the active form of Rhes was found to interact as a scaffolding protein between Dexras1, DMT1, and ACBD3, leading to increased iron uptake (Choi et al. 2013). The only enzyme appearing to tentatively regulate Rhes activity was found to be phosphorylated-PKA, which activates the Rhes-GTPase to regulate intracellular iron influx (Choi et al. 2013). Choi et al. also suggests that when striatal-specific Rhes activity is perturbed, dysregulation of dopaminergic function in the striatum may result, since PKA is activated by dopaminergic activity, known to regulate learning and memory (Heyser et al. 2000), addiction (Hiroi et al. 1999), and synaptic plasticity (Lee et al. 2000). More recently, it was also found that Rhes can form a ternary complex with mHtt and ACBD3, whereby ACBD3 appears to be a major determinant of neurotoxicity, as its overexpression is cytotoxic and its deletion abolishes Rhes neurotoxicity (Sbodio et al. 2013). ACBD3 protein levels are markedly increased in brains of patients with HD, in neuronal cell lines with mutant polyQ repeats, as well as in brains of transgenic HD models (Sbodio et al. 2013). ACBD3 is predominantly a Golgi protein, and Rhes and mHtt also tend to localize in the Golgi. Diverse cellular stresses, including mitochondrial or ER stress, lead to the upregulation of  ACBD3   10 levels, which are upregulated in neuronal cells overexpressing mHtt (Sbodio et al. 2013). The importance of ACBD3 is evident by the fact that HD neurotoxicity is abolished or increased when ACBD3 is deleted or over-expressed, respectively.(Sbodio et al. 2013). A meta-analysis of striatal gene expression found that Rhes is consistently down-regulated in HD and replacing Rhes expression augments toxicity (Seredenina et al. 2011). Furthermore, it has been shown deletion of Rhes in the R6/1 HD mouse model (Baiamonte et al. 2013) and a 3-nitropropionic acid (3-NP) neurotoxin HD mouse model (Mealer et al. 2013) delays onset of symptoms and reduces striatal degeneration and motor dysfunction, respectively.  The ubiquitin-proteasome system (UPS) and autophagy play active roles to lower toxic diffuse mHtt levels (Arrasate and Finkbeiner 2012), but in HD these systems become altered and even degraded by increased macroautophagy (Ravikumar et al. 2004; Bennett et al. 2007; Mitra et al. 2009). In HD, there is diminished loading of autophagic vesicles, increased autophagosome levels, and polyQ-dependent changes in neuronal autophagy (Nagata et al. 2004; Martinez-Vicente et al. 2010; Zheng et al. 2010). Ravikumar et al. have demonstrated that mHtt sequesters mTOR, leading to decreased mTOR kinase activity and enhanced autophagy. More recently, Rhes protein has also been discovered be involved in influencing autophagy (Mealer et al. 2014). Rhes robustly binds the autophagy regulator Beclin-1, decreasing its inhibitory interaction with Bcl-2 (Mealer et al. 2014). Also, co-expression with mHtt blocks Rhes-induced autophagy activation, and Rhes/mHtt interactions may augment cytotoxicity, diminish the autophagic capacity of the neuron, accounting for the striatal selectivity and delayed onset of HD (Mealer et al. 2014). Mice lacking Rhes have been shown to exhibit decreased L-DOPA-induced dyskinesia, demonstrating that Rhes physiologically activates mTOR in the striatum (Subramaniam et al. 2009). The delay of HD symptoms in Rhes knock-outs may be related to changes in the autophagic activity of Rhes due to its ability to activate mTOR (Mealer et al. 2014). Autophagy, a lysosomal degradation pathway implicated in ageing and HD, becomes a prominent pathway with advancing age (Li and Li 2011).  There is significant evidence for increased oxidative stress in HD.  Build-up of lysosomal digestion products, known as lipofuscin, is typically associated with increased oxidative stress and is apparent in HD (Túnez et al. 2011). The presence of lipofuscin may   11 also be due to alterations of the degradation pathways. Proteins involved in cellular energy metabolism, protein folding, anti-oxidant response, and vitamin B6 metabolism undergo protein oxidation by carbonyl formation (Sorolla et al. 2008; Sorolla et al. 2010; Sorolla et al. 2012). The present ROS can act as second messengers to NADPH oxidase activity involved in microglial activation (Qin et al. 2013). The abundance of NO in the cell also increases the potential to react with ROS and create neurotoxic peroxynitrite radicals (ONOO-) (Calabrese et al. 2009). Lipid peroxidation in CSF has been reported to be increased in HD (Montine et al. 1999; Reddy and Shirendeb 2012), including increased nitrate and nitrite products, decreased antioxidant copper/zinc SOD, and decreased ceruloplasmin ferroxidase activity (Boll et al. 2008). Overall, mHtt appears to have multiple toxic gain-of-function properties that could contribute to HD pathogenesis (Zuccato et al. 2010). Some pathological features may also be due to functionally decreased WT Htt levels. Most other tissues have low expression of Htt; thus it is plausible that mHtt expression levels are associated with the degree of pathology in the affected tissues (Cisbani and Cicchetti 2012). Lowering Htt levels in transgenic HD control and mutant mice during embryonic development results in defects in neural tube development, in the epiblast, and profound cortical and striatal structural abnormalities (Auerbach et al. 2001).  On the contrary, increasing WT Htt levels has been shown to protect striatal neurons from NMDAR-mediated excitotoxicity as well as ameliorate striatal neuronal atrophy in mice models, though not efficiently enough to be used as a therapeutic approach (Leavitt et al. 2006; Van Raamsdonk et al. 2006). Interestingly, levels of WT Htt seem to decrease with advancing disease progression in mouse models of HD and other neurologic diseases, again highlighting the importance of Htt expression levels (Zhang et al. 2003). 1.5 Mouse Models of HD Prior to transgenic animal models, neurotoxin-based models existed that exhibited similar neurodegenerative hallmarks to that seen in HD. One of the first was a model of excitotoxicity induced by a chemical called kainic acid (KA), derived from Japanese seaweed Dignea simplex (Japanese translation: µJKRVW >RU PRQVWHU@ IURP WKH VHD¶ KA is related   12 biochemically to glutamate, a precursor of GABA, and was found to selectively target the striatum, specifically the MSNs, in adult rats and mice (Schwarcz et al. 1977). Upon binding to the MSNs, there is resulting overstimulation of neurons, DNDµH[FLWRWR[LFLW\¶DQGUHVXOWLQJneuropathology presents similar to that in HD brains. Althought this result heightened interest in this area of research, this model did not exhibit chorea, one of the main hallmarks of HD. Shortly thereafter was discovered another excitotoxin, which exists endogenously in the brain as quinolinic acid (QA), that offered a closer analogy to HD pathogenesis. It is also chemically similar to glutamate, and when injected into brains of rats or mice, directly targets and binds to specific NMDARs leading to excitotoxicity, resulting in a more precise replication of HD neuropathogenesis and behaviorial patterns (Beal et al. 1991; Ferrante et al. 1993).  Another neurotoxin model that is sometimes used is the 3-NP, a mitochondrial respiratory chain complex II (CII) inhibitor, leading to mitochondrial dysfunction, excitotoxicity, oxidative stress, and altered iron and copper homeostasis (Dexter et al. 1991; Fox et al. 2007; Simmons et al. 2007). This model provides insight into specific mitochondrial mechanisms gone awry in HD, and this complex appears to be down-regulated with disease stage in the striatum of HD post-mortem tissue. This chemical model exhibits similar striatal degeneration to HD (Brouillet et al. 1999) and has been shown to be rescued by NMDAR antagonists (Lee et al. 2006). However, neurotoxin-based models do not provide sufficient insight into exact disease mechanisms and so, following the discovery of the HD gene in 1993, transgenic animal models have been developed to recapitulate the disease. These animal models provide useful insight into the natural history and pathogenic mechanisms of the disease and are useful for testing novel therapies.  Mouse models are most commonly utilized as a disease model since they are an economical and efficient option. As Htt appears to be a very interesting protein, there have been various knock-out models which have helped to elucidate some of the functions of WT Htt. Various HD mouse models exist that differ in CAG repeat length, transgene expression levels, or background strain. The three main categories of HD mouse models are the knock-in models, the fragment transgenic mice, and the full-length transgenic mice.   13 Knock-in HD models are generated by homologous recombination techniques using mouse embryonic stem cells, containing a specified number of CAG repeats. These mice are heterozygous for a WT Hdh allele and a CAG-expanded Hdh allele. Many knock-in HD mice have been created, please see Menalled et al. (2005) for a review.  Fragment transgenic mice models, R6/1 and R6/2, were the first HD mouse models generated (Mangiarini et al. 1996). They contain the N-terminal (exon 1) of the human HTT gene with the addition of CAG repeats (Menalled et al. 2009a). These models have proven that the presence this N-terminal fragment is capable of producing HD-like symptoms. The most extensively studied and utilized model is R6/2, which expresses roughly ~150 CAG repeats; while the R6/1 model which contains ~110 CAG repeats (Mangiarini et al. 1996).  These models offer the advantage of manifesting disease rapidly and aggressively, with a life span of 15 weeks and behavioral phenotypes at 5-6 weeks.  The R6/1 has lower mHtt expression than R6/2, but displays similar phenotypic alterations although it tends to survive slightly longer (Mangiarini et al. 1996). Knock-in models are engineered via pronuclear injection, resulting in random transgene insertion, although R6/2 has been found to function as a single copy integrant (Chiang et al. 2012). However this is important to consider since it can interrupt large segments of native mouse chromosomal DNA.  Another model is the N171-82Q, containing the first 171 amino acids of Hdh protein and expression controlled by a mouse prion promoter to direct expression mainly in the brain. There are also other models which have localized tissue expression or varied numbers of CAG tracts. The N-terminal fragment mice are also prone to undergoing generational drift, the instability of the length of the CAG tract to expand in both germ line and somatic cells, thus it is always important to monitor the length of CAG tract for experimental purposes and breeding selection.  Many knock-in mouse models exist that express mHtt in its native mouse genomic context, with CAG repeats engineered that vary between 48-200 in different models (Heng et al. 2009).  There are two types of full-length transgenic models that can be generated, either using a bacterial artificial chromosome (BAC) or a yeast artificial chromosome (YAC) (Hodgson et al. 1999). These are large constructs containing full-length human HTT transgene and its endogenous regulatory elements, and tend to integrate at a single genomic locus and in low copy numbers. The YAC mice express WT (YAC18) and mutant (YAC46, YAC72, YAC128) forms of the human Htt gene, and display increasing alterations at earlier time   14 points with increased CAG repeats (Hodgson et al. 1999). The YAC128 mouse expresses Htt at roughly the same level of endogenous mouse Hdh (Slow et al. 2003). BAC HD mice express 97 CAG repeats and display late-onset neuropathology in striatum and cortex, but provided insight due to the symptoms of HD but the absence of nuclear accumulation of mHtt in pathogenesis (Gray et al. 2008). The BAC HD and YAC128 mice are comparable in in overall general health and motor-related behavioral phenotypes, although have critical differences in transcriptional regulation (Pouladi et al. 2012). The YAC128 model recapitulates the gradual HD disease progression, with behavioral deficits beginning at 3 months, motor deficits by 6 months, decreasing cognitive and motor function progressively with age, and significant and selective atrophy in the striatum and cortex (Slow et al. 2003; Van Raamsdonk et al. 2005).    15 CHAPTER 2 : INTRODUCTION TO IRON 2.1  The Role of Iron in Biological Systems Iron is an essential element that provides a wide range of reduction-oxidation (redox) potential, ranging from +1000mV to -550 mV depending on the ligand environment, which makes electron transfer possible for metabolic reactions necessary to sustain life (Pantopoulos et al. 2012). Proteins can utilize iron as a prosthetic iron-sulfur (Fe-S) cluster in heme or as a gas generation/reception mechanism (Calabrese et al. 2009). The primary functions of heme-containing proteins are two-fold: to store and transport O2 and NO, and to act as a catalytic site for various enzymes (Calabrese et al. 2009). There are 12 hemes and 7 Fe-S clusters in the mitochondrial respiratory chain (Pantopoulos et al. 2012). Although it is not entirely clear how iron crosses the BBB, potential routes include DMT1, and transferrin or lactoferrin receptors (TfR/LfR) that facilitate receptor-mediated endocytosis of the iron carrying proteins transferrin or lactoferrin. Transferrin is the main iron carrier, with two binding sites for ferric iron (Fe3+), and has an overall ~30% saturation at equilibrium (Gkouvatsos et al. 2012). Lactoferrin is a less understood iron transporter that is expressed on the outer membrane of blood vessels, is endogenously produced in the brain, and has multiple immunological functions (Leveugle et al. 1993). Hypoxia inducible factor-Į+,)-Į) is an iron- and oxygen-sensitive transcription factor that controls DMT1 levels, and is found to be highly expressed on brain endothelial cells and vasculature (Rouault 2001).  When TfR is endocytosed, iron is released by reducing to ferrous iron (Fe2+) in the acidic vesicle, and transported across the membrane by DMT1 or directly transferred to organelles by direct endosomal contact (a process NQRZQ DV µkiss-and-UXQ¶ /DPEH et al. 2009; Richardson et al. 2010). The highly reactive Fe2+ can become part of a small labile iron pool (LIP) comprised of free forms of iron at negligible concentrations in the cytosol (Friedman et al. 2011). Molecular chaperones carry iron to specific target proteins such as the mitochondrial iron transporter protein mitoferrin (Chen et al. 2009; Philpott 2012). Poly-binding proteins (PCBP1 and 2) are also known to target iron to ferritins (Shi et al. 2008).   16 The iron-storage protein ferritin can store up to 4500 iron atoms in a hollow sphere made up of 24 heavy (H) and light (L) chain subunits, that vary according to tissue distribution (Arosio et al. 2009). The H-chains confer antioxidant ferroxidase activity and are prevalent in tissues such as heart and brain; the L chains contribute to structural stability and the transfer of iron to the ferritin core, pronounced in tissues such as liver and spleen (Levi et al. 1993; Harrison and Arosio 1996). There also exists an intronless mitochondrial ferritin gene that is restricted to a few tissues with high metabolic activity (including testis), and contains ferroxidase activity similar to H-ferritin, but is not iron-regulated (Aisen et al. 2001; Levi et al. 2001; brogio et al. 2007; Friedman et al. 2011). Ferritin and TfR mRNA transcripts contain iron-response elements (IREs) and thus iron regulatory proteins (IRP1/IRP2) control the expression of iron transport and storage in the cell (Pantopoulos 2004). IRPs are cytoplasmic RNA-binding proteins that respond to cellular iron levels and post-transcriptionally bind to mRNA stem loop structures depending on intracellular iron concentrations. Iron homeostasis is sensitive to inflammatory signals, as shown in microglia and astrocyte primary cultures, in which the iron pathways are differentially regulated by TNF-ĮDQG7*)-ȕ5DWKRUHet al. 2012). These two cytokines are expressed during CNS inflammation and disease and can have profound and divergent effects on iron homeostasis in different cell types. Inflammation also induces cytokines that affect levels of hepcidin, the master regulator of iron in the body produced by liver (Krause et al. 2000; Park et al. 2001; Nemeth et al. 2004; De Domenico et al. 2010; Armitage et al. 2011). During development, brain iron and ferritin are initially present in microglia, but when myelination is initiated, iron, ferritin, and transferrin are found in oligodendrocytes (Connor and Menzies 1996). There are many different ferroxidase proteins found in the brain that catalyze Fe2+ to Fe3+ in order to be taken up by certain proteins, as well as ferrireductase proteins for the reverse reaction (Schulz et al. 2011). Ferroportin, a ferroxidase, is an important iron channel associated with iron export (Wang and Pantopoulos 2011). Hepcidin directly binds ferroportin to promote internalization and lysosomal degradation, therefore reducing iron uptake into the cell (Ross et al. 2012). Hepcidin expression can increase in the presence of pathological inflammation but, when prolonged inflammation occurs, can be associated with anemia and chronic disease (Weiss 2009). Hepcidin is regulated at the   17 transcriptional level, induced by iron, inflammation or ER stress (Ganz and Nemeth 2011). Overall, the movement of iron into and around the brain is complex and multi-faceted. 2.2 *HWWLQJ³5XVW\´ZLWK$JH One of the key functions of ferritin is its antioxidant activity, which keeps ROS to a minimum by sequestering toxic reagents. Mitochondria contain their own ferritin, not regulated by iron levels, in order to maintain a constant source of iron sequestration. Otherwise, available Fe2+ can react with oxygen (O2) being produced by the mitochondria to form superoxides (O2*). Enzymes such as catalase and glutathione exist to keep toxic products to a minimum by converting it to less toxic and endogenously abundant hydrogen peroxide (H2O2). However, Fe2+ can be oxidized by endogenous H2O2, resulting in production of two different oxygen-radical species (Zhao et al. 2006) as follows: Fenton reaction:  (1) Fe2+ + H2O2 +H+ĺ)H3+ +2‡+2O (2) Fe3+ + H2O2ĺ)H2+ +22‡++ It is vital that the cell has enough properly functioning proteins, such as antioxidant enzymes,  to keep iron levels under control, to avoid catalysis of toxic reactions. The mitochondria contain many iron-containing proteins that provide a strong catalytic potential to enhance oxidative stress and ROS production, and therefore the mitochondrial-specific ferritin exists to provide extra antioxidant activity. Iron has been shown to naturally accumulate in the brain with age, with the highest concentrations located in the GP, although it is suggested that putaminal iron is the best predictor of physiological aging in a healthy population (Cherubini et al. 2009; Pfefferbaum et al. 2009). Iron accumulation occurs in all aging mammals, in areas primarily associated with motor activity, including the GP, red nucleus, dentate nucleus, and SN (Koeppen 1995; Gelman et al. 1999). In humans these brain areas tend to accumulate iron starting in the third decade of life following peak myelination when they become rich with ferritin (Zecca et al. 2004). The increase in brain iron levels with age is not always associated with pathology, although it has been linked to cognitive and motor dysfunction in the elderly (Pujol et al. 1992).    18 2.3 Altered Iron Levels in Brain Diseases Metals have well defined physiological functions within cells, but abnormal metal accumulation can cause neuronal dysfunction (Browne et al. 1997; Bossy-Wetzel et al. 2004; Coppedè et al. 2006). Neurons are highly susceptible to free radical-mediated injury due to their requirement for high levels of oxidative metabolism and because neuronal membranes are rich in polyunsaturated fatty acids (Halliwell 1992). Activated microglial cells are also less efficient in maintaining iron homeostasis, so that iron-induced oxidative damage may be amplified by multiple factors (Shoham and Youdim 2000). Many neurodegenerative disorders that present with cognitive and movement dysfunction are associated with increased iron accumulation in the brain and are referred to as neurodegeneration with brain iron accumulation (NBIA) disorders (Rouault 2001). These disorders share a common phenotype of iron accumulation in basal ganglia structures associated with altered motor activity and have identified causative genetic mutations. Two of these nine different NBIA genetic mutations involve iron metabolism, and the others involve fatty acid metabolism or lysosomal activity (Rouault 2013).  Neuroferritinopathy is a NBIA disorder that is an HD phenocopy, caused by autosomal dominant mutations in the ferritin light chain gene, FTL (Friedman et al. 2011).  Ferritin tends to aggregate in tissues with decreased ability to retain iron, and decreased serum ferritin is frequently reported. There are hypointense T2 signals in the basal ganglia, and excess iron deposition becomes evident in the putamen, GP, and dentate nucleus over time (Chinnery et al. 2007).  A loss-of-function mutation in the copper-containing ferroxidase protein ceruloplasmin is responsible for the NBIA autosomal recessive disorder aceruloplasminaemia (Kono 2012). Ceruloplasmin, synthesized by astrocytes in the CNS, is known for copper transport and antioxidant functions in the prevention and the formation of free radicals in serum. It is linked to the outer leaflet of the plasma membranes to facilitate the oxidation of iron as it is exported via ferroportin (Jeong and David 2003). The loss of ceruloplasmin results in reduced cellular export of iron, and MRI shows accumulation of iron mainly in the retina and basal ganglia, but also to a lesser degree in the dentate nucleus and cerebral cortex. Iron is reportedly located in astrocytes in affected brain regions, coinciding   19 with many signs of oxidative stress and protein carbonylation (Jeong and David 2003). Ceruloplasmin activity has also found to be ~80% reduced in the SN of idiopathic PD cases and reduced in the CSF of HD patients (Hare et al. 2013) . Pantothenate kinase-associated neurodegeneration (PKAN) is an NBIA caused by a mutation in the PANK2 gene responsible for biosynthesis of coenzyme A and formation of pantothenate (vitamin B5) in the inner mitochondrial membrane (Kruer et al. 2012). PKAN results in dystonia, dementia, dysphagia, spasticity, rigidity, and tremor with childhood-onset. Iron deposition detectable by MRI is believed to precede development of clinical symptoms, and is seen in the GP in <90% of patients (Hayflick et al. 2001). Fatty acid hydroxylase-associated neurodegeneration (FAHN) is caused by mutations in the fatty acid-2 hydroxylase (FA2H) gene resulting in similar motor impairment to that seen in HD, with the addition of seizures (Gregory and Hayflick 2011). Iron is present in the GP and to a lesser degree the SN detected by MRI. Other NBIA disorders include phospholipase A2, group VI (PLA2G6)-associated neurodegeneration (PLAN), mitochondrial membrane-associated neurodegeneration (MPAN) and several others (Rouault 2013). )ULHGULFK¶VDWD[LDLVDGLVHDVHQRWusually considered to be an NBIA, but is caused by a mutation in the Frataxin gene which is responsible for assembling Fe-S clusters (Philpott 2012). There is a bioenergetic defect similar to that seen in HD skeletal muscle, and both disorders correlate triplet repeat expansion with energy metabolism deficits (Schapira and Lodi 2004). Iron chelators have been VKRZQWRDPHOLRUDWHV\PSWRPVLQ)ULHGULFK¶VDWD[LDDQGother NBIA disease models (Pandolfo and Hausmann 2013), providing further support that iron accumulation is a pathogenic trigger.  The iron chelator deferiprone has been reported to protect against iron overload-induced retinal oxidative stress and degeneration in the aceruloplasminemia mouse model. Treatment prevented lipofuscin accumulation, ataxia, and increased the lifespan of the mice (Hadziahmetovic et al. 2011). Another iron chelator, desferrioxamine, has been used in patients to treat aceruloplasminemia and resulted in decreased brain iron stores, prevention of neurological symptoms, and reduced plasma lipid peroxidation (Miyajima et al. 1997). This chelator has also been utilized in AD patients via intramuscular injections, where it showed a reduction in the overall rate of decline and thus promise as a potential therapeutic treatment (Crapper McLachlan et al. 1991).   20 In general, iron chelators contribute to reducing oxidative stress. Tau knock-out mice develop age-dependent atrophy, iron accumulation, and parkinsonism pathology that can be prevented in mice using oral treatment with a moderate iron chelator, clioquinol (Lei et al. 2012). Young transgenic mice with ceruloplasmin knock-out develop parkinsonism and can be rescued by a 3-month iron chelation treatment (Hare et al. 2013). More convincing evidence for the detrimental effects of elevated iron has been shown in an acute animal model of PD showing brain damage that can be diminished by reducing iron levels (Kaur et al. 2003). A SOD1 mutant mouse model of ALS displays iron accumulation in the CNS, dysregulated IRPs and iron levels, and treatment with an iron specific chelator resulted in increased mean lifespan. The iron chelators desferal and VK-28 have proven to be neuroprotective against neurotoxin models of PD (Youdim et al. 2004). In the KA model of epilepsy, iron deficiency lead to attenuated microgliosis, whereas iron supplementation increased damage, demonstrating a tight relationship between iron accumulation and microgliosis (Shoham and Youdim 2000). 2.4  Detection of Iron Levels in the Brain  Established methods are available to visualize cellular iron in post-mortem brain, such as the histological stains for ferric and ferrous iron, known as Perls and Turnbulls staining, respectively. Other analytical methods exist to precisely quantify iron biochemically such as atomic absorption spectroscopy (AAS) or inductively coupled plasma-mass spectrometry (ICP-MS). Importantly, it has been found that MRI signals present in brain images correlate closely with histologically established patterns of brain iron deposition.  MRI is a powerful tool to assess magnetic field inhomogeneities (MFIs) in the brain, which provide an MR signal based on mobile protons. A nuclear MR signal of water may be strongly influenced by variations in magnetic susceptibility due to presence of MFIs, which are 1-100 µm microstructures produced by capillaries, iron rich cells, or the presence of an extracellular paramagnetic contrast agent (Jensen et al. 2006). Paramagnetic substances that can impact MR signal include ions of the transition group (iron, manganese, and copper), and of the rare earth group (gadolinium and dysprosium) (Schenck 2003). MRI methods used to assess brain iron in HD studies are briefly reviewed here.   21 Iron can be detected as non-heme iron in mineralized ferrihydrite form when it is stored in ferritin or hemosiderin; and lower molecular forms of iron are not readily detected by MRI (Schenck 2003). Image contrast can be weighted to demonstrate different structures or pathologies, using magnetic pulses to excite tissues and observe the return to equilibrium state either by T1 (spin-lattice) or T2 (spin-spin) relaxation. Longitudinal (T1) relaxation time and transverse (T2) relaxation time contrast images are based on variations among brain regions in their density of solvent water, with the presence of iron resulting in hyperintense T1 shortening on T1-weighted images, and hypointense T2 shortening on T2-weighted images (Schenck 2003). Iron content has been widely imaged in the aging brain using T1 and T2 relaxation rates, a verified technique that correlates linearly with iron concentration (Langkammer et al. 2010). The T2 hypointensity associated with iron deposition has been observed in various neurodegenerative diseases, including Alzheimer¶V disease (AD) (Bartzokis and Tishler 2000), Parkinson¶V disease (PD) (Michaeli et al. 2007), and multiple sclerosis (MS) (Drayer et al. 1987; Grimaud et al. 1995; Bakshi et al. 2000; Nishii et al. 2000; Bakshi et al. 2001; Bermel et al. 2005). MRI signals attributed to aging-related calcification in the brain may also generate T2/T2* hypointensities, but can be differentiated because calcifications similarly impact T1 (longitudinal) relaxation times whereas iron deposits do not (Valdes Hernandez et al. 2011). Relaxation describes several processes by which nuclear magnetization prepared in non-equilibrium state returns to the equilibrium distribution, including T1, T2, and T2* relaxation rates. The T2* relaxation rate is usually much shorter than T2, since T2* is influenced by MFIs and takes into account the difference in strength of the locally varying field (Jensen et al. 2006). Low-field relaxation rates (R2) are much less sensitive to ferritin iron and provide a good measure for relative increase in tissue hydration with decreasing R2, while R2 tends to increase in the presence of iron (Bartzokis et al. 2007).  Field-dependent relaxivity increase (FDRI) is a technique that uses two different MRI field strengths to differentiate R2 in the same tissue area (Bartzokis et al. 1993). The R2 linearly with increasing magnetic field strength when ferritin or hemosiderin are present, obtaining highly specific and reproducible measures of iron (Bartzokis et al. 1993). Increasing R2 values with higher field strength is directly proportional to ferritin   22 concentration (Stankiewicz et al. 2007). FDRI can be used in conjunction with susceptibility-weighted imaging (SWI), a method which exploits the susceptibility differences between tissues and uses the phase image and magnitude to detect these differences, and is very sensitive to iron. SWI uses a type of contrast different from T1 and T2, with a fully flow compensated, long echo, gradient recalled echo pulse sequence to acquire images, and can be used for calculation of the FDRI (Pfefferbaum et al. 2009).  Another technique developed by Jensen et al. that provides a quantitative measure of MFIs is magnetic field correlation (MFC), which can be estimated with MRI using an asymmetric spin echo sequence (Jensen et al. 2006). It is independent of relaxation mechanisms, such as dipolar interactions, unrelated to MFIs, and takes into account non-monoexponential decay effects (Jensen et al. 2006). Thus, there are many different methods with varied sensitivities to indirectly measure iron using MRI. Methods usually vary from study to study, making direct comparison of findings difficult. However, whether or not it is entirely iron being detected, there appears to be a correlation with disease progression and neurodegeneration.    23 CHAPTER 3 : ALTERED IRON LEVELS IN HD Iron overload is often suggested to be a major cause of oxidative stress in neurons and can be readily detected due to its redox activity and electron spin states. There are a number of reports in the literature suggesting that iron accumulation occurs in the specific brain areas of neurodegeneration in HD and that iron homeostasis may be altered in the disease. A number of important heme-based mitochondrial respiratory chain complexes and Fe-S enzymes are reported to have altered expression in HD striatum (Bowling and Beal 1995; Roze et al. 2008). The existing literature on iron alterations in HD will be reviewed in the following sections.  3.1 Clinical Evidence  In 2010, there was a case study of a woman with HD that had a rare, unexpected early onset presented as gradual evolution of involuntary movements/chorea, confusion/cognitive deficits, and changes in behavior (Walker 2010). She was genetically tested and diagnosed with HD at age 50 but had a low CAG length of 36. Coincidentally, she was found to have elevated liver function tests and while participating in HD trials was shown to have markedly elevated serum transaminases, which lead to the finding that she also carried the homozygous mutation (C282Y) for hereditary hemochromatosis (HH) at age 55.  This is a disease that results in accumulation of body iron stores, and suggests clinically that elevated iron levels may play an important role in accelerating neurodegeneration in HD. One other case of unclear significance was reported in 2004 when a patient homozygous for the mutation (C282Y) for familial hemochromatosis presented with segmental dystonia, chorea, imbalance, and carried a diagnosis of HH-related movement disorder since age 56. From then on, she managed her HH successfully. By age 59, she was referred to a neurologist due to progressive balance problems, and years later with worsened symptoms (head tremor, dystonic posture/limb chorea, dystonic speech, and depression) at age 63 it was found she also had the HD mutation. The repeat length was not stated, just that is was beyond 38   24 repeats, and her family history was difficult to obtain with no known previous HD relatives (Russo et al. 2004). 3.2 Neuroimaging Evidence  There are numerous imaging studies examining iron levels in the brain and the potential changes that occur in premanifest HD and symptomatic HD patients. The overall conclusion suggests increased iron signal in the basal ganglia of HD patients, with further reports of decreased iron in white matter and cortex. The role of iron imaging in HD has been recently reviewed by van den Bogaard et al. 2013 (van den Bogaard et al. 2013). This review reports that both overall in vivo and ex vivo findings support the hypothesis that excess iron is present in the brains of HD patients, although there is no evidence for early increases in brain iron as a potential initiator of the pathological cascade. Table 3.2-1 summarizes the evidence from imaging studies for iron overload in HD brain. 25  Table 3.2-1: Magnetic resonance imaging studies quantifying iron levels in HD brain Research Study Sample     size Methods Findings in HD population Increased Basal Ganglia Iron Levels in Huntington Disease (Bartzokis et al. 1999) HD 27 Quantify brain ferritin iron:  - FDRI, 0.5T and 1.5T T MRI - ROI: caudate, putamen, GP and frontal white matter  ÏIron in all basal ganglia ROIs ÐIron in frontal white matter Ctrls 11 Myelin breakdown and iron changes in Huntington's disease: Pathogenesis and treatment implications (Bartzokis et al. 2007) HD 11 Measure structural integrity of myelin and iron content: - FDRI, 0.5 and 1.5 T MRI - ROI: splenium and genu of the corpus callosum (swm/gwm), frontal lobe, caudate, putamen, GP, hippocampus, thalamus ÏIron in basal ganglia regions ÐIron in frontal lobe and gwm ÐTissue integrity in caudate and putamen, swm and frontal WM Ctrls 26 MR relaxometry in Huntington's disease: Correlation between imaging, genetic and clinical parameters (Vymazal et al. 2007) HD 34 Correlate T2 with CAG repeat size and clinical status: - 1.5T MRI, T2 relaxometry - ROI: head of caudate, putamen, pallidum, frontal WM, and frontal cortex ÏT2 shortening in pallidum  ÐT2 shortening frontal WM Correlation: CAG size and T2 shortening in left pallidum / Inverse correlation for left caudate. Ctrls 24 MRI T2 Hypointensities in basal ganglia of premanifest Huntington's disease (Jurgens et al. 2010) PreHD 16 Measure signal hypointensities in basal ganglia and assess for clinical/biological associations:  - 3T MRI, T2 - 8QLILHG+XQWLQJWRQ¶V'LVHDVH5DWLQJ6FDOH8+'56 - ROI: whole brain volume and manual segmentation ÏT2 signal in caudate, putamen, GP Correlation: basal ganglia hypointensities with UHDRS motor scale, CAG size and greater probability of developing symptoms within 5 years in gene carriers Ctrls 13 Elevated Brain Iron is Independent from Atrophy in HD (TRACK-HD) (Dumas et al. 2012) PreHD 22 Correlate iron levels to amount of atrophy: - 3T MRI, T2, MFC - ROI: caudate, putamen, GP, hippocampus, amygdala, nucleus accumbens, thalamus Ï Iron in caudate and putamen in early HD vs. controls.  Correlation: Iron content in putamen with disease state in gene carriers, independent of volume Early HD 27 Ctrls 25 Alterations in brain transition metals in Huntington disease: an evolving and intricate story (Rosas et al. 2012) PreHD 28 Identify transition metals by MRI: - 1.5T MRI T1 SWI  Field Mapping - ROI: based on anatomical T1 images ÏIron in pallidum, caudate and putamen Correlation: with increasing disease severity ÏIron in cortex in advanced HD  Early HD 34 Ctrls 56 Seeking Huntington disease biomarkers by multimodal, cross-sectional basal ganglia imaging. (Sanchez-Castaneda et al. 2013) PreHD 17 Identify MRI markers of various neurobiological changes that occur in subcortical nuclei:  - 3T MRI  - T1W, T2*W, T2W, relaxometry (ferritin detection), and DTI = mean diffusivity (MD), fractional anisotropy (FA) - ROI: caudate, putamen, GP, thalamus, nucleus accumbens, hippocampus, amygdala Ï Ferritin in basal ganglia ÏIron in GP (early HD>pre-HD) Weak correlation: iron accumulation and decreased MD in the GP and increase FA in striatum and GP   Early HD 12 26  Imaging iron in the brains of HD patients began in the early 1990s. An early report by Bartzokis et al. (1999) indicated increases in FDRI signal in the caudate, putamen, and GP, and a decreased signal in the frontal white matter of brains of HD-afflicted patients (Bartzokis et al. 1999). This study concluded that iron appears significantly increased in the basal ganglia of HD patients comprised of grey matter, and iron may even be decrease in white matter structures. The same group later found that iron elevations are specific to earlier-myelinating regions (caudate, putamen, and splenium of the corpus callosum) and not present in later-myelinating regions (hippocampus, thalamus, genu of the corpus callosum, and frontal lobe white matter). The lengthened relaxation times in later-myelinating regions may not be explained by iron levels but are possibly due to degrading white matter tissue and an increase in water signal (Bartzokis et al. 2007). Increased iron may be related to neuronal loss in the striatum, and premature myelin breakdown may occur due to mHtt effects on vesicular axonal transport, thereby causing excessive oligodendrocyte production associated with a homeostatic attempt to repair/remyelinate these regions, requiring more sources of iron (Bartzokis et al. 2007). Different hypointensities have been reported between the left and right basal ganglia in HD (Vymazal et al. 2007), suggesting interruptions in axonal transport, which has previously been observed in stroke patients (Zecca et al. 2004). Vyamazal et al. (2007) suggest that covariations in T2 measurements and CAG repeats may be reflective of an increase in ferritin in the pallidum and a decrease of ferritin in the left caudate, which may possibly be another form of iron such as the low±PROHFXODUµWR[LF¶IRUPVWKDWGRQRWHQKDQFHT2 relaxation (Vymazal et al. 2007).  A further study confirmed findings of increased iron using 3D-T1-weighted scans to acquire whole brain volume, and looked at a correlation between T2 hypointensities and clinical measures in premanifest HD gene carriers (Jurgens et al. 2010). Hypointensities in the basal ganglia showed associations with higher UHDRS motor scale, higher CAG number, and greater probability of developing symptoms within 5 years in gene carriers, suggesting this could be considered a biomarker for HD (Jurgens et al. 2010). Dumas et al (2012) used similar methods, magnetic field correlation (MFC) mapping to quantify MFIs (Dumas et al. 2012), to show that higher MFC values were observed in early HD vs. controls and premanifest patients, but no significant difference between premanifest and controls was    27 observed. MFC values in caudate/putamen were higher in early manifest HD than in controls, yet these patients had smaller brain volumes than premanifest patients and controls. The conclusion is that iron content in the putamen relates to disease state in gene carriers, independent of brain volume.  Rosas et al (2012) utilized SWI and field-mapping measurements to identify brain metals in premanifest HD (Pre-HD), manifest HD, and controls (Rosas et al. 2012). Pre-HD and HD show higher signals in pallidum, caudate and putamen, associated with increasing disease severity. Advanced HD patients even showed higher field mapping values in the cortex. Sanchez et al (2013) used a multimodal approach with T1/T2 measurements to demonstrate that increased iron in the basal ganglia of HD patients is independent of aging and starts before disease becomes manifest (Sanchez-Castaneda et al. 2013). An increase in iron accumulation was reported and weakly correlated with decreased mean diffusivity (MD) in the GP and increase fractional anisotropy (FA) in the striatum and GP. MD and FA were calculated from diffusion-tensor imaging (DTI) measurements. However, the higher FA values found in pre-manifest and manifest HD may be due to selective loss of some specific subcortical connections that has transformed the striatum into a more organized structure (Douaud et al. 2009). All HD subjects also showed increased ferritin in basal ganglia, particularly the GP in early HD compared to pre-HD.  3.3 Biochemical and Histochemical Evidence There has been various biochemical/histochemical evidence of iron alterations in HD over the past two decades. Table 3.3-2 summarizes this evidence from studies indicating iron alterations occur in HD.  28  Table 3.3-2: Histochemical and biochemical studies assessing iron accumulation in human HD Research Study Sample size Methods Findings in HD population Alterations in the levels of iron, ferritin and other trace metals in Parkinson's disease and other neurodegenerative diseases affecting the basal ganglia (Dexter et al. 1991) HD 2-10 Post-mortem brain tissue:  - ICP-MS to quantify Fe, Cu, Zn, Mn levels  - Ferritin radioimmunoassay - ROIs: caudate, putamen, cerebral cortex, SN, cerebellum Ïiron in caudate nucleus  Ïcopper in putamen    Ctrls 32 Serum ferritin deficiency in Huntington's disease patients (Bonilla et al. 1991) HD 18 Blood samples: - GFAAS to quantify iron - Ferritin radioimmunoassay - Vi B12, folic acid, hemoglobin, hematocrit  Ðserum ferritin (females>males) Ðhemoglobin levels (males) Ctrls 86 Ferritin accumulation in dystrophic microglia is an early event in the development of Huntington's disease. (Simmons et al. 2007) HD 17 Post-mortem brain tissue: - ROIs: striatum, frontal cortex - Ferritin immunolabeling  Ïarea of ferritin labeling in striatum and cortex Ïstriatal ferritin with disease progress Ctrls 8 Alterations in brain transition metals in Huntington disease: an evolving and intricate story (Rosas et al. 2012) HD 25 Identify transition metals by ICP-MS: - ROIs: pallidum, putamen, anterior frontal, precentral, anterior cingulate, and occipital cortex  Ï iron in putamen, pallidum, occipital cortex Ï zinc in pallidum  Ð manganese in superior frontal region Ctrls 12 29  The earliest direct study of trace metal changes in post-mortem HD brain was published by Dexter et al. which analyzed levels of iron, copper, zinc, and manganese in HD brains using ICP-MS (Dexter et al. 1991). Of note, the HD brains for this study were identified pathologically by marked cell loss and gliosis in the caudate and putamen, as genetic testing was not yet available. The sample size in this study was limited to 2-10 HD samples for different brain regions, with only four caudate and putamen samples for HD. The study reported a 56% increase in iron levels in the caudate and a 44% increase in the putamen in HD patients compared to controls, as well as a trend of higher copper in the putamen and SNpc. No differences were detected in zinc or manganese. A human spleen ferritin antibody (composed of mainly L-ferritin) was used to measure ferritin levels but no changes were detected in HD. It was also mentioned that brain areas were not always identical and came from different brain banks with different post-mortem intervals. This is concerning, as it was shown in 2010 that formalin fixation and different post-mortem intervals have distorting effects on the levels of brain transition metals in archived samples (Schrag et al. 2010). Although there are concerns about the robustness of these findings, this is the study generally referenced to show increased striatal iron levels in HD. In the same year, a group looked at the levels of serum ferritin in HD patients (Bonilla et al. 1991). This group examined serum from 18 HD patients and 86 healthy controls to compare levels of iron, ferritin, B12, and folic acid, and found significantly diminished levels of serum ferritin in choreic HD patients. A few years later serum iron was again looked at again with ferritin and total iron binding capacity, in 42 HD patients with early HD and 148 matched controls (Morrison and Nevin 1994). Again only serum ferritin levels were significantly lower in HD male subjects compared to controls. The relevance of these peripheral serum findings to the underlying biochemical defect in HD and the role of potentially elevated brain iron levels in HD patients is unknown, but these findings are similar to the decreased serum iron and increased brain iron levels reported in other NBIAs (Chinnery et al. 2007). More recently, Simmons et al. suggested that there are perturbations of iron metabolism in HD based on immunohistochemical analysis of post-mortem HD brain sections (Simmons et al. 2007). Cellular ferritin (L-chain) was quantified with antibody  30 staining and appeared to be increased in the striatum of early grade HD patients. By grade 2, ferritin staining was also increased in cortex along with activated microglia. There were morphologic changes in microglia that were associated with HD progression. Perls staining was used to assess localization of Fe3+ -- which was suggested to be exclusively contained in microglia. In this study, Fe3+-positive microglia were identified by morphological criteria only; thus, further investigation is warranted to confirm the cell type expressing elevated ferritin in these. Nevertheless, ferritin immunostaining increased with age and correlated with microglia activation. However, the increase in ferritin and Fe3+ staining compared to controls could also be due to loss of neurons and the resulting increase in glial cells. Simmons et al. also assessed Perls staining and ferritin levels in the R6/2 HD mouse model, and the first time that elevated brain iron and ferritin levels were described in a murine HD model. In addition, Htt immunostaining (antibody not specified) did not appear to co-stain with microglia or ferritin, but may have been indicative of inclusions in late stage R6/2 mice. This study suggests there is perturbed iron metabolism in HD, albeit not with unequivocal evidence.  In addition to MRI analysis of a cohort of HD patients, Rosa et al. also assessed levels of transition metals in HD port-mortem brain tissue to verify the identity of metals measured by MRI (Rosas et al. 2012). Similar to the cohort of MRI HD patients, post-mortem HD brains had increases of iron in putamen, pallidum, and occipital cortex. There were also reported increases of zinc in pallidum and a decrease of manganese in the superior IURQWDOUHJLRQ7KHVHUHVXOWVFRUUHODWHGZLWKWKHLQFUHDVHGµLURQ¶VLJQDOLQSDOOLGXPFDXGDWHand putamen as well as with disease severity. Based on these findings the authors concluded that metal modulation is a potentially beneficial therapeutic approach in HD (Rosas et al. 2012). Another study provided data indicating HD transgenic mice have increased brain levels of iron and copper using ICP-MS (Fox et al. 2007). CAG140 mice were reported to have a 15% increase in cortical iron levels, although no change in brain weight or behavioral analysis was observed. R6/2 mice, on the other hand, did not have significant changes in iron levels, but did have behavioral changes and decreased brain weights consistent with advanced HD. Interestingly, copper was significantly increased in R6/2 mice, by 26% in the  31 striatum and by 51% in the cortex. This study observed that N-terminal constructs of Htt interact much more strongly with Cu2+ than Fe3+ or Zn. Cu2+  was shown to be reduced by Htt and Cu2+ also promoted aggregation of Htt. Copper has the potential to promote altered Htt conformation, aggregation, and redox activity, much like it does in AD with beta-amyloid, inducing its oligomerization. Furthermore, several glycolytic enzymes exist that are sensitive to copper-mediated inactivation, including LDH and SDH (Heron et al. 2001; Sheline and Choi 2004). LDH is important for shuttling lactate from astrocytes and used as an energy source for neurons, and lactate levels have been shown to be increased in human HD striatum measured by MRS (Kasischke et al. 2004). LDH mRNA is decreased in N171-82Q mice and human HD brain (Weydt et al. 2006). The potential role of copper in HD pathogenesis is largely un-explored but is a promising area of study in the future. Fox et al. (2007) went on to further identify an N-terminal interaction between mHtt and metals in 12 week-old R6/2 mice. Cortical metal levels were quantified in different biochemical fractions (soluble/membrane/pellet) using ICP-MS and correlated with mHtt levels in each fraction. Copper and iron were significantly increased in the soluble and membrane fractions, and iron also showed significant increases in the pellet fraction. There were roughly 17x more cortical copper than mHtt in pellet form, which would be sufficient to bind all mHtt present and still leave excess metal to disrupt proteins. This study concluded that cellular levels of metals may be altered in HD, and specifically that excess copper and iron may potentially contribute to creating a toxic microenvironment, catalyzing toxic cellular reactions in HD pathology.  Many altered heme-based mitochondrial respiratory chain complexes and Fe-S enzymes are also characteristic in HD striatum (Bowling and Beal 1995). Specifically, activity of the succinate dehydrogenase complex (CII) of the mitochondrial respiratory chain is reduced in the striatum in HD (Gu et al. 1996; Browne et al. 1997). This loss of activity is associated with reduced expression of two subunits, one of which is an Fe-S subunit  (Benchoua et al. 2006). It has been shown that dysfunction of CII has a direct effect on degeneration of striatal neurons in HD (Damiano et al. 2013). Examining the subunits in three different HD models showed preferential loss of CII expression and activity, the Fe-S subunit more so than the other subunit. Significant overexpression of these proteins showed  32 evidence of neuroprotection of striatal neurons against mHtt neurotoxicity (Benchoua et al. 2006). Therefore, proper functioning and structural stability of heme-based mitochondrial proteins are extremely important, and loss of their function may contribute to neuronal death in HD.  The mechanisms of metal intoxication in HD were investigated using mouse STHdh striatal neuronal cells (Williams et al. 2010b). Eight neurotoxin metal treatments were screened in these cells: Fe3+, Cu2+, Pb2+, Co2+, Zn2+, Ni2+, Cd2+, and Mn2+. The only metals to show differential survival effects on WT and mutant cells were Cd2+ and Mn2+. HD cells were more sensitive to Cd2+ toxicity, not more sensitive to Fe3+, and were actually protected from Mn2+ toxicity. The authors concluded that Fe3+ may not have been in soluble form and therefore unable to exert a toxic effect on these cells; however, in HD pathology there may be excess Fe2+ in the stressed environment which would be more toxic and reactive. They also showed that YAC128 HD mice had impaired manganese accumulation in the striatum, and propose three possible mechanisms for deficient manganese accumulation and toxicity in HD cells: (i) a decrease in manganese uptake, (ii) an increase in manganese export, and (iii) a decrease in manganese storage capacity. This intriguing phenotype of mutant HD cells does not appear to be due to altered cellular iron levels.  The same group continued to investigate the role of manganese in HD, focusing on pathways that contribute to cellular manganese transport, including the iron pathway (Williams et al. 2010a). Iron and manganese transport are tightly linked, both utilizing TfR-mediated uptake, although TfR has a higher affinity for iron. Both metals utilize DMT1 for transportation and the iron exporter ferroportin has been shown to participate in Mn export. Evidence for dysregulation of TfR expression has already been reported (Trettel et al. 2000). Striatal HD mouse lines were utilized to assess manganese exposures and resulting changes in TfR, DMT1, and iron levels. TfR expression was actually decreased compared to WT cells in response to a low chronic dose of manganese exposure, and DMT1 levels showed no significant changes. Manganese exposure led to increased iron levels only in WT cells, and adding low or high ferric chloride treatments to cells resulted in HD cells accumulating 30-40% less iron than WT cells. Basal levels of manganese were 10x higher in HD than WT cells, but there were no basal difference in levels of iron, despite differences in TfR levels.  33 The HD cells had decreased iron and manganese uptake compared to WT cells, but no differential sensitivity to iron toxicity. Again, this may be due to the lack of solubility of Fe3+, but investigation of the iron pathway has also lead to very interesting results in the response of mHtt cells to manganese.  A very recent paper addressing the role of iron in HD mouse models has reported evidence supporting a potentiating role for a specific form of neuronal iron accumulation in HD (Chen et al. 2013a). This group previously showed that iron did not interact with N-terminal Htt fragments, and thus suggest that any effects of mHtt on iron must be mediated by downstream iron homeostatic pathways (Fox et al. 2007). The aim of this study was to investigate whether brain iron elevation contributes to disease pathogenesis in a mouse model of HD, with a focus on identifying cellular and subcellular sites of iron accumulation, characterizing changes in iron protein response machinery, and assessing the effects of an iron modulatory treatment. A time-course of iron levels in R6/2 mice revealed cortical iron elevations when behavior abnormalities were present, and striatal iron elevation occurred later. Another HD mouse model (N171-82Q HD mouse) had elevations of cortical iron, but did not have elevated striatal iron compared to WT controls, thus was not used since it did not recapitulate what is seen in patients.  A microanalytical technique, synchrotron X-ray fluorescence (XRF), was used to visualize cellular and subcellular locations of iron in brain sections. Ferrous iron, Fe2+, was identified in small puncta consistent with secondary lysosomes in brains from R6/2 mice. There were no major differences in ferric iron staining. These findings of a specific localization of Fe2+ accumulation are interesting since mHtt is known to affect trafficking of endosomal components, and HD brains contain large amounts of lipofuscin. Lipofuscin is believed to be residue of lysosomal digestion of oxidative products and damaged organelles and contains various metals including iron. An increase in lysosomal activity, an imbalance of degradation products in the presence of iron, and an acidic vesicular environment provides a potentially hazardous microenvironment that could promote iron-induced oxidative stress.   34 3.4 Iron Chelation Studies in HD Mouse Models Few studies have addressed iron chelators in HD. It has previously been shown that clioquinol down-regulates mHtt expression in vitro and mitigates pathology in the R6/2 HD mouse model, possibly by silencing redox activity of mHtt (Nguyen et al. 2005). A green tea flavonoid and copper chelator, epigallocatechin-gallate, has also been tested in a Drosophila model of HD and shown to modulate early events in Htt misfolding and reduce neurotoxicity (Ehrnhoefer et al. 2006). Utilizing ONOO- decomposition catalysts prior to treatment with neurotoxins have proved beneficial. In an HD neurotoxin rodent model, iron porphyrinate, Fe(TPPS) attenuated and even prevented pathologic markers including immunoreactivity to GFAP, IL-6, and iNOS (González-Cortés et al. 2008), in addition to ameliorating motor deficits and improving recovery of mitochondrial function in synaptic membranes (Pérez-De La Cruz et al. 2005; Pérez-De La Cruz et al. 2009). Iron is a cofactor for the enzyme 3HAO of the kynurenine pathway, and an increase in iron tends to increase 3HAO activity, resulting in increased formation of toxic QA.  HD rodent and human post-mortem tissue brain homogenate was treated in vitro with either ferritin or the iron chelator HBED (N,N0-bis(2-hydroxybenzyl) ethylenediamine-N,N0-diacetic acid), and this resulted in a dose-dependent attenuation of QA toxicity (Stachowski and Schwarcz 2012). Chen et al (2013) investigated whether brain iron levels contribute to disease pathogenesis in the R6/2 mouse model of HD, and have reported the effects of an iron modulatory treatment on the phenotype of this model (Chen et al. 2013a). Delivery of the iron chelator deferoxamine (DFO) via osmotic pump into the left ventricle of young R6/2 mice led to gradual improvement in Rota-rod function compared to vehicle-treated R6/2 mice. DFO-treated mice had significantly decreased ventricular dilation versus untreated mice. This paper concluded that these findings are compatible with an effect of the iron chelator in reducing endosomal and/or lysosomal iron-induced oxidative stress as has been reported previously in another model system (Kurz et al. 2006). This was, intriguingly, the first evidence of benefits from iron chelation therapy in an HD mouse model.  35 3.5 Wildtype Huntingtin Has a Functional Role in Iron Regulation Evidence supporting iron dysregulation in HD has also come from studies of the normal biologic function of WT Htt. Deletion of the huntingtin gene in murine embryonic stem cells has a dramatic effect on the appearance of perinuclear organelles, mitochondrial clusters, Golgi, ER, and recycling vesicles (Duyao et al. 1995; Hilditch-Maguire et al. 2000). The TfR LVDPDUNHUXVHGIRU UHF\FOLQJYHVLFOHVDQG LVDOWHUHG LQ+WW³NQRFN-RXW´FXOWXUHVexpressing significantly more TfR than WT cultures. An iron chelator was used to stimulate the iron pathway, which dramatically increased TfR expression in WT cells. However, it also appeared that Htt protein expression increased significantly in WT cells after chelation treatment, suggesting that Htt gene expression is regulated by cellular iron levels. This study raises the interesting hypothesis that iron chelation therapy may actually increase Htt levels in HD. However it is not known if iron chelation also leads to increase expression of mHtt, but would be important to address since increases could theoretically accelerate HD. However, this study concluded that Htt is an iron-regulated protein essential for the development of normal nuclear and perinuclear organelles (Hilditch-Maguire et al. 2000). TfR1 protein levels are also elevated in ES cells lacking Htt derived from homozygous knock-out mice (Duyao et al. 1995). Further support for a functional role of Htt involved in iron homeostasis comes from a zebrafish model of Htt knock-down during development (Lumsden et al. 2007). This resulted in developmental defects that included a cellular iron starvation phenotype shown again by increased transcripts of TfR. This was found to be due to decreased hemoglobin (Hb) production, despite the presence of iron within blood cells, and was not due to altered ȕglobin expression (Metzler et al. 2000). Erythrocytes acquire iron for Hb exclusively via TfR-mediated endocytosis of transferrin, which places the disruption of Hb production downstream in the Htt-deficient blood cells. It appears that without the presence of Htt, iron transport was disrupted, but when biologically available iron was provided to cytoplasm there was a rescue of hypochromic blood and morphological phenotype. The levels of TfR1a and TfR1b mRNA transcripts were quantified, and both increased in Htt-deficient zebrafish embryos. These results support the evidence of Htt having a role in transport of vesicles and endosomes through the iron-recycling pathway and indicate Htt may be involved in making  36 endocytosed iron available for cell utilization. While HD pathogenesis is generally considered to be caused by toxic gain-of-function effects of mHtt, there is evidence that loss of wildtype Htt function could also play a role by contributing to dysregulated iron homeostatsis.  37  EXPERIMENTAL AIM & HYPOTHESIS The initial aim of this thesis was to measure iron-related changes or potential increases of iron in the YAC128 HD mouse model at various time points. It was hypothesized that this mouse model would mimic gradual increases in striatal iron as seen in various published literature using MRI techniques to quantify the iron. An aged cohort of 18 month old mice was available at the commencement of this study and thus we first analyzed this time point. This is late stage disease for the mice so if there are any iron-related changes in the brain, this is a time point we would expect to see accumulation, based on evidence from human HD patients versus controls in MRI studies.   We hypothesize that the YAC128 HD mouse model, which closely recapitulates human HD, will develop elevated striatal iron levels compared with wild-type littermates. The methods used to measure iron levels included MRI, GFAAS, and a histochemical stain for iron. Magnetic resonance spectroscopy (MRS) was utilized in accordance with MRI measurements to see whether a spectra-width difference, due to MFIs such as iron, could be detected between genotypes  due to MFIs such as iron. This study based on previous evidence that HD patients exhibited a larger full-width half maximum (FWHM) than non-HD control patients when comparing MRS spectra. Post-mortem HD brain samples and control samples were also used to measure iron levels in human tissue in addition to the mice. Post-mortem HD patient and control brain samples were obtained from the Huntington Disease BioBank at the University of British Columbia.  38  CHAPTER 4 : METHODS 4.1 Animal Husbandry & Surgery Transgenic YAC128 mice expressing WT or mHtt with ~128 CAGs from a yeast artificial chromosome were used for these experiments, with WT littermates used as controls (Slow et al. 2003). Mice were bred and raised in the Transgenic Facility at the Centre for Molecular Medicine & Therapeutics (CMMT). Transgenic YAC128 mice were bred on the FVB/N (Charles River, Wilmington, MA) background strain. Mice were group housed with a normal light-dark cycle (lights on at 6:00 AM, lights off at 8:00 PM) in a clean facility and given free access to food and water. All animal protocols were approved by the Animal Care Committee at the University of British Columbia. Genotype was determined at birth and confirmed at death using tail clippings. All surgical procedures were performed in sterile conditions and in accordance with National Institutes of Health guidelines for animal care.  At sacrifice, mice were anesthetized by intraperitoneal injection of freshly prepared tribromoethanol (Avertin® Sigma, 1.6 mg/ml).  Blood was collected from heart, and mice were transcardially perfused with ice-cold saline for 10 minutes. Brains were removed and wet weight recorded. Small portions of peripheral tissues were dissected, the left half of brain microdissected, and the right half of brain stored; all samples were immediately frozen and stored at -80*C until required.  For one positive control mouse in neuroimaging: A 5-month old mouse was anesthetized by inhalation of 4% isoflurane with 1% oxygen and positioned in a stereotaxic frame (Stolling In). Subcutaneous analgesic include 0.1 mg/kg buprinophine (30 µl, 0.03 mg/ml) and local analgesic bupivacaine at 1 mg/kg (50 ul/mouse). A working solution (2 mg/ml) of Iron Dextran (Sigma, D8517) was made from the stock solution (100 mg/ml) for an iron dose of 1 µg/0.5 µl. Coordinates of injection from Bregma were: +0.8 mm anterior, +1.8 mm mediolateral, and 3.5 mm dorsoventral. A hole was drilled and injector (Nanoliter  39 2000, WPI) inserted to target position. The needle was left in at injected site for 2 min, and the iron-dextran solution was injected at a rate of 250 nl/min (0.2 ul for left striatum). The needle was left in an additional 5 minutes when it was then withdrawn, injection site sutured and cleaned, and mouse placed in a clean cage with heating pad for observation. Mouse checked every 30 min until awoken, and transferred to a clean cage.  4.2 Magnetic Resonance Imaging A 7 Tesla preclinical scanner (BrukerBiospin, Ettlingen Germany) was utilized at the UBC MRI Research Centre for susceptibility-weighted imaging (SWI). A multi gradient echo (MGE) sequence was applied with 5 echoes: Echo times (TE) = 4, 8, 12, 16, 20 milliseconds and repetition time (TR) = 30 milliseconds. The acquired matrix was 256x128x128 and reconstructed matrix of 128x128x128 with a resolution of 100x100x100 microns. Images were SURFHVVHGE\9DQHVVD:LJJHUPDQQRI'U$OH[DQGHU5DXVFKHU¶s lab to generate relaxation (R2*) maps, considered to correlate with iron content. An online anatomical mouse brain atlas was used to segment brain regions (MRI-based mouse brain atlas of C57BL/6 obtained from http://brainatlas.mbi.ufl.edu/) overlaid on SWI images using FSLview software.  The same ten 18 month old mice (n = 4 YAC128, n= 6 WT) were used for MRI and MRS measurements, plus one 5 month old FVB mouse injected intra-striatally with iron (see surgery above) as a positive control for neuroimaging.  4.3 Magnetic Resonance Spectroscopy All experiments were performed on a 7 Tesla preclinical scanner (BrukerBiospin, Ettlingen Germany) using Paravision 5.1. These mice were the same cohort from MRI imaging, although were imaged a month-and a half later at 19 months of age. This was performed again to increase the spectral width of the acquisition to avoid data processing issues with the spectral fitting program, and to reduce contamination from other regions, we  40 reduced the voxel size from 2.5x2.5x2.5mm3 to 2x2x2mm3, at the expense of slightly lower signal to noise ratio. Prior to the collection of spectroscopy data, RARE spin echo images were acquired in three orientations to assist voxel placement, and a fieldmap-based shimming procedure was invoked to reduce magnetic field inhomogeneity to improve spectral linewidths. The water-suppressed PRESS sequence was used to acquire the spectroscopy data within a 2x2x2mm3 voxel (TR/TE = 5000/10.48ms, # averages=240, 20 minute acquisition, acquisition bandwidth=10 ppm, eddy current compensation on), centered in the caudate putamen (which was identified on a MRI-based mouse brain atlas of C57BL/6 obtained from http://brainatlas.mbi.ufl.edu/).  An unsuppressed water spectrum was acquired to serve as an endogenous reference to make absolute quantification of metabolite concentrations possible.  Spectral processing was performed using LCModel by fitting the data to a predefined template.  Only metabolite concentration estimates with less than 15% standard deviation were deemed to be reliable measures; therefore the following metabolite concentrations were included in subsequent analyses:  Cr+PCr, Glu+GLn, Ins, Tau, GPC+PCh, NAA+NAAG, and GABA. 4.4 Immunohistochemistry  3HUO¶V 3HUIXVLRQV as described in (Meguro et al. 2008): 3HUO¶V VROXWLRQ SRWDVVLXPferrocyanide and HCl) allows one to histologically visualize iron in tissues, as the ferric iron deposits react with soluble ferrocyanide to form insoluble blue deposits that can be enhanced with DAB. Five one year-old mice (n=5 WT, n=5 YAC128) were given intraperitoneal injections of Avertin and perfused with 3% paraformaldehyde for 10 min followed by perfusion with 7% potassium ferrocyanide in 3% HCl and saline for 10 min. Whole brains were removed and post fixed in 3% paraformaldehyde for 3 hours and frozen at -80°C. Brain sections were cut using a cryostat, and every 8th coronal section (25 µm) through the striatum was used for stereology. The floating sections underwent 3,3'-Diaminobenzidine  (DAB) enhancement: 4  41 sections floating per 24 well-plate and incubated with 1:10 DAB in dilution buffer for 30 minutes. The reaction as stopped by rinsing with phosphate-buffered saline solution.  Iron positive cells were counted using the Stereology Investigator system (Microbrightfield Inc.) with a counting frame of 25x25 µm and a grid size of 350x350 µm: 8 sections each, counting at least 200 cells per genotype.  4.5 Graphite Furnace Atomic Absorption Spectroscopy GFAAS (Varian 240) was used in all iron quantification experiments. Partitioned tubes were used with a pyrolytically coated graphite tube (Agilent part #6310001200). A bovine liver (1577c) Standard Reference Material was used as an external standard, and a standard curve was run every 10 samples measured. Each sample and standard was measured in a replicate of 3.  Furnace settings: measurement mode peak height, calibration mode in concentration, wavelength 248.3 nm, slit width of 0.2 nm, deuterium lamp background correction, calibration algorithm based on new rational, total injected volume 20 µl, sample volume 15 µl, and 5 µl Palladium matrix modifier solution post-injection (Sigma-Aldrich 428914). Additonal furnace settings: Step Temp (°C) Time (s) Argon Flow (L/min) Drying 85 5 0.3 Drying 95 40 0.3 Drying 120 10 0.3 Pyrolysis 1200 17 0.3 Pyrolysis 1200 6 0.0 Atomization 2700 2.8 0.0 Atomization 2700 2.0 0.3  Total iron measurements were measured similar to described methods (McAuley et al. 2012): Tissue sample wet weight (10-60 mg) measured, followed by digestion in 250 or 500 µl ultrapure concentrated nitric acid (Fisher A467, 67-70%) heated overnight at 65*C in  42 water bath. Next day cooled to room temperature and diluted as necessary in 0.1% HNO3 (all water used was Type 1 (Ultrapure) water prepared by BCIT technician Bob Nicholson). GFAAS for non-heme iron measurements similar to previous methods (Grundy et al. 2004; Chen et al. 2013a): tissue sample wet weight (10-60 mg) measured, followed by homogenization in 260 µl of 500 µm EDTA. Then 100 µl of trichloroacetic acid (TCA in 20% EDTA) was added and sample vortexed and incubated for 30 minutes at 90*C. Then 700 µl of 500 µm EDTA was added and sample vortexed. The sample was centrifuged at 13,0000 rpm for 10 minutes, and supernatant removed and froze in -20*C freezer until needed.  GFAAS for measuring iron in red blood cells modified from (Fitsanakis et al. 2008): using 100 µl of blood, add 25 µl 0.5% Triton-X and vortex. Bring volume up to 1 ml with 2% HNO3. Centrifuge at high speed for 15 minutes (13,000xg) until the supernatant is clear. Remove 100 µl of supernatant and bring up to 1 ml with 1% HNO3. Analyze samples.  See Table 4.5-1 for characteristics of post-mortem tissue analyzed. Human striatal and cortical post-mortem samples were obtained from the Huntington Disease Biobank at the University of British Columbia (UBC), located at the CMMT, with approval from UBC Ethics.  Samples were collected with minimal postmortem interval to autopsy and stored at -80°C prior to use in experiments. 43  Table 4.5-1: Characteristics of post-mortem brain tissue.    HD brain tissue samples Age Sex Grade PMI Age-matched Controls Age Sex PMICaudate 41 F 0-1 5.5 Caudate 29 F 4.5Caudate 35 M 3 19 Caudate 46 M 60Caudate 43 F 3 3.5 Caudate 60 M 8Caudate 53 F 3 9 Caudate 60 F 17Caudate 59 F 3 7 Caudate 72 M 2.5Caudate 61 M 3 3 Caudate 72 F 10Caudate 63 F 3 5.5 Caudate 74 M 6.25Caudate 64 M 3 10 Caudate 75 M 12.5Caudate 75 F 3 3 Caudate 75 M 10.8Putamen 62 F 3 11 Putamen 65 M 22Frontal Cortex   35 F ? 19 Frontal Cortex   29 F 4.5Frontal Cortex   63 F ? 2 Frontal Cortex (MFG) 36 F 10Frontal Cortex   (MFG) 41 M 3 5.5 Frontal Cortex   42 F 17Frontal Cortex   (SFG) 43 F 3 3.5 Frontal Cortex  (MFG) 46 M 10Frontal Cortex   (SFG) 62 F 3 11 Frontal Cortex   50 M 5Frontal Cortex  (MFG) 63 F 3 5.5 Frontal Cortex   60 M 8Frontal Cortex  (MFG) 64 M 3 10 Frontal Cortex   68 M 7Frontal Cortex   (MFG) 75 M 3 3 Frontal Cortex   74 M 2Frontal Cortex   (MFG) 53 F 4 9 Frontal Cortex   74 M 6Frontal Cortex (MFG) 59 F 4 7 Frontal Cortex   76 M 1844  4.6 Iron Loading Studies in Mice Four two year old FVB WT mice were injected intraperitoneally daily (Mon-Fri) for 2 weeks with 70 mg/kg Iron-Dextran (Sigma' LQ5LQJHU¶V VROXWLRQ WR reach a total dose of 700 mg/kg (n = 4 WT iron-dextran injected). An equal number of two-year old FVB mice were also injected with saline control over the same time period (n = 4 WT saline injected). This experiment was repeated on two-year old mice (n = 3 YAC128, n = 3 WT), without a saline control due to minimal numbers of mice available. 4.7 Colorimetric Ferrous Iron Assay An iron colorimetric assay kit was purchased (Biovision K390-100) to measure ferrous iron in one year old mice (n = 12 YAC128, n = 8 WT). Samples were homogenized in WKHNLW¶Viron assay buffer, followed by centrifugation at 14,000xg for 15 minutes. In the assay, ferric carrier protein will dissociate ferric into solution in the presence of acid buffer. Samples of 50 µl were added to a 96-well plate and incubated for 30 minutes with 5 µl iron reducer, and then 100 µl of iron probe is added to each well, including standards, mixed, and left to incubate for 60 minutes at room temperature in the dark. After reduction to the ferrous form (Fe²᥿), iron reacts with Ferene-S to produce a stable colored complex and give absorbance at 593 nm. A specific chelate chemical is included in the buffer to block copper ion (Cu²᥿) interference. The kit measures iron in the linear range of 0.4 to 20 nmol in 50 µl sample, or 8 µM to 400 µM iron concentration in various samples.  4.8 Statistics The statistics used to compare the magnitude of relaxation maps between genotypes as an indirect measure of iron quantification were the Kruskal Wallis test and one-way analysis of variance  45 to calculate p-values. Statistics used to compare genotypic values of FWHM and differences of brain metabolites from MRS data, the number of iron positive cells in the striatum, and iron levels between individual brain regions of mice and post-mortem HD were all analyzed using a two-sample t-test. A two-way analysis of variance was also used to measure significant interactions between brain regions. A two-way analysis of variance was used to calculate significance of iron content in red blood cells between 18 month and 25 month old mice. Kruskal-Wallis one-way analysis of variance was used to compare levels of iron in the striatum and liver of iron-injected mice (WT and YAC128) versus saline control mice. Unless stated otherwise in bar graph figures, bars represent the mean with error bars representing the standard error of the mean.   46 CHAPTER 5 : RESULTS 5.1 Regional Brain Iron Levels Measured by MRI are Similar in YAC128 and WT Mice The initial plan was to perform a longitudinal analysis quantifying brain iron levels at various time points in the YAC128 HD mouse model, from 3 months to 24 months. However, since there was already an aged cohort of mice available, we decided to investigate an 18-month cohort of mice representing late-stage +XQWLQJWRQ¶Vdisease. If changes in iron levels occur in this mouse model we would expect to see it by this time point. T2-weighted cross-sectional brain images were segmented using a C57BL/6 MRI-based mouse brain atlas (see Figure 5-1). 47  Figure 5-1: (A) Representative SWI images from an 18 month-old mouse; Overlaid with anatomical C57BL/6 mouse brain atlas using FSLview software: (B) Coronal view; (C) Axial view; (D) Sagittal view.  48  However, to our surprise, there were no significant differences detected in regional brain relaxivity values between genotypes. Based on previous human data, we expected to see significantly different levels of iron in the caudate/putamen and pallidum. However, it appears there is a trend for the mice to all have quite variable R2* values in each of the measured brain regions as can be seen in Figure 5-2. R2* values were also compared in each brain region across each mouse ID to determine if there were any trends of mice with increased R2* values, however, measurements were generally variable between mice (see Figure 5.3).  It is noteworthy to mention that the positive-control mouse, with intrastriatally injected iron, exhibited the lowest R2* values. MRI and MRS ended up being repeated to account for technical errors. When the MRI and MRS were repeated, this positive-control mouse had been injected intrastriatally for 5-6 days. Thus, it appears that the intrastriatally-injected iron cannot be detected by MRI or MRS within this region by this time point, or the voxel placement may not have been covering this area. This mouse was only 5 months-old when it was injected and remained very hyper-reactive and stressed for the duration of its life ± when it was sacrificed 5 months later. However, we previously did some intrastriatal iron injections in mice while WURXEOHVKRRWLQJ IRU 3HUO¶V VWDLQLQJ DQG D FRXSOH GD\V you could directly see an increase in iron staining. However, this iron staining appears tightly localized and not spread throughout the entire brain. Thus, it is possible this dose of iron is too small for MRI detection or voxel placement did not pick it up.  49  Figure 5-2: Magnitude of relaxation maps (R2*) calculated from various brain regions of interest: thalamus, pallidum, hippocampus, and caudate/putamen. Measured in 18 month-old YAC128 (n=4) and WT littermates (n=6). Dot plots represent each measurement, mean, and standard error of the mean. No significant differences were found using the one-way analysis of variance Kruskal Wallis test to calculate p-values.   50  Figure 5-3: Quantified R2* values for brain regions of interest compared to the average across all 18 month-old YAC128 (n=4) and WT (n=6) mice. Note: positive control mouse #160 (left). The x-axis is labeled by mouse-identification number (1 positive control mouse + 10 experimental mice), and the connected dots indicate a particular brain region by color.   51  5.2 Brain Metabolite Concentrations and FWHM Quantification via MRS Showed No Significant Differences in 19-Month Old YAC128 and WT Mice  We performed MRS measurements on an aged cohort of YAC128 and WT mice. MRS measurements were taken by placing a 2x2x2mm3 voxel, with the sensitivity to detect and measure seven different brain metabolites, over the left caudate/putamen (see Figure 5-4). The primary objective of MRS was to determine whether a difference in FWHM quantified from MR spectra output, could be detected between YAC128 and WT mice. This was based on previous evidence by our lab (Sturrock et al. unpublished) that utilized MRS in HD patients in the Track-On and Mitigate studies at the Centre for HD at the UBC hospital, using a 3T clinical scanner. With a voxel placed on the left putamen and left frontal cortex, there appeared to be a significant increase in FWHM values in early HD compared to pre-HD and control subject in the putamen only (see Figure 5.5). The output spectra from early HD patients had wider and less pronounced peaks than controls and pre-HD patients, which was predicted to be due to magnetic field inhomogeneities (MFIs), such as iron.  52  Figure 5-4: Representative magnetic resonance spectral graph with labeled detectable brain metabolites (left). Voxel 2x2x2mm3 placed over left caudate/putamen (right).   53  Figure 5-5: Relative FWHM values calculated from magnetic resonance spectra graphs from the left putamen and frontal cortex of  HD patients (pre-HD (n=25) and early HD (n=30)) and controls (n=30). This data was provided by Dr. Aaron Sturrock (unpublished). Statistics XVHGLQFOXGH6WXGHQW¶VW-test between genotypes in each group, and two-way analysis of variance for group differences between brain regions and genotype.  The single star represents a significant difference between control and early HD FWHM in the putamen. Bars represent mean plus standard error of the mean.    54  Similar to our MRI findings, there was no significant difference in relative FWHM values between the YAC128 mice compared to WT littermates (see Figure 5-6, left image). However, since there were no detectable differences in iron measured by MRI, the FWHM quantified via MRS agrees with MRI data indicating that there may not be significant MFIs in these particular mice. However, it was surprising that we did not detect any significant differences in brain metabolite concentrations at 18-months (see Figure 5-6, right image). Other HD mouse models, including R6/2, have shown significant changes in metabolite concentrations during disease progression, however using 9T magnetic field strength (Tkac et al. 2007). In the same HD patient cohort from the Track-On and Mitigate studies, significant changes in levels of N-acetylaspartate (NAA), a marker for neuronal integrity, as well as myo-inositol (mI), a marker for gliosis, were detected (Sturrock et al. 2010). Total NAA and NAA were lowest in early HD compared to pre-HD and controls, and mI levels were 50% higher in early HD than pre-HD, which also correlated with clinical measures (Unified Huntington's Disease Rating Scale (UHDRS) motor scores). Total NAA also correlated with disease burden score and performance on a tongue pressure task in pre- and early-HD. Despite the significance of MRI-detected iron in humans, we did not see significant changes in the YAC128 mouse model. However, it appears the relative levels of brain metabolites corresponds well with the relative levels in humans, although not significantly different metabolite concentrations between genotypes in the mice.   55  Figure 5-6: (Left) Relative FWHM values calculated from MRS in 19 month-old YAC128 (n=4) and WT (n=6) mice. (Right) Concentration of various brain metabolites measured by MRS. No significant differences were detected using two sample t-test between genotypes of FWHM data and for each brain metabolite. Bars represent the mean plus standard error of the mean.   56  5.3 Immunohistochemistry Shows No Significant Differences in Ferric Iron Staining in YAC128 versus WT Mice Total brain iron levels were assessed by immunohistochemistry in aged YAC128 mice. No significant differences were found in brain iron levels via MRI and MRS was detected in 18-19 month old-mice, so an earlier time point was investigated since it is possible that by late-stage disease the levels of iron may have changed/normalized. Therefore, we used a well-established protocol to stain iron Fe3+-positive cells in one year-old mice, with 5 mice per genotype. This highly sensitive protocol requires perfusion of mice with a modified Perls stain to detect Fe3+-positive cells in tissues (Figure 5-7 A). We were unable to successfully co-stain with antibodies (including NeuN/IBA1/GFAP) to detect what type of cell the iron-positive cell was, due to peroxidation from DAB enhancement. Attempts to immunostain prior to DAB treatment were also not successful. A stereology expert in our lab, Dr. Sonia Franciosi, determined the majority of cells resembled oligodendrocytes or microglia, with regards to morphology and cell size. Stereology was used to count the density of iron-positive cells, but again no significant differences detected between iron-positive cells in the striatum of YAC128 and WT (see Figure 5-7, right).   57  Figure 5-7: (A) Representative figures of WT and YAC128 Perls perfused striatum at 100x magnification.  DAB enhancement is used to identify Fe3+-positive cells and counterstained with cresyl violet. (B) The density of iron-positive cells in the striatum of 1 year-old YAC128 and WT littermates was not different (n=5 mice per genotype). Statistic used was the two sample t-test to compare genotypes. Bars represent mean plus standard error of the mean.   58  5.4 Total Elemental Brain Iron Levels Showed No Significant Differences in Aged YAC128 versus WT Mice Since no significant changes in iron have been detected thus far, an alternative analytical method, GFAAS, was utilized to quantify levels of total elemental iron. Total elemental iron levels were measured in blood collected from aged mice to determine whether there was a systemic difference in iron levels within blood cells between YAC128 and WT mice. Although, there were no significant changes in iron levels between YAC128 and WT mice, there appeared to be an overall increase in blood iron levels in 24 month-old mice (n=value) compared to 18 month-old mice (see Figure 5-8).  59  Figure 5-8: Total elemental iron levels in red blood cells from 18 month-old and 24 month-old mice, respectively (n=3 and n=5 YAC128; n=2 and n=3 WT), measured by GFAAS. No significant effect of genotype or age was found using a two sample t-test. Bars represented as mean plus standard error of the mean.   60  To determine whether there are altered iron levels in HD pathogenesis, levels of total elemental iron were quantified in various brain regions of 2 year-old YAC128 and WT littermates. In agreement with previous results from imaging and Perls staining, there were no significant difference in total elemental iron levels were detected in any of the brain regions between 18 month-old YAC128 and WT mice (Figure 5-9).  61  Figure 5-9: Total elemental iron levels in 5 different brain regions of two year-old mice (n=14 YAC128; n = 9 WT) measured by GFAAS. No significant differences were identified using a two sample t-test between genotypes for each brain region. Bars represented as mean plus standard error of the mean.   62  5.5 Post-mortem HD Striatal and Cortical Brain Tissue Show No Trend of Increased Iron Levels Post-mortem tissue was analyzed to determine whether any differences in brain iron levels occur between YAC128 and WT mice. Initially post-mortem tissue from striatum and cortex of HD patients and controls was measured using GFAAS to quantify total iron levels.  No significant differences were found in total iron levels between HD-patients and control patients. Total iron levels values were variable in both striatum and cortex tissues (Figure 5-10 A). Post-mortem tissues had differing post-mortem intervals and were not extracted from identical brain regions. Samples were sex- and age-matched (see Table 4.5-1: Characteristics of post-mortem brain tissue.). Non-heme iron levels were assessed in non-saline-perfused samples to normalize for variability of iron in blood samples. However, non-heme iron levels were variable between subjects and were not significantly different between HD patients and controls (Figure 5-10 B).   63  Figure 5-10: (A) Total elemental iron levels in post-mortem striatum and cortex tissue from HD patients and controls (n=10 per genotype) measured by GFAAS. (B) Non-heme iron levels in post-mortem striatum and cortex tissue from HD patients and controls (n=10 per genotype, new samples used between A and B). No significant differences were identified using a two sample t-test between controls and HD for each brain region. Bars represented as mean plus standard error of the mean.    64 5.6 Intraperitoneal Injections of Iron in Mice Does Not Affect Striatal Iron Uptake and Does Not Show Differential Accumulation in YAC128 versus WT Mice We attempted to identify whether YAC128 mice demonstrated increased vulnerability to iron-overloading when injected peripherally with an external iron source. A pilot study was performed to optimize the injection dose of iron-dextran in with WT mice, based on reported literature. Iron-dextran injections were given to four WT mice and four WT mice received saline control injections. A dose of 70 mg/kg of iron-dextran was selected based on inference from published literature Due to insufficient numbers of YAC128 mice, saline control injections were not performed.  Intraperitoneal (IP) injections of iron-dextran were performed on multiple mice and GFAAS was utilized to determine total iron levels in the striatum compared to liver of treated mice. No significant changes were detected between iron levels in the striatum of saline- and iron-dextran injected mice (see Figure 5-11).  For comparison with peripheral tissue, total iron was quantified in the liver of each mouse. Significantly increased levels of iron were detected in the liver of iron-dextran injected mice versus saline controls. However, no significant difference in iron uptake was detected in the liver between iron-dextran injected WT and YAC128 (see Figure 5-12).  65  Figure 5-11: Total iron levels in striatum of 2 year-old mice after IP injections (70 mg/kg) iron-dextran or saline control. White bar = saline treated WT (n=4); Gray bar= iron-dextran treated WT (n=7); Black bar = iron-dextran injected YAC128 (n=3). No statistical differences were seen using Kruskal-Wallis one-way analysis of variance. Bars represented as mean plus standard error of the mean.     66  Figure 5-12: Total iron levels in liver of 2 year-old mice after IP injections (70 mg/kg) iron-dextran or saline control. White bar = saline treated WT (n=4); Gray bar= iron-dextran treated WT (n=7); Black bar = iron-dextran injected YAC128 (n=3). Statistics used was a Kruskal-Wallis one-way analysis of variance, with significance only between saline and each Fe-Dextran group (p=0.0388). Bars represented as mean plus standard error of the mean.  67  5.7 Fe2+ Levels Did Not Differ in Brain Regions of Aged YAC128 versus WT Mice Lastly, GXULQJ ILQDO VWDJHV RIP\0DVWHU¶V WKHVLVChen et al. published a paper in October 2013 that looked at quantifying levels of iron in another mouse model, the R6/2. A microanalytical technique, synchrotron x-ray fluorescence, was utilized to directly measure trace elemental distribution at the subcellular level. Iron was detected as numerous small puncta of fluorescence in perinuclear cytoplasm of striatal neuronal cell bodies in R6/2 mice (Chen et al. 2013a). Chen et al. also utilized Perls perfusion stain and Turnbulls stain, which stains for Fe3+ and Fe2+, respectively, coupled with electron microscopy for subcellular analysis. However, it appears the difference they found in iron levels in the R6/2 mouse was specifically Fe2+, which appeared to be present in what looked like secondary lysosomes. Chen et al. then measured various iron transport proteins that appeared dysregulated in the R6/2.  Using a ferrous iron assay kit, Fe2+ levels were measured in the striatum, cortex, and hippocampus in 1 year-old YAC128 and WT littermates. However, once again, we did not detect significant differences between genotypes, and levels of Fe2+ appeared highly variable, especially in the striatum. No significant differences in levels of Fe2+(II) were detected between YAC128 and WT mice and Fe2+(II) levels were variable, particularly between striatum. 68  Figure 5.7- 1: Ferrous iron levels measured in striatum, cortex, and hippocampus of 1 year old mice (n=12 YAC128, n = 8 WT) using Biovision iron assay kit. No significant differences were found using a two sample t-test between genotypes of each brain region and one way analysis of variance. Bars represented as mean plus standard error of the mean.   69  CHAPTER 6 : DISCUSSION  Reviewing the published literature to examine potential mechanisms of iron dysregulation in HD, there was a lot of evidence for selective accumulation or alterations of iron levels in affected brain regions in HD. However, it is still currently unclear whether absolute iron levels are unequivocally elevated in the caudate or putamen as an early pathologic change in HD. The previous studies of iron content in post-mortem HD brain samples have had a number of critical limitations; lack of confirmatory genetic testing, small numbers of samples, and high measurement variability. However, the signal coming from MRI studies seems to correlate well with disease stage and may be a useful biomarker in HD, even if this does not directly reflect altered iron levels.  Iron is theoretically one of the easier transition metals to quantify by exploiting its magnetic properties, especially via neuroimaging techniques. The major drawback with this technique is that the signal is an indirect indication of the presence of iron, which may be including other dysregulated metals, ions, or toxic species that could influence the signal. There are many different forms of iron in the brain, but typically only the ferritin signal is reported. Concentrations of the low-PROHFXODUµWR[LF¶LURQDUHWRRVPDOOWREHGHWHFWDEOHYLDMRI, and therefore imaging is not likely providing a complete picture of brain iron.  %DVHG RQ H[SHULPHQWV SHUIRUPHG IRU WKLV 0DVWHU¶V GLVVHUWDWLRQ WKHUH ZHUH QRsignificant differences of iron levels using various techniques including MRI, MRS, Perls perfusion, GFAAS, and a ferrous iron assay kit. Therefore, it appears the YAC128 mouse model does not have significantly different levels of iron compared to WT littermates. There is the possibility that this specific HD mouse model does not develop the phenotype of increased brain iron. This has previously been shown in the R6/2 and N171-82Q (Chen et al. 2012), and it would have been an excellent control to measure these models using these techniques. Also, the positive control mouse used containing intrastriatally injected iron could have been assessed further to verify an increase of iron the striatum and verify it is in the proper position for voxel placement.  Although we did not see significant changes in iron levels, Chen et al. reported the change in iron may be due to Fe2+ levels contained in secondary lysosomes of the R6/2  70 mouse. Even a minor excess of iron from the LIP, loosely-bound proteins, or storage sites could potentially lead to detrimental effects, especially in the presence of H2O2. It is known that degradation and recycling pathways become altered in HD, thus a lot of the iron-induced damage may be coming from dysregulation of vesicle/lysosomal/autophagic activity, which would increase the potential of contained redox-active iron to form toxic by-products. Iron-containing proteins become disassembled or altered in HD pathogenesis, including the heme proteins involved in the electron transport chains (Benchoua et al. 2006). Altered calcium homeostasis and mitochondrial dysfunction are a well-known features in the cellular pathology of HD (Chen et al. 2011). See Figure 6-1 to see how mHtt can lead to oxidative stress in the presence of iron.      71 Figure 6-1: Iron and mHtt-induced oxidative stress in HD.  MHtt binds NMDAR receptor synaptic proteins leading to excitotoxicity and an increase of intracellular calcium levels. Intracellular calcium levels also increase due to mHtt binding to mitochondrial proteins and decreases membrane potential. These mitochondrial alterations create an oxidative environment and increase probability of iron-proteins to disassemble and lose structurally stability. Even the slightest availability of Fe2+ can react with oxygen (O2) that is continually produced by the mitochondria to create superoxide radicals (O2*-). In the presence of O2 Fe3+ can also be reduced to Fe2+ which is then available to hyperoxidize oxygen O2*-. O2*- can react with the abundance of NO produced in the cell by nNOS to form peroxynitrite radicals (ONOO-), and these in turn can react with water to create a nitrite ion and hydrogen peroxide (H2O2). The antioxidant enzyme superoxide dismutase (SOD) also creates H2O2 and O2 by catalyzing the dismutation of O2*- from mitochondria. If there is free Fe2+ available the presence of endogenous H2O2 can lead to the Fenton Reaction, upon which two different oxygen radical species. Again this can lead to further redox reactions that catalyze the continual process of toxic products.      72 The YAC128 mouse model does not develop HD as aggressively as the R6/2, and the slower progression of disease may be related to lower levels of iron in the brain. The YAC128 mouse also tends to gain weight with age, with is not a feature common to HD patients. The YAC128 CAG tract is also not a pure CAG tract, interspersed with 9 CAA codons, thus possibly having an effect on role of RNA structure and toxicity and somatic instability. The lack of vulnerability to CAG somatic expansion could be a confounding factor of these mice that results in difference of pathophysiology.  That being said, we also did not see significant differences in post-mortem HD brain and control tissue, which has been previously reported using other iron-quantifying methods (Dexter et al. 1991; Simmons et al. 2007). A reason for this includes the fact that these brain regions have not been perfused, and thus the contribution of signal from blood introduces huge variability. This was the reason for measuring levels of non-heme iron. However, these regions of cortical and caudate structures could have also varied from each other from which location they were dissected from, as well as having a range of post-mortem intervals which is known to have an effect on detection of trace metals.  If fuQGLQJZDVQ¶WDQLVVXHLWZRXOGhave been nice to actually measure HD patients from the HD clinic using similar imaging techniques to verify this is seen increase of iron signal can be witnessed using our imaging equipment.  The role of glutamate excitotoxicity as a pathological mechanism in HD has been studied for many years and many neurodegenerative diseases are characterized by alterations in glutamate secretion (Aarts and Tymianski 2003). The neurotransmitter glutamate is found throughout the CNS and retina, as well as in non-neuronal tissues, including the gastrointestinal tract, heart, testes, osteoblasts, and pinealocytes (McGahan et al. 2005). As previously mentioned, the testes are a significant area of degeneration in HD (Van Raamsdonk et al. 2007), and interestingly have a similar Htt gene expression pattern as the brain (Guo et al. 2003). The testes express multiple glutamate transporters, including unusually spliced forms, suggesting that glutamate homeostasis ought to be critical in this organ (Lee et al. 2011). The testes and the brain, specifically the striatum, have high levels of glutamatergic activity, and iron levels are known to have a direct impact on glutamate activity. For example, the addition of iron to neuronal cells leads to the increased secretion of glutamate (McGahan et al. 2005). If glutamate excitotoxicity is one of the main causes  73 leading to the degeneration of tissues in HD, any excess metals such as iron could contribute to speeding up the process of degeneration. It is worth considering the similarities and differences between the two regions of highest degeneration, thus identifying pathways and mechanisms of toxicity.  It will also be interesting to see what develops from the Rhes story, as we have already witnessed therapeutic implications with modulating levels of Rhes as well as ACBD3. These more recent findings give clues to possible downstream pathways worth targeting to protect striatal neurons from the vulnerable toxicity of mHtt. Future studies addressing the role of iron toxicity in HD should address the contribution of these proteins as well. See Figure 6-2 for an overview of cellular stress in the presence of mHtt that induces iron uptake into the cell.  It will be important in the future to assess the role of directly altering iron levels in HD model systems. This could be tested through iron overload or depletions studies designed to significantly affect the levels of iron in the brain. HD transgenic mice could be crossed with other genetic mouse models with alterations in iron metabolism, such as hemochromatosis/anemia models, heavy/light chain or mitochondrial ferritin overexpression models, or even by altering the diet of the mice to be iron-supplemented or iron-deficient. Use of iron-overloading or iron-chelation treatments is also a plausible experiment, and must be optimized to find an appropriate age and dose that would affect the mice.  Whether biologically relevant iron overload even occurs in the HD brain is still debatable and further well-controlled studies are clearly needed. The potential for iron overload to play a contributing role in the pathogenesis of HD is an intriguing hypothesis, but additional experimental support is required before clinical trials of iron chelation would be warranted.   74 Figure 6-2: Potential interactions of Iron and mHtt-induced oxidative stress in HD 1) In the presence of mHtt, there is increased sensitivity to NMDA receptor activation which leads to increased intracellular calcium levels. 2) mHtt is also known to bind PSD96 at synapses and affect receptor signaling. 3) Extra-synaptic NMDA receptor activation leads to neurotoxic pathways, including the activation of Rhes which can sumoylate mHtt, and may reduce its ability to aggregate, increasing toxic soluble mHtt leading to altered interactions and dysregulation of other proteins. 4) mHtt is also known to associate with mitochondria, an organelle containing many iron-proteins which is known to become dysfunctional in HD. With loss of membrane potential and functional proteins, the permeability transition pore eventually releases the intracellular calcium from mitochondria. 5) Increased intracellular calcium levels leads to the activation of calcium-dependent enzymes, including nNOS. nNOS can bind to NMDARs via Dexras1 and PSD95, leading to production of NO that directly S-nitrosylates receptor subunits and contributes to altered signaling. The abundance of NO produced in the cell can also form peroxynitrite radicals in the presence of superoxides produced from the altered respiratory signaling in the mitochondria.  Peroxynitrites can react with water to produce more endogenous H2O2.  6) Calcium-activated nNOS has also been shown to bind DMT1 via S-nitrosylation of scaffolding protein Dexras1 and activated Rhes protein, leading to the influx of intracellular iron - at the plasma membrane and endosomal level. 7) This increased influx of iron is then available to react with endogenously produced H2O2 (antioxidant enzyme SOD creates H2O2 and O2 by catalyzing the dismutation of superoxides from mitochondria). This is known as the toxic Fenton reaction, upon which Fe(II) reacts with H2O2 to form a hydroxyl radical, water, and Fe(III) - which can in turn react with H2O2 or superoxides to form Fe(II) and lead to production of another hydroxyl radical species. The presence of Fe(II) and hydroxyl radicals, superoxides, and reactive nitrogen species all leads to increased lipid peroxidation, DNA damage, cellular stress, and eventually neuronal cell death.   75              76 BIBLIOGRAPHY Aarts M. 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