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Identification of novel striatal-enriched transcripts and their analysis in Huntingdon's disease Mazarei, Gelareh 2008

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IDENTIFICATION OF NOVEL STRIATAL-ENRICHED TRANSCRIPTS AND THEIR ANALYSIS IN HUNTINGTON’S DISEASE  by Gelareh Mazarei B.Sc., University of British Columbia, 2005  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE  in  THE FACULTY OF GRADUATE STUDIES  (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  OCTOBER 2008  © Gelareh Mazarel, 2008  ABSTRACT Selective neuronal degeneration of caudate and putamen, collectively known as the striatum, is a disease hallmark in Huntington’s disease (HD). In the striatum of HD patients, the largest neuronal sub-population (comprising 90% of the total population), the GABAergic medium spiny projection neurons, are predominantly lost. The exact mechanism(s) behind this specific neurodegeneration is still unknown. Gene expression changes are considered as important pathogenic events during the course of HD. These changes can be due to a variety of different upstream effects including direct transcriptional dysregulation by mutant huntingtin as well as transcriptional changes as secondary effects of neuronal loss. Many expression studies on diseased human post-mortem brain, as well as different mouse models exhibiting striatal degeneration, have demonstrated changes in the expression of many genes. Genome-wide microarray approach is the common technology used in these studies while striatal-enriched systems have also been studied to identify genes implicated in the pathology of HD. While the former approach can often detect thousands of expression changes, the latter has shown how particular genes important to the functions and physiology of the striatum could be involved in specific vulnerability of this region to neurodegeneration. In this study I have used the Serial Analysis of Gene Expression (SAGE) database (www.mouseatlas.org) and compared the mouse striatum to 18 other brain regions to select for striatal-enriched genes. Within these genes, I have identified: 1) known striatal-enriched genes; 2) genes that have not been previously described as striatal-enriched; and 3) potential novel striatal genes in the genome. The expression of these genes was subsequently tested in the YAC 128 mouse model of HD and candidates with altered levels of expression were examined in the human post-mortem caudate samples. Under this investigation, I could identify interesting transcripts with altered levels of expression in the YAC128 mice. Some of these transcripts showed consistent mRNA expression changes in the  11  human post-mortem tissue. Continuation of this project will include further computational and biochemical analysis of candidate striatal-enriched markers and their implications in HD and will be pursued by me as a PhD project. In summary, this Masters of Science project has resulted in the identification of striatal enriched genes that manifest expression changes in the brain of YAC 128 mouse model of RD and human post-mortem RD brain. This can eventually lead to our better understanding of the pre-existing physiological pathways involved in HD pathogenicity of the disease or alternatively, add to our knowledge about novel mechanistic pathways contributing to selective neurodegeneration in this disease.  111  TABLE OF CONTENTS Abstract  .1  Table of Contents  ii  List of tables  vii  List of figures  viii  Acknowledgements  ix  Dedication  x  Co-Authorship Statement  xi  CHAPTER 1: INTRODUCTION  1  1.1  History  1  1.2  HD clinical features  1  1.3  HD genetics  3  1.4  HD neuropathology  3  1.5 1.5.1 1.5.2 1.5.3 1.5.4  HD mouse models HD chemical models HD fragment protein models HD full-length protein models HD knock-in mouse models  5 6 6 7 8  1.6  Serial Analysis of Gene Expression (SAGE) and its use in transcriptome studies  8  1.7 Transcriptional dysregulation in HD 1.7.1 Global transcription 1.7.2 Striatal-enriched systems  10 10 11  1.8  Hypothesis and research objectives  11  1.9  Bibliography  12  CHAPTER 2: IDENTIFICATION OF THIRTY-FOUR NOVEL STRIATAL TRANSCRIPTS USING SAGE  iv  18  2.1  Introduction  .18  2.2 2.2.1 2.2.2 2.2.3 2.2.4  Materials and Methods SAGE data analysis In situ hybridization database Quantitative real-time PCR Statistical analysis  18 18 19 19 20  2.3 Results 2.3.1 Identification of striatal-enriched SAGE tags 2.3.2 Identification of thirty-four novel candidate striatal-enriched transcripts through subsequent filtration 2.3.3 Computational characterization and mapping of the thirty-four candidate striatal transcripts 2.3.4 Analysis of temporal expression patterns of the thirty-four novel candidate striatal markers and positive control  21 22  2.4  Discussion  35  2.5  Bibliography  41  24 30 31  CHAPTER 3: EXPRESSION ANALYSIS OF NOVEL CANDIDATE STRIATAL TRANSCRIPTS IN A MOUSE MODEL OF HD AND HUMAN HD BRAIN  42  3.1  Introduction  42  3.2 3.2.1 3.2.2 3.2.3 3.2.4  Materials and methods Animals Human post-mortem caudate and putamen samples Quantitative real-time PCR Statistical analysis  42 42 43 43 45  3.3 Results 3.3.1 Analysis of the candidate transcripts in the YAC 128 mouse model of HD 3.3.2 Analysis of striatal gene expression changes observed in YAC 128 in the human HD caudate  45 45  3.4  Discussion  52  3.5  Bibliography  56  CHAPTER 4: SUMMARY AND CONCLUSION  58  4.1 Summary and significance 4.1.1 Potential pitfalls  58 61  4.2 Future directions: Further computational and biochemical analysis of candidate striatal enriched markers and their implications in HD 62 V  4.2.1 Introduction 4.2.2 Detailed characterization of novel candidate striatal-enriched transcripts 4.2.3 Study of expression changes of candidate striatal-enriched genes in more detail  .62 63 65  4.3  Expected outcomes  66  4.4  Bibliography  68  vi  LIST OF TABLES Table 2.1: Mouse primer sequences used in qRT-PCR analyses  21  Table 2.2: List of positive controls obtained from the SAGE analysis  26  Table 2.3: Final list of novel candidate striatal-enriched transcripts obtained using SAGE  29  Table 2.4: Temporal expression patterns of positive controls and candidate transcripts  33  Table 2.5: Striatal-enriched genes not selected through our SAGE analysis  37  Table 3.1: Caudate/putamen samples used from the CMMT brain bank  43  Table 3.2: Human primer sequences used in qRT-PCR analyses  45  vii  LIST OF FIGURES Figure 1.1: SAGE procedure  .9  Figure 2.1: Step by step selection algorithm used to identify novel striatal-enriched transcripts  23  Figure 2.2: Examples of qRT-PCR results to validate SAGE data  27  Figure 2.3: Temporal expression patterns in different splice-variants of two candidate striatal-enriched genes  34  Figure 2.4: SAGE and the discovery of unannotated transcripts  39  Figure 3.1: Transcriptional changes of the novel candidate striatal-enriched genes and two positive controls in the striatum of the YAC128 mouse model of HD  46  Figure 3.2: Alteration of striatal-enriched transcripts in post-mortem caudate of HD subjects and controls  51  viii  ACKNOWLEDGEMENTS  First of all, I would like to give many thanks to my supervisor, Dr. Blair Leavitt, for all his selfless support, continuous encouragement, and wonderful mentorship. To me, you are not only a role-model in scientific pursuits, but also a man of balance in whose footsteps I would like to follow with the hope of one day serving both as a scientist and a dutiful family-person.  My sincere gratitude also goes to the members of my supervisory committee, Drs. Simpson and Lefebvre, for providing valuable guidance on this project. Our discussions were instrumental in expanding my horizons and in developing my ability to view various scientific works (and most importantly, my own work) with constructive criticism.  I would also like to extend my heartfelt thanks to all members of the Leavitt lab for their support and friendship within the past couple of years. Jenny, Kristina, Ge, Austin, Tern, Kevin, Angela, Laura, and Scott: you all created an environment ideal for to the pursuit of great research. Thank you! I also like to thank Cheryl Bishop, the Graduate Secretary at the Dept of Medical Genetics, for her help and friendship.  Finally, I would like to thank all my friends and family, in particular, my parents, Rokhsareh and Saeed, and my little sister, Tarlan. The three of you mean the world to me and I am where I am because of what you have done. I am so blessed to have you.  ix  x  s3uaJvdIim oJ.  CO-AUTHORSHIP  In January 2006, I started my graduate studies as a MSc. candidate in the Department of Medical Genetics at the University of British Columbia. Since then, approximately 85% of my time has been dedicated to planning, experimental design, conducting experiments, and communicating with collaborators in the Mouse Atlas of Gene Expression project as well as the BC Genome Science Centre. My work inside the laboratory consisted of two sections: bioinformatic and computational analysis (using different databases and software applications) as well as the development of new techniques in the wet lab. I was fully responsible for performing the majority of experimental procedures, from tissue preparation to molecular work, including troubleshooting at all stages. Additionally, I am fully in charge of preparation of this thesis draft and the future manuscripts resulting from this project. Through this period, Scott Neal helped through experimental designs and Ge Lu performed all the animal surgeries and dissections. Finally, this entire project has been done under Dr. Blair Leavitt’ s constant supervision and his continual advice.  xi  CHAPTER 1: INTRODUCTION Huntington disease (HD) is an autosomal dominant neurodegenerative disorder caused by a CAG repeat expansion in the HTT gene resulting in expression of mutant huntingtin protein (HTT) with an expanded polyglutamine region. The prevalence of HD is estimated as almost 1 in 10,000 in populations of European descent and it is reduced up to 10 fold in individuals of Asian or African ancestry. Patients with HD develop symptoms of motor, cognitive and neuropsychiatric disturbances which eventually lead to complete disability and death approximately 18-25 years following the onset of the disease. HD is characterized by selective neuronal loss which occurs predominantly in the caudate nucleus and putamen (striatum). The underlying cause of the selective neuronal loss in HD is not currently understood. Consequently, no effective treatment or cure exists for HD. 1.1 HISTORY HD was first described by the 21-year-old physician George Huntington, in 1872 in an article entitled “On Chorea” in The Medical and Surgical Reporter of Philadelphia. “There seems to exist a hidden power, something that is playing tricks and keeping the poor victim in a continual jigger as long as he remains awake”, said George Huntington of the choreic movement. Furthermore, he accurately defined the autosomal inheritance of the disorder and explained the association of chorea with psychiatric disease (1). One hundred and eleven years later, in 1983, the genetic defect was mapped to Chromosome 4.pl 6.3 (2) and 10 years later, the HD Collaborative Research Group found the expansion mutation in the gene 1T15 (interesting transcript 15) to be the cause for HD (3). 1.2 CLINICAL FEATURES OF HUNTINGTON DISEASE Classic signs and symptoms of HD can be divided into three general categories: motor dysfunction, cognitive impairment, and neuropsychiatric disturbance. The Greek word for a kind of dance, chorea is the main feature of motor dysfunction in HD. It involves involuntary 1  movements of face, limbs, or trunk which is seen in early phases of the disease. The other type of involuntary movement in RD patients is dystonia which results in constant twisting and repetitive movements. Motor disorder in HD also consists of abnormal voluntary movements such as bradykinesia (slowness in the execution of the movement), rigidity, dysphagia (difficulty in swallowing), dysarthria (difficulty in speaking), gait problems, and abnormal eye movements (4). Cognitive impairment in HD begins with subtle slowing of intellectual processes, loss of inhibition of action, and reduced mental flexibility which can manifest prior to the onset of motor symptoms. However, many studies have failed to show strong and consistent cognitive changes in most cases of presymptomatic HD (5). Cognitive deficits exacerbate as the disease progresses through motor onset and manifest as “subcortical dementia” which leads to learning and memory disturbances (6, 7), difficulty with complex intellectual tasks such as strategy generation and problem solving (8). Neuropsychiatric disturbances have been reported in cases of presymptomatic RD (9). However, after performing extensive neuropsychologic evaluations, some studies found no significant difference between genotype positive and genotype negative individuals suggesting that neuropsychiatric changes, like cognitive changes, may be subtle and variable early on. Psychological disturbances become more pronounced as the disease progresses. Consistent with original observation by George Huntington who described “a tendency to insanity and suicide” in lTD patients (1), signs and symptoms of depression, apathy, feelings of suicide, and anxiety are common in symptomatic HD patients (10). Additionally, patients with HD have been reported to exhibit psychosis, paranoia, and signs and symptoms of obsessive compulsive disorder. Finally, weight loss due to altered metabolic state (11, 12), sleep and circadian rhythm disorders (13), and testicular degeneration (14) have also been reported in HD. 2  1.3 GENETICS OF HUNTINGTON DISEASE HD is caused by a CAG repeat expansion in the first exon of the HTT gene, originally called 1T15. 1T15 is 210 kb long and is located on the short arm of human’s Chromosome 4 at position  4pl 6.3. Due to alternative polyadenylation, two different-sized transcripts are formed; the larger transcript is dominant in the brain while the smaller transcript predominates elsewhere (15). Both messages are predicted to encode a 350 kDa protein product which is expressed ubiquitously throughout the body with higher expression levels in the brain and testis (16). The expression of the mutant gene results in the formation of a protein product with an abnormally long polyglutamine tract, leading to a dominant toxic gain of function of the protein. However, a role for loss of normal HTT function in HD pathogenesis remains a possibility. HD is developed when expanded HD alleles contain greater than 35 CAG repeats. The size of the repeat is inversely correlated with the age of onset (17) and its length dynamically changes during somatic development as well as parent to child transmission. This instability is intensified in larger CAG repeat sizes and paternal transmission of the expanded allele results in expansion rather than contraction of the repeat tract (18, 19). This leads to a phenomenon called anticipation which is defined as decreasing age of onset, or increasing severity of disease in successive generations (20, 21). 1.4 NEUROPATHOLOGY IN HUNTINGTON DISEASE An important hallmark of HD is the selective neuronal degeneration in the caudate and putamen (together known as the striatum) which can be characterized by striatal atrophy, neuronal loss as well as selective aggregate formation in this region of the brain. It has also been shown that the severity of striatal pathology is correlated with the degree of motor and cognitive impairments (22, 23) suggesting a central role for striatal degeneration in HD. Interestingly, this selective degeneration occurs despite the fact that mutant HTT protein is expressed widely throughout the brain and body of HD patients. 3  Magnetic resonance imaging (MRI) reveals that atrophy in the striatum of the HD patients begins many years prior to diagnosable HD (24). In early-mid stages of the disease, striatal volume loss has been measured as 53% compared with controls using MRI or post-mortem volume replacement (25, 26). Volume loss in other brain regions such as globus pallidus and cortex is not as severe and has been estimated as 41% and 23% respectively (25, 26). No other significant volume loss has been reported in the brain of HD patients (26). At the cellular level, neuronal loss occurs predominantly in the striatum early on during the course of HD. This phenomenon has been reported as 90% loss of the total number of cells in the striatum (27) and it selectively affects the main population of striatal neurons, the GABAergic medium spiny neurons (MSNs) (28). Within GABAergic MSNs, the loss of enkephalinergic neurons occurs early on and is followed by substance P neurons in later stages of the disease (29, 30). This is supported by further studies that showed decreases in mRNA for preproenkephalin in striatal MSNs in early stages of HD, and only in later stages of illness there were reductions in substance P mRNA levels (31, 32). The other major population of striatal neurons, the aspiny cholinergic interneurons, remains relatively intact through the course of the disease (29, 33, 34). Interestingly, some studies suggest that striatal pathology in HD would manifest as neuronal dysfunction before the well-documented neuronal death in this region (35). This indicates that early symptoms seen in the early stages of the disease in the absence of obvious neuropathology (e.g. early manifestation of chorea) may result from neuronal dysfunction rather than neuronal loss (23). Additionally, there has been indication of inflammation including astrocytosis, microgliosis, and complement activation in the striatum of HD patients (36). Striatal neurons however, are not the only brain cells affected in HD. At later stages of the disease, neuronal loss manifests in the pyramidal projection neurons in layers V and VI of cerebral cortex (37) and the CAl region of hippocampus (38). Interestingly, expressing mutant 4  HTT only in pyramidal cortical neurons of mice did not create neurologic phenotypes similar to models where mutant HTT is expressed ubiquitously leading to the idea that inter communication between brain regions is crucial in HD pathogenesis (39). As HD progresses, neuronal degeneration generalize and include other brain regions such as the globus pallidus, hypothalamus, thalamus, and substantia nigra (28). This generalized pattern of degeneration in later stages of the disease resembles patterns seen in the juvenile HD brain (40). Formation of nuclear and cytoplasmic aggregates is another feature of HD brain which was first noticed in a mouse model of HD (41) and was later found in the striatum and cortex of post mortem HD brains (42). Interestingly, in cases of juvenile HD, aggregates are more wide-spread and are present at earlier stages than in adult onset HD (42). A paradox exists however, on how these aggregates are involved in the pathogenesis of HD; it is not known whether they play a causal role in the HD pathology or whether they are simply the results of defense mechanism of neurons against death. 1.5 MOUSE MODELS OF HUNTINGTON DISEASE Since there are no naturally occurring animal models of HD, a great deal of effort has been put into engineering appropriate animal models of HD. An ideal animal model of HD mimics the main features of the human disease with respect to etiology, symptomatology, and pathophysiology. Additionally, in order to be used in testing drug efficacies in preclinical therapeutic trials, an animal model must exhibit quantifiable, biologically relevant differences from WT animals. Mus musculus has been the species of choice for generating models of HD since it reproduces rapidly, produces large litters, has a reasonably short lifespan, and takes up little space. Moreover, using inbred strains allows a better understanding of the specific manipulation by ruling out the involvement of genetic variations. Four general categories of HD mouse models have been generated thus far: i) chemical models generated by the administration of specific neurotoxins, ii) fragment transgenic models 5  generated by the insertion of an N-terminal fragment of human HTT gene containing the expanded CAG repeat into mouse genome iii) full-length transgenic models generated by the insertion of the human full-length mutated HIT gene into the mouse genome, and iv) knock-in transgenic models generated by the selective introduction of CAG expansion into mouse Hdh gene. Although species differences complicate the exact phenotype comparisons that can be made, genetic HD mice overall recapitulate cognitive failure, motor dysfunction, and striatal neurodegeneration as seen in human HD patients (43) 1.5.1 Chemical models of HD Chemical HD models were widely used prior to the discovery of the HIT gene. These models were generated by the administration of neurotoxins into rodent striatum. These chemicals ranged from excitotoxic neurotoxins such as kainic acid (44, 45) and quinolinic acid (46) to inhibitors of mitochondrial function such as 3-nitroproprionic acid and malonate (47). Instrastriatal injection of these toxins leads to neurodegeneration and in some cases behavioural changes that are similar to HD. However, the accelerated nature of pathogenesis in these models prevents them from being appropriate models to study the process of neuronal damage in HD. Overall, despite the discovery of the HIT gene and subsequent development of HIT gene transgenic models, these chemical models prompted considerable research into specific pathophysiological mechanisms (i.e., mitochondrial dysfunction, NMDA receptor mediated excitoxicity) in HIT gene models under investigation today (43). 1.5.2 Fragment models of lID Fragment RD transgenic mouse models were created by the insertion of an N-terminal fragment of human HiT gene containing the expanded CAG repeat into mouse genome. The best characterized of these models are the R612 and N 171-82Q models which are frequently used in studies investigating different aspects of the disease including pre-clinical drug trials. The R6/2 mouse model contains HD exon 1 carrying a (CAG) 130 expansion and —1kb of the 6  promoter region (48). The N171-82Q mouse was created with a slightly longer N-terminal HTT gene fragment than the R6 line containing 82 CAG repeats (49). Both R612 and N171-82Q develop a robust and early phenotype which includes behavioural abnormalities, intranuclear aggregates, hypoactivity, weight reduction and a non-selective degeneration in the brain (41, 50-  52). Fragment models however, fail to display some of important HD-specific phenotypes such as reduction in brain size and selective striatal degeneration which occurs in human HD (48, 53, 54). 1.5.3 Full-length models of HD Full-length models have been created using two different strategies: The use of a yeast artificial chromosome (YAC) containing the entire region of the full length human HTT gene with 18, 46, 72 and 128 polyglutamine repeats as well as all regulatory elements (-P25 kb of upstream regulatory sequence) (55, 56), and the use of a bacterial artificial chromosome (BAC) expressing full-length human mutant HTT with 97 glutamine repeats under the control of endogenous HIT regulatory machinery (57). YAC 128 is the best-characterized full-length mouse model and shows a spectrum of measurable behavioral, cognitive, motor, and neuropatholgical changes amenable to its use in therapeutic testing. Behavioral changes in this mouse model occur first at 3 months of age evident as hyperactivity and progression to hypoactivity by 12 months. Motor deficit is evident on the rotarod beginning at 6 months of age and progressively worsening with age (56, 58). Neurodegeneration of specific brain regions also develops in the mice with striatal atrophy beginning at 9 months of age and cortical atrophy arising by 12 months (56). BACHD mice exhibit progressive motor deficits starting at 2 months and selective neuropathology occurs at 12 months in striatum and cortex (57). In general, HD full-length models display a consistent phenotype including motor dysfunction and selective neuronal loss demonstrating the specificity of placing the CAG expansion within the context of the full-length human protein (43). 7  1.5.4 Knock-in models of HD Knock-in models of HD are considered the most genetically accurate mouse model of HD existing in either the heterozygous or homozygous state. These models exhibit modest and inconsistent behavioural phenotypes, and neuropathological changes are almost absent. This limits their utility in therapeutic trials for I{D. The knock-in model however, has provided valuable evidence of the behavioral and neuropathological differences surrounding homozygosity and heterozygosity of the mutant HTT gene. Additionally, knock-in mice with larger CAG tracts, show a more severe phenotype and has proven very useful for the investigation of the early pathogenic events in HD (59-62). 1.6  SERIAL  ANALYSIS  OF  GENE  EXPRESSION AND  ITS  USE  IN  TRANSCRIPTOME STUDIES Serial analysis of gene expression (SAGE) is a high throughput technique that provides absolute measures of gene expression based on sequencing of short mRNA-derived fragments, or SAGE tags. The length of a SAGE tag is either 10 (short SAGE tag) or 17 (long SAGE tag) base pairs following a known restriction site (63) This technique was first described in 1995 (64) and has been used for a large number of studies of normal and diseased tissues, cell lines, and model organisms. In summary, SAGE involves extraction of SAGE tags from polyadenylated RNA by conversion to cDNA followed by a series of restriction digestion. Tags are then PCR amplified, concatenated, and DNA sequenced. Figure 1.1 depicts the general SAGE procedure. The frequency of SAGE tags in the final sequences should be directly proportional to the abundance of their parents mRNA molecule (65). The ability of SAGE to determine transcript abundance is dependent on the depth to which the library is sequenced, which can range from 20,000 tags to 100,000 tags from a single RNA sample (65). SAGE tags then undergo the process of tag-to-gene mapping to determine the genes they represent. It is worth noting that LongSAGE tags have high specificity in gene mapping compared to ShortSAGE tags (63). 8  SAGE is a relatively unbiased method of large-scale gene expression profiling as, unlike microarray methods, it does not require prior knowledge of the genes expressed. Thus, it has the potential to identify novel genes  (65).  Additionally, because of the digital nature of SAGE data,  it can be easily shared among investigators and compared across different experiments  and  tissues (66, 67). However, the disadvantages of SAGE are that the technique is expensive and labour intensive, and prone to sequencing errors (68). AAAAA  4,  Reverse transcription AAAAA TITFT  4,  CAGT GTAC  Cut with anchoring enzyme AAAAA TTTTT  4, pilmer  Tagging enzyme site  Add linker sequence AAAAA TTTFT  CAGT GTAC 1  Linker sequence  Cut with tagging enzyme CAGT GTAC___________  Tagging enzyme site  4, Tagging enzyme site  Blunt-end ligate to form ditags and PCR-amplify ditags  ‘—.--—-—-  SAGE tag CATGL  ragging enzyme  GTAC SAGE tag  SAGE tag  CATG GTAC SAGE tag  Site  Cut with anchoring enzyme to isolate ditags  SAGE tag  Concatenate and sequence CATG GTAC  CATG  V  CATG  CATG  Frequency?  Figure 1.1 SAGE procedure.  For more detailed procedure, see Velculescue  9  et al., 1995  1.7 TRANSCRIPTIONAL DYSREGULATION IN HUNTINGTON DISEASE The hallmark neuropathologic feature of HD is early neuronal loss in the caudate and putamen (striatum). Interestingly, this selective degeneration in the striatum occurs depite the fact that the mutant HTT protein is expressed ubiquitously throughout the brain and body of HD patients. Consequently, this finding has led to many studies investigating the unique physiology of the striatum compared with other brain regions. Increasing evidence suggests that transcriptional dysregulation may be an important pathogenic mechanism in HD (69-71). There have been several proposed mechanisms describing how mutant HTT protein disrupts transcriptional processes in neuronal cells. First, it has been suggested that mutant HTT interacts with ubiquitous transcription factors such as specificity protein 1 (Spi) (72, 73), the nuclear receptor co-repressor (N-CoR) (74) CREB-binding protein (CBP) (75), TATA-box binding protein (TBP) (76), TAFII13O (77) Sin3A (74), and p13 (78) via soluble or insoluble complexes. The mutant HTF has also been shown to disrupt the members of the core transcriptional machinery such as the pre-initiation complex (79), as well as to interfere with acetylation and deacetylation states of histones (80) resulting in repression of general transcription (79-81). Finally, intranuclear inclusions are highly suspected to alter general gene expression by decreasing association of transcription factors to DNA binding sites (82). These proposed mechanisms are not mutually exclusive and may have synergistic roles in disrupting transcription. 1.7.1 Global transcriptional changes Many studies have been pursued to examine gene expression changes in HD. Comparative transcriptomics have been performed in fragment models as well as full-length HTT models of HD (83-85). Recently, microarray analyses were carried out on brain samples from human subjects with HD (86, 87). As a result, a large number of genes were found to be altered in mice (1.2%) and human (2 1%) (88). Although these studies have been performed in striatum, many of 10  the genes with altered levels of expression show widespread patterns of expression throughout the brain. These changes can potentially correspond to pathology in other brain regions, clinical heterogeneity in HD, and/or different regulatory mechanisms in the striatum compared to other brain regions. Results from these studies have also demonstrated that mutant HTT directly or indirectly can reduce the expression of a distinct set of genes involved in signaling pathways known to be critical to striatal neuron function (85, 86) 1.7.2 Striatal-enriched systems Although insightful, results from global transcriptional analyses do not establish how only specific neuronal populations are affected in HD. The relative abundance of genes in different brain regions can provide clues to this selective vulnerability. Good examples are “striatal enriched” genes which are associated with several biological processes previously implicated in HD. These processes encompasses a wide variety of functional groups such as transcription factors, and genes involved in calcium homeostasis and G-protein signaling (88). The exceptionally high percentage of striatal-enriched genes whose expression levels are altered in HD compared to other genes expressed in the striatum strongly suggests that striatal-enriched genes are functionally relevant to HD pathogenesis (88). 1.8 HYPOTHESIS AND RESEARCH OBJECTIVES In this study, I hypothesize that genes that are transcribed specifically and abundantly in the striatum play crucial roles in the physiology of this region and may underlie the selective degeneration in HD. Identification of these genes and their expression patterns in both normal and HD brains will provide insight into the pathogenesis of the disease. 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Kuhn, A., Goldstein, D.R., Hodges, A., Strand, A.D., Sengstag, T., Kooperberg, C., Becanovic, K., Pouladi, M.A., Sathasivam, K., Cha, J.H. et at. (2007) Mutant huntingtin’s effects on striatal gene expression in mice recapitulate changes observed in human Huntington disease brain and do not differ with mutant huntingtin length or wildtype huntingtin dosage. Human molecular genetics, 16, 1845-61. Thomas, E.A. (2006) Striatal specificity of gene expression dysregulation in Huntington disease. Journal of neuroscience research, 84, 115 1-64.  17  CHAPTER 21: IDENTIFICATION OF THIRTY-FOUR NOVEL STRIATAL ENRICHED TRANSCRIPTS USING SAGE 2.1 INTRODUCTION Because disease is the result of the normal state going awry, a detailed characterization of the normal state is essential (1). In this project, we used data from the SAGE dataset of the normal mouse brain which is currently available on the Mouse Atlas of Gene Expression Project database (http://www.mouseatlas.org/) to compare striatum with other brain regions. The Mouse Atlas of Gene Expression Project was established at the BC Genome Science Centre (personal communication E.M Simpson) in 2005 (1) and since then, it has generated a quantitative and comprehensive atlas of gene expression in mouse development. The scope of the project encompasses multiple stages of development, from the single cell zygote to the adult, and includes an extensive collection of over 200 carefully micro-dissected tissues including the —  65 brain libraries (consisting of 34 brain regions) used in this project (1). As expected, SAGE  libraries available in this database contain tags that are representative of the relative abundance of specific mRNA molecules within a given tissue in the wild-type mouse. The brain libraries were created using the LongSAGE protocol, so they ensure higher specificity than shortSAGE protocol during tag-to-gene mapping (2).  2.2 MATERIALS AND METHODS 2.2.1 SAGE data analysis SAGE data analysis: SAGE libraries were generated by the Mouse Atlas of Gene Expression Project (www.mouseatlas.org). The DiscoverySpace software application (http://www.bcgsc.calbioinfo/software/discoveryspace/) was used to compare the striatum library to 18 other brain region libraries. These libraries were compared to striatum library one  A version of this chapter will be submitted for publication. Mazarei. G. Neal, S.J., Lu, G., Leavitt, B.R. Identification of Novel Striatal-Enriched Transcripts Using Serial Analysis of Gene Expression.  18  at a time after tag counts in each library were normalized to that of the striatum library. A “p value score” for each striatal tag-sequence was obtained by calculating the sum of (1- p-values) for that tag in all pairwise comparisons (p-values obtained using Audic Claverie statistics): P-value score for tag x *  (1- p-value)  t  IF striatum count> any other brain region count then P IF striatum count < any other brain region count then P  = =  P -P  +  t The formula has been generated by Pavle Vrljicak in BCCA, Vancouver, BC (manuscript submitted, 2008)  Tag to gene mapping was performed using the mouse Refseq, MGC, and Ensembi databases available on DiscoverySpace. Tag ‘position’ was determined as a number whose figure represented the location of the Nialli site relative to the polyA tail and whose sign represented the directionality of the tag relative to the gene to which it was mapped. Candidate striatal specific tags were selected based on the following selection criteria: 1) more abundant in striatum, by at least one tag count, compared to 18 other brain libraries at 12 weeks post birth, 2) p-value score ratio  13.005 at 12 weeks post birth where -16 p-value score  +17, and 3) mean  2.5 when striatum was compared to 18 other libraries at all available time-points.  2.2.2 In situ hybridization database In order to be more stringent in selecting SAGE tags, I used the Allen Brain Atlas (AIBS) (http://www.brainaUas.org/AIBS/) in situ hybridization images to obtain a visual assessment of relative abundance of the transcript corresponding to the SAGE tags. Obviously, only SAGE tags mapping to known genes could be filtered through this process. A demonstrated striatal enriched/specific expression pattern in the AIRS in situ hybridization database was considered a necessary criterion in our analysis. SAGE tags corresponding to unknown regions were not filtered out at this stage.  2.2.3 Quantitative real-time PCR RNA from 4 brain regions (striatum, cortex, cerebellum, hippocampus) as well as liver and 19  muscle was prepared from three 3-month-old mice using the RNeasy kit (Qiagen). Primers were designed using Primer3 (http://frodo.wi .mit.edu/cgi-bin/primer3/primer3 www .c giT) and spanned introns where possible. Amplicons were between 110 and 150 bp for efficient amplification. Primer efficiencies were determined using a dilution series of adult striatum cDNA. Only primers pairs with an efficiency greater than 0.98 were used in subsequent analyses. The primers were also designed to encompass the SAGE tag (See Table 2.1 for the list of primers). An ABI 7000 real-time PCR system (Applied Biosystems) and SYBR® Green Master mix (Qiagen) were used for validations of the SAGE data and further filtering the number of SAGE tags. cDNAs were obtained by the reverse transcription (RT) of 1 ig of total RNA from newly dissected brain regions using the Quantitect RT kit (Qiagen). All reactions were carried out in duplicate using 2 ,ul of generated cDNA in each reaction. For validation of the SAGE data, absolute quantity of the targets in each sample was calculated based on the standard curve method and normalized to endogenous control, fl-actin.  20  CD  CD  -  CD  CD  CD CD  CD  CD 0  CD  CD  CD  )-t  CD  -4  -  CD  cM  0  cM  0  —.  c,  ‘V  .  CD  —.  CD  CD  -4  cM  CD CD  CD  cL  tIQ  CD CD  CD  CD  C  z  ç  pz c<  III.  CD  -t  cM  CD  •c  11)  CD CD  CD -t CD  •  -  C  CD  -  CD  CD  o  CD  tr  -  •c  0  CD  -  -  cn  .  -  ‘V  cF  .  .  -  D  •  •  I  —  2.3 RESULTS In this part of the project, our aim was to select for striatal enriched transcripts that were not previously reported in two publications that by far present the most complete lists of striatal enriched transcripts (3, 4). Of these two studies, Desplat et at. (3) generated a list containing sequences previously published from their laboratory, genes previously described in the literature, and some unpublished sequences that they identified using the method TOGA (5), while de Chaldee et al. generated their list of striatal-enriched transcripts using shortSAGE (4). From these two studies, I adopted a list of previously-known striatal-enriched genes (positive controls) based on which I assessed our SAGE analysis. 2.3.1 Identification of striatal-enriched SAGE tags In order to identify novel striatal-enriched transcripts, I compared the striatum SAGE library with eighteen other brain libraries. A set of stringent selection criteria (see MATERIALS AND METHODS for details) that result in a manageable list of novel gene candidates with the largest number of previously reported striatal-enriched genes, was developed to select for novel candidate transcripts. Figure 2.1 depicts an algorithm showing these selection criteria.  22  [Initial striatal tag sequences (22.000)  1  More abundant in striatum, by at least one tag count, compared to 18 other brain libraries at 12 weeks post birth.  680 tag sequences (excluding singletons)  4,  j  p-value score  13.005 at 12 weeks post birth  268 tag sequences  r  23 tag sequences  Mean ratio 2.5 when striatum libraries were compared to 18 other brain libraries at four time-points.  187 tag sequences  I  L mapping to unknown regions J ‘s  .  4”  10 tag  ‘V  L  (mitochondrial  164 tag sequences mapping to known genes  Demonstrated striatal-enriched expression pattern in ABA for known genes.  8 tag sequencfl  ag sequences  L_seauences  >1  J  82 tag seuencej(  tag sequences  I  ‘s  II  50 tag sequences Positive Controls: 34 genes j  “I”  40 tag sequences  qRT-PCR lidation  L__________________  -.  32 tag sequences ‘—  Cand’dates  .  23 known genes 4 uncharacterized genes 2 novel splice variants of known oenes  Figure 2.1 Step by step selection algorithm used to identify novel striatal-enriched transcripts. I defined “striatal-enriched” transcripts as transcripts with dominant patterns of expression in striatum compared to 18 other brain regions. Accordingly, I designed our selection criteria to select for the greatest number of previously-known striatal-enriched genes.  Striatal enrichment/specificity was determined based on relative expression of striatal transcripts when compared among all nineteen libraries. First comparison was made in the adult brain (at 48 days postnatal (dpn)). Out of  -‘  22 000 total striatal tag-sequences (potential  transcripts) in the adult striatum library, 6672 tag-sequences were more abundant in the striatum than other brain libraries by at least one absolute tag count (raw transcript abundance in the mRNA population of  100 000). This allowed elimination of all tag-sequences that showed  higher abundance in any other brain region than in striatum. Of these tags, 6044 tags were found at a single count and were exclusive to the striatum (ie. one tag count in the striatum and zero elsewhere). These ‘singletons’ may result from sequencing and PCR errors or may well represent bona fide transcripts in the tissue (1). However, an un-biased and careful investigation 23  of such great number of tag-sequences and their corresponding transcripts, despite their potential importance, is beyond the resources of this project. For this reason, I eliminated these tags from our further analysis to reduce the level of complexity they introduce. Next, a p-value score representing the level of significance of striatal abundance for each tag relative to other brain regions was used to narrow down the remaining six hundred and twentyeight non-singleton tags to two hundred and sixty-eight tag-sequences. The abundance of these tag-sequences was also compared among all nineteen libraries as a mean ratio offive different time-points (pre-striatum, Theiler stage 25, and at dpns 7, 35 and 84) over the rest of brain libraries, where available. This was to ensure the tag-sequence was preferentially expressed in striatum throughout development. Through this comparison, no previously reported striatal enriched genes were eliminated and fifty eight tag-sequences were selected to be preferentially expressed in the striatum. 2.3.2 Identification of thirty-four novel candidate striatal transcripts through subsequent filtration SAGE is a powerful technique that provides absolute measures of gene expression based on sequencing of mRNA derived fragments. Brain SAGE libraries used in this project have been constructed from an individual mouse for every time-point. One should keep this in mind when performing comparison studies and question the consistency of observed tag frequencies when more than one animal is studied. Libraries constructed from different individual mice, or mice kept in different laboratories show correlation of only 0.66-0.78, or 0.5-0.58 for counts of count <100, even when the same inbred strain and tissue is used (6). To further ensure striatal enrichment of the selected tags, two subsequent analyses were performed on the one hundred and eighty seven remaining tags. First, 82 tag-sequences representing known transcripts but  lacking demonstrated striatal-enriched expression patterns in the AIRS in situ hybridization images were eliminated from the list, resulting in ninety tag-sequences. Of these tags, fifteen 24  tag-sequences mapped to the mitochondrial genome or could not be mapped to a single region in the mouse genome using any of the databases (ambiguous hits) such as Refseq, Mammalian Gene Collection (MGC), and Ensembl. Of the remaining ninety unambiguous tag-sequences, fifty mapped to thirty-four previously reported striatal-enriched genes using these transcript databases (Table 2.2). These thirty-four genes represented 28/54 (-‘52%) of the Desplats genes (3) and 18/28 (-‘64%) of the de Chaldee genes (4) which confirmed the validity of our selection criteria in the identification of the striatal-enriched transcripts.  25  (I,  0  CD  CD  0  CD  CD  CD  0  D)  -t  CD  OQ  CM  CD  0  CD  0  CD CD  CD  -t  CD  4-)  0  -t  CD  —4-  *  4:-)  -  .  —  1-4  C)  CM  I  —  CM  C  •  I-I  —. e  —  D  .  —  —.  ) CD  -.4  CDCD  — d  CD  I.  0  cLcrQ.  z  C C  -•  Cl)  -  CD  4-.  CD  CD 4•+)  C) CD  .  C  r1-1s  CD 1 CD  Second, the differential expression of the remaining forty tag-sequences mapped to unambiguous regions (a single sense position gene, an expressed sequence tag (EST) or an AceView predicted transcript), were examined by qRT-PCR in different tissues of three animals. Out of 40 transcripts, 18 (Ablim2, Actnl, Carl], Cd4, Cyldi, Gpr83, GrinS, Indo, Meis2, Ppp]r9a, Sh2d5, Smpd3, Tbcid8, Tmeml58, tag downstream of A183664] tag downstream of Pde7b) were significantly ,  more abundant in the striatum and 7 transcripts (Me2, Rasl]Odb, Sytl5, Tmod], Co30007]OiRik, tag downstream Neb] gene) showed lower expression in striatum than one or more other regions (using one-way ANOVA). 13 out of 40 transcripts (Apis], Cacna2d3, Gnaoi, Gpr]55, Kcnabi, Phactri, Plxnd], C0300]3GO3Rik, BB360574, tag downstream Tmem]6C) showed a trend illustrating higher striatal expression, the majority of which failed to show a significant difference in transcription levels between striatum and cortex but not other regions which suggest physiological similarities of these two regions. Figure 2.2 illustrates qRT-PCR results for Tmemi58, tag downstream of A1836640, Apis], and Tmodi as examples of above categories. a. 4.5-j .c  *  4  C C  E 3.5 C,) C  1.5  E 0.5  H  I  CTX  HIPP  27  CER  LIVER  MUSCLE  b.  *  1.60E+OO  0 0 C 0  1.40E+OO  C .0 0 0 >  1.20E+OO  0  1.OOE+OO  -  -  40  8.OOE-O1  -  6.OOE-O1 c)  4 0  E  4. OOE-O1 2.OOE-O1  0 0 (0 C 0  O.OOE+OO TR  CIX  HIPP  CIX  HIPP  CER  LIVER  MUSCLE  -2. OOE-O1  C.  6  -  (0 C 0  E  5  0,  C (0  4  C  z  .0 (0 (0  3-  (0  24  z E E  0 I  0—  —  CER  LIVER  MUSCLE  CER  LIVER  MUSCLE  1 1.OOE÷00  d.  9.OOE-O1  8 E  -  8.OOE-O1 7.OOE-O1 H .  6.OOE-O1  -  5.OOE-O1 4OOE-O1  E 4  3.OOE-O1  -  -  2.OOE-O1 H tOOE-O1  -  O.OOE÷OO STh  CIX  HIPP  Figure 2.2 Examples of qRT-PCR results to validate SAGE data. Transcript expressions validated (a, b), not validated (c), and showing a trend (d) using qRT-PCR. Significant differences were determined by a one-way ANOVA analysis using a threshold value of p<O.O . 5 28  C)  -  C,,  C) ‘CD 0 0’  B  0’ C)  BCD  C) 0 0  <0’ CD C. C)  II  11O  0>  >00  —o  -I,  CD —C) 0’ CD  <0z C) C) C) C) 0’ C) 0’- 0’ C) 0’  C),,,  -I  —  CD.-.  B  C -IC)  C)<  C) C) CD.  C,,  00 C)  C)  B  I  H  0’ . C)  >>  e1z z  C)  0 0 C C “ —C  C  0  >  z  >H  H  0)  —I  C  Cl,  C  0  >  z  H>C  >9  > O>O  -  —  —  C00 — D’J C 1. -) .0’. 0000CC 00 -.) ‘ -  —.  —.  —  C C CC —C 0000 tJ — CC 00 00 — tJ LI, 0\ CC UI I.I 00 UI, C CC  zzzzz>’::zz  -  o>>> H>C>>() HH>> HOfl>0 0OH>H> >>H>H> > >O  >-O>C)  >fl>>  HC)->  fl-a HO  >>  b CCJCC1i-  00CCtJ0CC (JD.0’.—JU,CJ1C  00  b—bo  •‘CD  C)  t’) —  00CC1  C).  -  ,  C). ——  —CC--) k) C) (JU,00.0’. C)  ,,  .0’.-1  fl000CD ro C B C,  —  nñ  bbccbo  .  C,,  C)  ---  C)  C)  .) ‘J .)  t.) 3 00  —  ).) L) L t’.) ‘J  —  —  ç.  CC CC  0) 0)  c.  —  zz  H 0 HH 0> H  >H H 0  ‘-  >> -O OH  j..  CCC DJ’J  bbo  ‘.C -  ‘  CD. 0’ CZ .  0’  L1i4) UlCC)00Lfl 00  000C00c.aJCCC(j  C)  \D00  CJ1  IN.) 00 CC ) CC  )  -.) 00  —  00 UI,  0) 0) IN.)  c,  —  -  z  zzz  .  0)  COUIIUI, 0 C.) C C.) 00 —.)--.)  l,  0 I_ I I_ I I C CCC —. 0 t C C C C CC CC CC 00 CC C-.)---J  a-.  H  HHflOH>H  ><><zzzzzz  2  H H  n  On->>OO>-O H(>-fl Hfl(Th H 0 (OHflO>fl> 0OH> HO OH0flH0O  DJ1  t’)  C  fl00>  00  oo>  ———— c.)CCCCCC 000000 00.0’CC 0)CCCCCC  zzzz  HOHH  OH >fln H>flO  0n-> >-H-()O >Th0 (> HO,> OH>  >OH>  CCj):..J—  QC0000 CO\01-11000  (0  BC)  00 0)  —  C  C C C C C.) C C  z  z  OH rH H(-) OH  O  -n 0) OH 00 >0 H00 HO HO  00CC JCC —CC  L1 00  —  B  H  I  C)  CC)  C)  C)  c  CD  00  fli.  CDD  -  c,,DD  C  -  CD  -0’Z:  -D 1 CDCD  DCDD  --  ()  N  C c-  ‘  )  ,—‘  ‘•  -  :,z  -  ——  0’ CD  CD  CD  C  C  IE  CD  0’)  0  0’)  I-..  CD  0  ‘  .  -  0  .  i i  .  CD  -  0CD  .,,  CD0  Y-D•  CD  _c  o::  vu  t’J-—  r  2.3.3 Computational characterization and mapping of the thirty-four candidate striatal enriched SAGE tags Tag-to-gene mapping of the thirty-four novel striatal-enriched SAGE tags revealed that these tags represented twenty-three known genes, two uncharacterized transcripts, and two potential novel splice variants of the previously annotated genes (Table 2.3). Among known genes, cylindromatosis (turban tumor syndrome) gene (Cyld), adaptor protein complex AP- 1, sigma 1 (Apis]), phosphatase and actin regulator 1 (Phactri) and protein phosphatase 1, regulatory (inhibitor) subunit 9A (Pppir9a) were represented by two or more selected SAGE tags implying two or more striatal-enriched splice variants for each of these genes. Transcript databases indicate two splice-variants for Cyld, and three for Phactri. This is consistent with the number of SAGE tags selected for these genes. For Pppir9a, databases indicate three representative transcripts one of which did not get selected using our selection criteria (SAGE tag existed but was filtered out). For Apis] however, we selected two striatal-enriched transcripts, one of which reveals an unreported splice-variant for this gene. Gene ontology (GO) provides controlled vocabulary to describe genes and their corresponding products. Upon using Expression Analysis Systematic Explorer (EASE) which provides statistical methods for discovering enriched biological themes within gene lists (7), I identified a wide range of classification throughout the list of candidate genes where no specific GO classes were overrepresented (Table 2.3). The uncharacterized genes were identified as a Riken transcript (CO300i3GO3Rik), an EST (BB360574), and genomic sequences that may contain potential novel transcriptional units (Table 2.3, tag #29). Tag-sequence # 29 corresponds to a novel transcript on the negative strand of Chromosome 19. To our knowledge, there are no previously reported expression data in this region. These results further indicate our approach was successful in the identification of novel markers of striatal expression using SAGE. 30  SAGE is outstanding for detecting putative new splice variants. This may include novel internal exons as well as novel alternative 3’ untranslated regions (UTRs) (6). As SAGE tags correlate to the 3’ most anchoring enzyme site in a gene, many SAGE tags are expected to be derived from the 3’ UTR which is not consistently included in the gene predictions. Two of the novel striatal-enriched tag-sequences mapped to regions downstream of transmembrane protein 16C (Tmernl6C) and phosphodiesterase 7b (Pde7b) genes where an AceView prediction (8) of alternate splicing for each of these genes also existed. Both of these tag-sequences also showed homology to the human mRNA of the corresponding gene. Similarly, in the list of the previously reported striatal-enriched transcripts (Table 2.2), mapping of SAGE tags to AceView predictions led to identification of potential novel splice-variants for previously-reported striatal-enriched genes such as adenylate cyclase 5 (Adcy5), diacylglycerol kinase, Dgkb, forkhead box P1 (FoxPi), Striatin (Stm), and cyclic AMP-regulated phosphoprotein, 21 (Arpp2l) (Table 2.2). 2.3.4 Analysis of temporal expression patterns of the thirty-four novel candidate striatal enriched markers as well as the thirty-four previously-reported striatal-enriched genes In the central nervous system, in addition to the development that occurs during embryogenesis, a considerable amount of morphological development, cell differentiation, and acquisition of function takes place during postnatal development (9, 10). Therefore, identification of the developmentally regulated transcripts in different brain regions can elucidate molecular mechanisms underlying complex developmental processes. Using SAGE, I analyzed the expression patterns of our novel striatal-enriched transcripts through the course of striatal development. The five SAGE libraries used in this analysis included pre-striatum (Theiler stage (TS) 20), Theiler stage 25, post natal day 7, 35, and 48. This was achieved through comparison of the absolute tag counts for each tag-sequence following normalization of the library sizes. Through this analysis, I noticed all candidate striatal-enriched transcripts as 31  well as previously-reported striatal genes were regulated developmentally. I also identified three general trends of temporal expression for these genes. First, 15 out of the 33 novel striatal candidates and 29 out of 50 previously-reported striatal-enriched genes showed a common pattern where there was a peak in expression at dpn35. This interesting observation can explain the importance of this subset of transcripts in brain hormonal changes at the time of sexual maturation (11). Additionally, 12 candidate transcripts and 9 previously-reported striatal genes showed a successive increase in expression from TS2O to 12 weeks post birth while only 3 candidates and 3 previously-reported striatal genes demonstrated an unchanged level of expression between dpn 7 and 12 weeks post birth. The rest of the candidate markers and previously-reported striatal-enriched transcripts showed “zig zag” patterns where expression level was fluctuated between the five time-points of development. Table 2.4 summarizes candidate transcripts and previously-reported striatal genes categorized under these various developmental expression patterns.  32  Table 2.4 Temporal expression patterns of the previously-reported striatal transcripts and candidate transcripts. peak at dpn35  unchanged between dpn 7 and 12 weeks increase from TS2O to 12 weeks post birth post birth  Fluctuation between the five time-points  Cpne5 (splice-variant B) Rasgrp2 Tad  Ppplrlb (splice-variant A) Arpp2l(splice-variant D) Dgkb (splice-variant C) Pdel Oa (splice-variant D) Adcy5 (splice-variant C) Pppc3a Adora2A Rgs9 A1836640 tag-sequence  Ppplrlb (splice-variant B) St8sia3 (splice-variant B) Tmem9Oa (splice-variant B) Gnal (splice-variant B) Bcll lb Drd3  Smpd3 BB360574 tag-sequence Carll  Gpr155 Actnl Indo Gnaol AblimI Pde7b novel variant Tbcld8 Cyld (splice-variant B) Phactrl (splice-variant C) Ppplr9a (splice-variant A) Kcnabl RasI I Ob  ApI SI (splice-variant A) ApIsI (splice-variant B) Plxndl Meis2  Positive controls: Penkl (splice-variant A) Penk2 (splice-vaiiant B) Arpp2l (splice-variant A) Arpp2l(splice-variant B) Arpp2l(splice-variant C) St8sia3 (splice-variant A) Drdl (splice-variant A) Drdl (splice-variant B) Tmem9Oa (splice-variant A) Dgkb (splice-variant A) Dgkb (splice-variant B) Cpne5 (splice-variant A) PdelOa (splice-variant A) PdelOa (splice-variant B) PdelOa (splice-variant C) Gnal (splice-variant A) Adcy5 (splice-variant A) Adcy5 (splice-variant B) Arppl9 Pcp4lI Spock3 Rapl gap Gpr88 Scn4b Tesc Gng7 Rxrg Rasd2 Pdel b Candidate transcripts: downstream of A1836640 Rgs4 Gpr83 Tmeml6C novel variant Tmem 158 AKO21O75 Sh2d5 Cyld (splice-variant A) Rgs7bp Phactrl (splice-variant A) Phactrl (splice-variant B) Cacna2d3 CO30013GO3Rik Cd4 Grm5  In  expression of tag-sequences corresponding to different  splice  two common patterns of developmental regulation were observed.  similar or  “parallel”  variants of  The  first group  trends of expression where different splice variants of  33  the  the  same gene,  included  same gene were  regulated similarly through the five time-points of striatal development. The second group of genes on the other hand, manifested divergent patterns of expression among their splice variants. Figure 2.3 depicts the temporal expression of the three Phactri and two Apis] different splicevariants. These general observations, once again, confirm that RNA alternate splicing in the striatum, like other regions of the brain, is regulated developmentally and this temporal regulation plays a central role in determining signature characteristics of the striatum through its development( 12).  a. 80  splice-variant A splice-variant B  70  splice-variant C  0  c 60 C  50 e 0  • 40-  z 301 0  20 0.  10 -1  0 p35  P84  34  b. 70  spIice-ariant A spIice-ariant B  60 0  50  -  40 0  0  30  200.  10 H  0 I E12.5 E17.5 P7  P35  P84  Figure 2.3 Temporal expression patterns in different splice-variants of two candidate striatal-enriched genes. Phactri (a) splice-variants A and B show parallel expression patterns while splice-variant C show a divergent pattern compared to the other two. Apis] (b) has two different splice-variants which also show divergent patterns of expression relative to each other.  2.4 DISCUSSION In this part of the study, we report a list of thirty-four novel striatal-enriched transcripts. Through this analysis, I was able to select transcripts that have not been previously reported as striatal-enriched. Through the use of SAGE, I was also able to identify potential novel transcripts as well as novel splice variants of the annotated genes. Identification of novel striatal enriched genes will be useful in better understanding the unique physiology of the striatum and may provide insight into selective degeneration of this region in HD. Although the goal of this project was to select for novel striatal-enriched candidates, keeping track of the previously-known striatal-enriched genes helped us at each selection step. Knowing that the designed criteria also selected for these previously-reported striatal transcripts ensured the validity of our selection in identification of unknown striatal markers. Accordingly, I was 35  able to select for a large percentage of the striatal-enriched genes previously reported in de Chaldee et at. and Desplats et at. The selection criteria in our analysis however, did not select for some of these positive controls. There are four main reasons for this observation: 1). As our initial selection criteria, for SAGE tag to be considered striatal-enriched it had to be more abundant in the striatum than other brain regions at dpn 84 (adult). As an example, retinoic acid receptor beta (Rarb) is highly expressed at post natal days 7 and 37 but not in the adult striatum. Therefore, it did not get selected as a striatal-enriched transcript despite its abundance in this region. 2). I compared and analyzed 19 different brain regions in this study and selected for SAGE tags that are higher in the striatum than eighteen other brain regions. Therefore, SAGE tags that manifest higher or equal abundance in at least one other brain region would get filtered out. Good examples include hippocalcin (Hpca) SAGE tag which despite the large abundance in the striatum, shows a higher expression in CAl and neuronal guanine nucleotide exchange factor (nGEF) SAGE tag that is more abundant in visual cortex than in the striatum. Many of the other previously reported genes are expressed much lower in the striatum than more than one other brain region (e.g. synaptoporin) and as a result, based on our criteria, these genes are not considered striatal-enriched. 3). Transcript abundance may not be high enough to be detected by SAGE or may be at the singleton level which does not get selected using our criteria. A good example is the SAGE tag for Htr6 gene (5-hydroxytryptamine (serotonin) receptor 6) which is present at a single count in the striatum. 4). Genes that are lacking the appropriate anchoring enzyme site (e.g. Nialli) will never appear in SAGE libraries as no tag for these genes will be extracted (6). Similarly, since SAGE relies on a polyadenylated tail for tag extraction, transcripts that are not polyadenylated will not be profiled (6). Dopamine receptor 3 (Drd3) transcript fails to have a valid SAGE tag since it 36  lacks a poly-A tail. However, SAGE tag for this gene could potentially exist in downstream sequences where there is no annotation for this gene as of today. Table 2.4 shows a summary of striatal-enriched genes not selected through our selection criteria.  Table 2.5 Striatal-enriched genes not selected through our SAGE analysis. Each gene is located under the category which potentially explains why the gene did not get selected. Gene is striatally-enriched at a developmental stage other than adult  More abundant in at least one other brain library (after normalizing)  Transcripts detected in the SAGE library as singletons  More abundant in at least one other library in the form of singletons  No valid SAGE tag for the annotated transcript  retinoic acid receptor beta Anaphase promoting complex, subunit 5 Neighbor of Brcal gene 1  B3ghtl BTE binding protein  5-HT6 B cell RAG ass’d protein Mu-opioid receptor  actinincL2 Ephrine A4  5-HT4 receptor dopamine receptor 3  OSBPL-8  Kappa opioid receptor KCNIP2  Delta opioid receptor Drrf Synaptoporin FoxP2 Hippocalcin nGEF Insulin receptor substrate p53 Neurotensin Nolzl (Zfp503) mPPP1RI6B  Therefore, despite being highly abundant in the striatum, many transcripts were filtered out due to above explanations. Similarly, many unknown striatal-enriched transcripts could potentially get filtered out through these processes. In selecting our list of striatal-enriched candidate genes, having performed the statistical filtration, I then used the AIRS database to filter further transcripts. The anatomic gene expression atlas interface in AIBS allows users to view spatial relationship maps based upon the gene expression data computed from  4,376 coronal image series in the mouse brain. I figured  that using AIRS database was necessary since it gave us a more clear understanding of patterns of expression of annotated transcripts. Correlation rate between SAGE and AIRS for candidate transcripts was estimated  50% in our analysis.  37  In a recent cDNA microarray study done by Ghate et al. (2007), the majority of novel striatal genes that came out do not demonstrate clear striatal patterns in their in situ images, which questions their abundance or specificity in this brain region (13). Therefore, using AIBS allowed a more stringent selection which led to a high-confidence list of candidates. However, this database was not a pragmatic source for testing the expression of un-annotated transcripts. Quantitative RT-PCR was used as another tool to further narrow down the candidates list. This was a useful method since unlike SAGE and AIBS datasets, more than one animal (n  =  3)  was tested. This resulted in a more realistic view of a transcript expression pattern within the brain. Moreover, relative expression of all transcripts, including unknown transcribed regions, was also tested using this technique. Using qRT-PCR, out of 40 transcripts, 18 (45%) showed a significantly higher level in striatum and 13 (32.5 %) showed a trend towards striatal enrichment. In a previous study, qRT-PCR analysis of mouse genes found by SAGE showed correlation with SAGE of 0.64 for tags of count 45-75 in a library of 100,000 (14). Lower correlation rate in our analysis could be due to our lower-abundance tags (<45 counts) representing the majority of our candidate transcripts. Due to the same reason, 7 out of 40 (17%) transcripts showed a lower level in striatum than at least one other tissue using qRT-PCR. These data suggest that the larger the abundance of the transcript, the higher the validation rate using techniques such as qRT-PCR. Computational analysis of the candidate striatal-enriched SAGE tags revealed selection of twenty-six known genes, two uncharacterized transcripts, and two potential novel splice variants of the previously annotated genes (Table 2.5). Within these candidate transcripts, Trans membrane protein 158 (TmemlS8), also known as Ras-induced senescence 1, had the highest p value score which was validated by qRT-PCR (Fig. 2.2a). Other transcript in Table 2.3 which showed the highest mean ratio is indoleamine-pyrrole 2,3 dioxygenase (Indo), a gene involved in tryptophan metabolism. This indicates that this transcript is striatal-enriched throughout 38  striatal development which has been tested and validated at 2 time-points using qRT-PCR (data not shown). Computational analysis also led to selecting for two unknown striatal-enriched transcripts one of which (i.e. tag #29 in Table 2.3 or tag downstream A1836640) maps to an Unannotated region of the Chromosome 19 genomic sequence (Fig. 2.4).The striatal enrichment of this transcribed region has also been shown in our study using qRT PCR (Fig. 2.2b). qRT-PCR results (data not shown) also indicate that this novel striatal-enriched tag-sequence may represent an alternative splice variant of the transcriptional unit which also encompasses the SAGE tag located in a highly-conserved EST sequence A1836640, also selected in our analysis (Table 2.2, tag # 1). I considered the latter tag-sequence a positive control transcript since EST A1836640 has been previously described as a striatal-enriched transcribed region in a study published by Desplats et al. (2006) (3). Other details of this chromosomal region are also depicted in Figure 2.4. This, once again, proved the ability of SAGE in finding novel transcriptional units.  5  AceView Predicted gene  olyA signal?  A183664O  SAGE tag #1, Table 2.2  AIBS probe  ,‘/  polyA signal?  SAGE tag #29 Table 2.3  Myorobe  -1.2kb  Figure 2.4 SAGE and the discovery of un-annotated transcripts. Two consecutive SAGE tags correspond to 3’ ends of two transcripts that potentially belong to the same transcriptional unit. These SAGE tags were selected in our analysis to be highly striatal-specific. Two in situ hybridization probes suggest predominant patterns of mRNA expression in the striatum. A CpG island and a high-quality CAGE signal (15) predict the 5’ end of the gene and allow for comprehensive promoter analysis of this potential novel striatal-enriched gene.  39  Finally, using SAGE libraries from the MouseAtlas project allowed temporal expression analysis of the candidate transcripts for five developmental time-points. A study (9) that tested gene expression changes during murine postnatal brain development showed that a small percentage of the genes expressed in the postnatal developing brain show changes in expression during the newborn to adult phase of development. Our data however, suggest that the majority of our candidate transcripts do show such changes, which would suggest the importance of regulation of striatal-enriched transcripts during post-natal development.  40  2.5 BIOBLIOGRAPHY 1.  2.  3.  4.  5.  6.  7. 8. 9. 10.  11.  12. 13.  14.  Siddiqui, A.S., Khattra, J., Delaney, A.D., Zhao, Y., Astell, C., Asano, J., Babakaiff, R., Barber, S., Beland, J., Bohacec, S. et al. (2005) A mouse atlas of gene expression: largescale digital gene-expression profiles from precisely defined developing C57BL/6J mouse tissues and cells. Proceedings of the National Academy of Sciences of the United States ofAmerica, 102, 18485-90. Li, Y.J., Xu, P., Qin, X., Schmechel, D.E., Hulette, C.M., Haines, J.L., Pericak-Vance, M.A. and Gilbert, J.R. (2006) A comparative analysis of the information content in long and short SAGE libraries. BMC bioinformatics, 7, 504. Desplats, P.A., Kass, K.E., Gilmartin, T., Stanwood, G.D., Woodward, E.L., Head, S.R., Sutcliffe, J.G. and Thomas, E.A. (2006) Selective deficits in the expression of striatal enriched mRNAs in Huntington disease. Journal of neurochemistry, 96, 743-57. de Chaldee, M., Gaillard, M.C., Bizat, N., Buhler, J.M., Manzoni, 0., Bockaert, J., Hantraye, P., Brouillet, E. and Elalouf, J.M. (2003) Quantitative assessment of transcriptome differences between brain territories. Genome research, 13, 1646-53. Sutcliffe, J.G., Foye, P.E., Erlander, M.G., Hilbush, B.S., Bodzin, L.J., Durham, J.T. and Hasel, K.W. (2000) TOGA: an automated parsing technology for analyzing expression of nearly all genes. Proceedings of the National Academy of Sciences of the United States ofAmerica, 97, 1976-81. Pleasance, E., Jones, SJM (2005) Evaluation of SAGE Tags for Transcriptome Study. In Wang, S.M. (ed.), SAGE: Current Technologies and Applications. horizon bioscience Norfolk. Hosack, D.A., Dennis, G., Jr., Sherman, B.T., Lane, H.C. and Lempicki, R.A. (2003) Identifying biological themes within lists of genes with EASE. Genome biology, 4, R70. Thierry-Mieg, D. and Thieny-Mieg, J. (2006) AceView: a comprehensive cDNA supported gene and transcripts annotation. Genome biology, 7 Suppi 1, S12 1-14. Clinton, M., Manson, J., McBride, D. and Miele, G. (2000) Gene expression changes during murine postnatal brain development. Genome biology, 1, RESEARCH0005. Akazawa, C., Ishibashi, M., Shimizu, C., Nakanishi, S. and Kageyama, R. (1995) A mammalian helix-loop-helixctor structurally related to the product of Drosophila proneural gene atonal is a positive transcriptional regulator expressed in the developing nervous system. The Journal of biological chemistry, 270, 8730-8. Ellis, P.J., Furlong, R.A., Wilson, A., Morris, S., Carter, D., Oliver, G., Print, C., Burgoyne, P.S., Loveland, K.L. and Affara, N.A. (2004) Modulation of the mouse testis transcriptome during postnatal development and in selected models of male infertility. Molecular human reproduction, 10, 271-81. Grabowski, P.J. and Black, D.L. (2001) Alternative RNA splicing in the nervous system. Progress in neurobiology, 65, 289-308. Ghate, A., Befort, K., Becker, J.A., Filliol, D., Bole-Feysot, C., Demebele, D., Jost, B., Koch, M. and Kieffer, B.L. (2007) Identification of novel stnatal genes by expression profiling in adult mouse brain. Neuroscience, 146, 1182-92. Anisimov, S.V., Tarasov, K.V., Tweedie, D., Stern, M.D., Wobus, A.M. and Boheler, K.R. (2002) SAGE identification of gene transcripts with profiles unique to pluripotent mouse Ri embryonic stem cells. Genomics, 79, 169-76.  41  CHAPTER 32: EXPRESSION ANALYSIS OF NOVEL CANDIDATE STRIATAL TRANSCRIPTS IN A MOUSE MODEL OF HD AND IN HUMAN HD BRAIN 3.1 INTRODUCTION In order to understand cellular changes that occur in HD pathogenesis, it is important to look at gene expression changes in mouse models of HD as well as in post-mortem brain samples from both HD and control subjects. Having tested expression levels of all 34 candidate striatal enriched transcripts in one-year-old YAC 128 mice, I found three genes with altered levels of mRNA expression. I further analyzed the mRNA expression levels of these three genes in human HD caudate samples. TheYAC 128 model expresses full-length mutant HTT from a YAC transgene containing the entire human HIT gene with 128 CAG repeats. This mouse model faithfully replicates key features of the human disease, including age-related motor and cognitive dysfunction, and progressive selective degeneration of specific neurons in the striatum that is identical to the pattern seen in human patients (1). By 12 months, there are clear reductions in striatal volume, striatal neuronal counts and striatal neuronal cross-sectional area in these mice (1). A recent study has investigated transcriptional changes in this mouse model at 12 months and 24 months using Affymetrix microarrays (2). They found that the 2-year-old YAC 128 mice exhibited the most pronounced transcriptional changes and significant HD-like mRNA signatures (2). In our study, using quantitative real-time PCR as a more sensitive method, I could detect transcriptional changes in the YAC 128 mouse model at 12 months of age. 3.2 MATERIALS AND METHODS 3.2.1 Animals For this study, we used YAC 128 mice developed in the laboratory of Dr. Michael Hayden on  2  A version of this chapter will be submitted for publication. Mazarei, G., Lu, G., Leavitt, B.R. Identification of Striatal Gene Expression Changes in Huntington Disease.  42  FVB/N background containing the complete human HTT gene including 128 CAG expansion and  25 kb of upstream promoter (1). All animal experiments were designed and pursued  according to UBC animal care guidelines and have been approved by the UBC Animal Care Committee. 3.2.2 Human post-mortem caudate and putamen samples Table 3.1 depicts a summary of human post-mortem caudate/putamen samples used in our experiments. The human brain bank is located in the Centre for Molecular Medicine (CMMT) and Therapeutics and has been stored at -80 °C temperature. The Ethics Certificate of Expedited Approval for establishment and maintenance of the brain bank has been obtained from UBC by the CMMT principal investigators working in the RD field (UBC creb # H06-70467). Table 3.1 Caudate/putamen samples used from the CM.MT brain bank Sample Control Control Control Control Control Control Control Control HD HD HD HD HD HD HD HD HD HD HD HD  age 60 74 68 21 46 36 29 18 35 61 37 41 43 63 64 75 62 53 59 80 PMI  PMI 8 6 7 8.5 10 10 4.5 10 19 3 37 5.5 3.5 5.5 10 3 11 9 7 9  =  GAG size  ? ? ? 18/52 21/49 23/44 17/42 19/43  17/45 17/53 23/47 ?  post-mortem interval (in hours)  3.2.3 Quantitative real-time PCR YACJ28 experiments  43  Grade  0-1 0-1 0-1 3 3 3 3 3 3 4 4 2  RNA was extracted from the fresh-frozen dissected striata of eleven wild-type and nine  YAC128 transgenic 12-month-old mice using the RNeasy kit (Qiagen). Yield and purity of RNA samples were monitored using 260/280 and 260/230 ratios respectively. Same primers as in Table 2.1 were used to amplify the candidate striatal-enriched transcripts. Primer efficiencies were determined using a dilution series of wild-type adult striatum cDNA. Only primer pairs with an efficiency greater than 0.98 were used in subsequent analyses. An ABI 7500 real-time PCR system (standard protocol) and SYBR® Green Master mix (Qiagen) were used to compare wild-type and transgenic mRNA expression of the candidate gene. cDNA sampeles were generated by the reverse transcription (RT) of 1 ,ug of total RNA from newly dissected brain regions using the Quantitect RT kit (Qiagen). All reactions were carried out in duplicate using 21 ul of generated cDNA in each reaction. For analysis, absolute quantity of the targets in each sample was calculated based on the standard curve method and normalized to endogenous control, fl-actin. Human post-mortem experiments RNA was extracted from 20 brain samples listed in Table 3.1 using the RNeasy kit (Qiagen).  Primers were designed using Primer3 against human genome for human GAPDH, ,B-actin, 1 8S rRNA, Apis 1, Cd4, Indo, A1836640 orthologous region, and the DARPP32 gene. Primer  efficiencies were determined using a dilution series of control caudate cDNA samples.Only primer pairs with the efficiency greater than 0.98 were used in subsequent analyses. An ABI 7500 real-time PCR system (Fast protocol) using ABI Fast SYBR Green Master mix (Applied Biosystems). Equal amounts of cDNA (Quantitect Qiagen RT kit) were used in PCR reactions. All reactions were carried out in duplicates. For the analysis, samples were normalized to 18S rRNA and relative quantity was calculated based on the standard curve method. A multifactorial ANOVA was performed to rule out the dependence of the results on variables such as age of  44  death, CAG size, and grade of the disease (ranging from 0-4 or from presymptomatic to advanced). Table 3.2 contains the human specific primers designed for qRT-PCR experiments. Table3.2 Human primer sequences used in qRT-PCR analyses Human Gene Name !Forward Primer (5’ > 3’) Apis 1 CACCTI’TCGGAGTGAGCTGT Darpp32 CTGCCAGTCAfl’CCTCCATT’ A1836640 ortholog GGGACTGGCACTCTGTACCT Indo TATGACGCCTGTGTGAAAGC Cd4 CTGCIITI’CATrGGGCTAGG GAPDH GAAGGTGAAGGTCGGCGTC AGTACTCCGTGTGGATCGGC 13-actin i8S rRNA CGCCGCTAGAGGTGAAATC  Reverse Primer (5’ > 3’) GGGTGACATCAGTTCTGCAA ATC17AGGGTCCTGCCCTGT GTGTCCCTGAGTGTCCTTGG TCAGTGCCTCCAGTrCC’TTT GAGGCTGCAAGTGGGATCT GAAGATGGTGATGGGAC GCTGATCCACATGTGCTGGA TI’GGCAAATGCTTTCGCTC  3.2.4 Statistical Analysis A Two-tailed Students t-test was performed for determining differences in the mean between treatment groups and Levene’s test to determine equality of variance. Differences were assumed statistically significant given they reached the 95 % confidence level. All values are represented as averages with standard error of the mean. 3.3 RESULTS 3.3.1 Analysis of the candidate transcripts in the YAC 128 mouse model of HD I hypothesized that transcripts that are enriched in the striatum compared with other brain regions are more likely to play a significant role in the physiology of this region and will provide insight into the selective degeneration of this region in HD. To analyze the expression of these novel striatal-enriched transcripts in the context of the disease, I measured their expression in the 12-month-old YAC 128 mouse model of HD using qRT-PCR I found that among thirty-four transcripts assessed by qRT-PCR, three genes exhibited a significant change in their mRNA expression level. These included Cd4 antigen (p-value YAC128) and Apis] (p-value  =  =  0.0 15, N= 10 WT, eight  0.003, N= nine WT, seven YAC128) that were down-regulated  in the striatum of the YAC 128 mice (Fig. 3.la, d, respectively) while indoleamine-pyrrole 2,3 dioxygenase (Indo) (p-value  =  0.05, N= 10 WT, eightYAC 128) was up-regulated in these mice 45  (Fig. 3. ib). I further tested the expression of these three genes at 3 months of age. Only Indo showed a significant up-regulation at this time-point (Fig. 3. ic). Among a set of randomly selected previously-reported striatal-enriched genes (Ppplrbl, Penk], Tad, and Gnal, Pcp4l, Spock3, St8sia3) tested in the striatum of the YAC 128 mice, I could demonstrate a significant down-regulation in Pppl rb transcript (p-value  =  0.029, N= 11 WT, eight YAC 128) (Fig. 3.le)  which was consistent with reduction in Ppplrb] encoded protein (DARPP-32) in the YAC 128 mice (56, 58). I additionally identified, for the first time in the YAC 128 mice, a highly significant down-regulation in Gnal (p-value = 0.02, N= 10 WT, seven YAC 128) transcript level (Fig. 3.10 which had a consistent expression change in human HD putamen samples (3).  a. 1.20E-O1  1.OOE-O1  8.OOE-02  g .0  6.OOE-02  4.OOE-02  p =0.015  -  -  -  -  -  z 2.OOE-02  -  O.OOE+OO WT  YAC  46  b. 0.18  p =0.05  0 .  4-  C 0  ( 1 V. IV -1  I  E C1  0.14  4-  0  H  C 0.12 o  ‘  a)  0.08  I  -  DWT • YAC  >  4-  .! 0.06 a,  -  z 0.04 E o 0.02  n  C  =  10  0WT  C.  YAC  p  =  0.039  0  0.9 o E 0.8 c) 0.7  -  C  -]  I  0.4 0.3  E  0.2 0.1  -  C  n=5  0—  -I  WT  YAC  47  d.  p= 0.003  0.4 0.35  I  E c..1 O.3 0.25 .o Cu a)  0.2 0.15-H  a)  01 ‘—  0.05 n=9  0.  0WT  YAC  e. 1.20E-01  p  =  0.029  C 0  E C%1  I  1.OOE-01 -1  4-  8.OOE-02  i  6.OOE-02  -  -J  16 4.OOE-02  2.OOE-02  -  n °-  =  11  0.OOE+00 WT  YAC  48  f. p0.02  0.18.1  C  0.14  -  0.12—I 0.1 0.08 0.06 0.04  -  1 -  E C  0.02 0 WT  YAC  Figure 3.1 Transcriptional changes of novel candidate striatal-enriched genes and two positive controls in the striatum of the YAC128 mouse model of HD. Testing of the mRNA levels of 34 novel striatal-enriched transcripts in the striatum of one-year-old YAC 128 mice revealed the most significant mRNA expression changes in the Cd4 and Apis] genes (a, b). Indo mRNA expression (c) exhibited a significant up-regulation at 12 months and 3 months of age (d). Well-known striatal transcripts, Pppi rib and Gnal showed significant changes at 12 months (e, f). Error bars represent SEM. Significant differences were determined by student’s t-test (unpaired; two-tailed) using a threshold value of . 05 0 p.< 3.3.2 Analysis of striatal gene expression changes observed in YAC128 in the human HD caudate In order to assess the relevance of the gene expression changes I observed in the YAC 128 mouse model of HD; the mRNA expression levels of striatal-enriched transcripts that showed altered levels of expression in this mouse model were tested in caudate specimens of HD patients and age-matched controls. To determine an unregulated endogenous reference gene in HD, I measured expressions of commonly used reference genes J3-actin, 18S rRNA, and GAPDH. Using real-time PCR analysis, I found that 13-actin and GAPDH were differentially  49  expressed in the brain specimens of both HD and control subjects, while 18S rRNA was similarly expressed in HD and control caudate samples. Therefore, 18S rRNA was used as the endogenous reference gene in our further qRT-PCR analyses of striatal transcripts. Initially, PPP]R]B mRNA was measured due to its previously reported altered expression in human HD caudate and in different mouse models of HD such as YAC 128 (reported in this study, section 3.3.1) fragment protein models, and knock-in models (4, 5, 6, 7). These results were reproduced in our qRT-PCR analyses of human post-mortem caudate samples where PPP]R]B mRNA levels were significantly reduced compared with controls (Fig. 3.2a). To reproduce expression data from YAC128 analysis, qRT-PCR was also performed for human orthologs of Apisi, Cd4, and Indo. Consistent with the mouse data, human APiS] mRNA was shown for the first time, to  be significantly reduced in human post-mortem caudate (Fig. 3.2b). This suggests a potential involvement of this transcript in HD pathogenesis. Despite being significantly enriched in mouse striatum, human CD4 and INDO transcripts showed unexpectedly low levels of abundance in human HD and control post-mortem tissues and to date, have not been successfully amplified in my qRT- PCR experiments.  50  a. p=O.019  8  j 7 6  5-  DControU • HD  41  .  E 0. 0.  I  T  2  n=7  n =11  Control  HD  b.  6p=O.039 5-  4-  .2 DCOfltroI r.  I  • HD  Ex  I-a,  0  1—2  n=8 0 Control  HD  Figure 3.2 Alteration of striatal-enriched transcripts in post-mortem caudate of HD subjects and controls. PPPJRJB (a) and APiS] (b) showed significant down-regulation in HD subjects compared with controls, similar to expression changes observed in the YAC 128 mouse model. Error bars represent SEM. Significant differences were determined by student’s t-test (unpaired; two-tailed) using a threshold value of p<O.05. .  51  Finally, as an example of an unknown striatal-enriched transcript with un-altered levels of expression in the YAC 128 mice, A1836640 EST human ortholog also exhibited no transcriptional change in human HD samples (data not shown). 3.4 DISCUSSION In this part of the project, I investigated the expression levels of the candidate novel striatal enriched transcripts selected by our SAGE analysis in the YAC 128 mouse model of HD as well as human post-mortem HD striatum. The YAC 128 mouse model of HD expresses full-length mutant HTT from a YAC transgene containing the entire human HiT gene (1T15) with 128 CAG repeats. This model faithfully replicates key features of the human disease, including the progressive selective degeneration of striatum (1). This selective striatal neuronal loss and neuropathology makes the YAC 128 a relevant model to study selective degeneration in HD. Striatal degeneration in these mice manifests as striatal atrophy beginning at 9 months progressing to neuronal loss at 12 months of age (1). Neuronal loss detected in the 12-month-old YAC 128 mice manifest as a significant 15% decrease in striatal neuronal count compared with control littermates (1). As a possible consequence of striatal pathology, 12-month-old YAC128 mice already exhibiting motor learning deficit, also show clear patterns of cognitive dysfunction appearing as decreased pre-pulse inhibition and habituation to acoustic startle (8). Therefore, the significant manifestation of neuropathology and cognitive phenotypes at 12 months, made us interested in testing striatal transcription at this pathological time-point. Having examined all thirty-four candidate striatal-enriched transcripts at 12 months, I identified three genes (containing four striatal-enriched transcripts) whose levels of mRNA expression were significantly changed in the striatum of YAC 128 mouse model of HD. This indicated that of the novel striatal-enriched transcripts selected through our SAGE analysis showed a significant expression change in this mouse model.  52  —  11%  One of the three genes showing an altered mRNA expression level in the YAC 128 mice was the Cd4 antigen. The Cd4 antigen is a membrane glycoprotein of T lymphocytes that interacts with major bistocompatibility class II (MHC II) antigens and is also a receptor for the human immunodeficiency virus (HIV). The gene is known to be expressed highly in thymus and spleen but also lower levels are found in the brain (9). In transgenic mice carrying the human CD4 gene, Buttini et al. (1998) found that human CD4 is expressed on microglia, the resident mononuclear phagocytes of brain. Interestingly, this group found no evidence of neuronal damage in the Cd4 mice at baseline, however, activation of brain microglia by peripheral immune challenges elicited neurodegeneration in human Cd4 mice but not in non-transgenic controls (10). In post-mortem brain tissues from AIDS patients with opportunistic infections, but without typical HTV encephalitis, human CD4 expression correlated with neurodegeneration (10). Accordingly, they concluded that human CD4 may function as an important mediator of indirect neuronal damage in infectious and immune-mediated diseases of the central nervous system (CNS). The important role of human CD4 expression on microglialmacrophages creates a pathogenetic link between the immune system and the CNS. The fact that Cd4 is a striatal enriched gene (found through our SAGE analysis) may explain the susceptibility of the striatum to neuroinflammation observed in HD (11) which can potentially lead to its selective degeneration. The altered mRNA levels in the YAC 128 mice however, suggest a potential correlation between down-regulation of Cd4 and striatal neuronal loss. Further investigation is required to first understand which brain cell types in our mouse model express Cd4 (ie. microglia vs. MSNs) and to subsequently explain the effect of Cd4 transcript down-regulation in the context of neurodegeneration in HD. Indo, another gene with altered mRNA expression levels in the YAC 128 mouse model, is a tryptophan-catabolizing enzyme which initiates the first and rate limiting step of the kynurenine pathway. In the brain, Indo can be induced in microglia by interferon-by-producing T helper (Th) 53  1 cells. Indo is thought to initiate a negative feedback loop which regulates neuroinflamiriation in an animal model of multiple sclerosis (MS) (12-14) Induction of Indo activates the, kynurenine pathway leading to production of neurotoxic metabolites such as 3 hydroxy Kyurenine and quinolinic acid known to be involved in HD pathogenesis (15). Accordingly, Kiwidzinski and Bechmann (2007) have proposed that neuroinflammation and neurodegeneration are linked by IFN--y-mediated Indo induction (16). In a recent expression study of amyloid B peptide-stimulated human postmortem brain microglia, the INDO protein was found to be up-regulated identifying a potential new inflammatory pathway in Alzheimer’s disease (17). The observed increase in Indo inRNA expression in the YAC128 mice could suggest for the first time, a similar involvement of Indo leading to neuroinflammation described in HD (11). Further analysis of Indo protein in the context of I-ID is needed to draw more conclusive results. Apis] is another striatal-enriched gene which is significantly down-regulated in YAC 128 mice. The protein encoded by this gene is an important subunit of the clathrin adaptor complex involved in clathrin-coated vesicle transport of endocytosis and Golgi processing. A very recent study has described a novel mutation in the human Apis] gene which was found in four families with Erythrokeratodermia Variabilis type 3 (EKV3) characterized by various ichthyosiform skin lesions, psychomotor retardation, and polyneuropathy. This observation was also validated in Apis] knock-down zebrafish which demonstrated severe motor deficits (Brustein et al., 2007, Neuroscience 2007, San Diego, CA). Many studies have investigated HT”T” s protein-protein interactions and have provided links to cellular transport mechanisms, specifically HTT is thought to play an important role in neuronal vesicular trafficking. HTT is involved in the transport of lipid vesicles either endocytic (18), synaptic (19), or lysosomal (20) along microtubules and/or the cell skeleton, via an energy dependent motor machinery (21). Impairment of this wild-type HTT function by the 54  polyglutamine expansion in mutant HTT has been suggested to result in decreased endocytosis (22, 23). Motor phenotypes observed in mutants with diminished EKV3 and Apisi protein endocytic function together with the potential role of wild-type HTT in endocytosis provide a hypothetical link between Apis] gene function and HD. The reduction in Apis] mRNA expression in the YAC 128 mice would imply vesicle transport defects involving clathrin dependent endocytosis and golgi processing. This observation however, requires much deeper investigation of Apis] encoded protein and further validation to confirm its role in neuronal degeneration in HD. The next step in our project was the examination of mRNA expression levels of the three significantly altered candidate genes in the human post-mortem brain. The best possible set of tissue samples with preferential post-mortem intervals  <  10 hours were chosen from the CMMT  HD brain bank. Consistent with expression results from YAC 128 analyses, mRNA expression levels of PPP]RJB as a positive control gene and APiS] as a novel striatal- enriched gene, both showed significant down-regulation in the post-mortem caudate samples. CD4 and INDO mRNA expression levels were also tested in the post-mortem brain. The results are so far not conclusive since unlike their high mRNA expression in mouse striatum, these transcripts seem to be very low in abundance in both HD and control caudate samples and have thus been hard to amplify to date. My inability to successfully amplify these transcripts could be due to technical factors such as RNA degradation in post-mortem tissues. 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(2006) Gene expression changes by amyloid beta peptide-stimulated human postmortem brain microglia identify activation of multiple inflammatory processes. Journal of leukocyte biology, 79, 596-610. McMahon, H.T. and Mills, I.G. (2004) COP and clathrin-coated vesicle budding: different pathways, common approaches. Current opinion in cell biology, 16, 379-9 1. Wu, L.G. (2004) Kinetic regulation of vesicle endocytosis at synapses. Trends in neurosciences, 27, 548-54. Rohrbough, J. and Broadie, K. (2005) Lipid regulation of the synaptic vesicle cycle. Nature reviews, 6, 139-50. Phelps, M.A., Foraker, A.B. and Swaan, P.W. (2003) Cytoskeletal motors and cargo in membrane trafficking: opportunities for high specificity in drug intervention. Drug discovery today, 8,494-502. Gil, J.M. and Rego, A.C. (2008) Mechanisms of neurodegeneration in Huntington disease. The European journal of neuroscience, 27, 2803-20. Trushina, E., Singh, R.D., Dyer, R.B., Cao, S., Shah, V.H., Parton, R.G., Pagano, R.E. and McMurray, C.T. (2006) Mutant huntingtin inhibits clathrin-independent endocytosis and causes accumulation of cholesterol in vitro and in vivo. Human molecular genetics, 15, 3578-91.  57  CHAPTER 4: SUMMARY AND CONCLUSION In this project, I was able to create a high-confidence list of novel striatal-enriched transcripts. These included known genes not previously reported as striatal-enriched, unknown transcripts, and new striatal-enriched splice variants of known genes. Although this novel list of striatal genes did not include an overrepresentation of any specific biological theme, it allowed the identification of novel striatal-enriched members of known striatal pathways as well as genes whose pathways were not previously implicated in the physiology of the striatum. The expression of these genes was subsequently tested in the YAC 128 mouse model of HD and candidates with altered levels of expression in these mice, were then examined in the human post-mortem caudate samples. 4.1 SUMMARY AND SIGNIFICANCE Based on our hypothesis, genes that are transcribed specifically and abundantly in the striatum play crucial roles in the striatal physiology and may therefore be involved in HD pathogenesis. I identified a group of novel candidate striatal-enriched transcripts using gene expression data from a previous mouse brain SAGE analysis, confirmed the brain expression patterns using the AIBS, and performed a subsequent validation of selected striatal-enriched transcripts using qRT-PCR. The expression of these candidate transcripts was then examined in the YAC 128 mouse model of HD. Gene candidates with altered levels of expression in this mouse model were further tested in human post-mortem tissues. Interestingly, significant expression changes were found in 4 out of 34 striatal-enriched transcripts  (— 11%) in YAC 128  mice at twelve months of age. This observation can be compared to a microarray study analyzing all striatal transcripts on a gene chip, which revealed no significant changes in gene expression in YAC128 mice at 12 months (1). These findings supports our hypothesis and demonstrates the benefit of studying candidate striatal-enriched genes in the identification of the 58  direct and indirect changes as potential results of mutant HTT expression in the brain. It is also worth noting that although expression of these transcripts may be altered in all brain regions, their relative abundance in the striatum may make this region susceptible to neurodegeneration. However, further investigation is required to assure the involvement of these genes in the selective neurodegeneration in HD. Identification of two genes of the immune system (Cd4 and Indo) with striatal-enriched patterns as well as altered levels of expression in the YAC 128 mouse model suggests a potential role of immune dysfunction in brain pathology in these mice. This finding indicates an element of neuroinflammation in HD pathology, and can lead to future studies investigating the activation of microglia or possibly other immune cells in the HD brain. Two transcripts of the Apis] gene, another striatal-enriched gene, were also shown to have altered expression levels in the mouse model as well as in human post-mortem HD striatum. These findings indicate a potential aberration in the function of clathrin-dependent endocytosis in the neurons of HD brain. Although these results have yet to be confirmed with further experiments, they suggest a new pathogenic mechanism by which mutant HTT alters neuronal function, and will likely provide additional clues towards understanding the selective striatal degeneration in HD. In this project, for the first time, expression changes of a novel group of striatal-enriched genes were measured in a full-length protein HD mouse model. Use of the YAC 128 mouse model strengthened this study in different ways. In addition to having the advantage of a mouse model with an age-dependent selective striatal neurodegeneration, the YAC 128 model provided the advantage of testing transcriptional changes within the context of the full-length human mutant HTT protein. Recently, it has been shown that prevention of caspase-6-mediated cleavage of the full-length mutant HTT protein in vivo can rescue the progressive HD phenotype of the YAC 128 model (2). This suggests that specific fragments resulting from caspase-6 proteolysis of the full-length protein, and not just any N-terminal fragments, are necessary for 59  pathogenesis in HD. Moreover, unlike N-terminal mouse models where mutant HTT aggregates in the nucleus and massive transcriptional changes have been demonstrated (3, 4), the agerelated progression of the neurodegenerative HD phenotype in the YAC 128 model likely increases the specificity of the observed expression changes and allows a careful investigation of these changes throughout the time-course of pathology. This leads to the identification of fewer, but more disease-specific changes, in the YAC 128 mice and may help to distinguish between specific upstream changes and those occurring downstream during the course of HD. Comparing the gene expression findings between the YAC 128 model and N-terminal fragment models once again suggests that increased HTT protein length reduces the number of polyglutamine-induced gene expression changes (5). As mentioned before however, this reduced number of gene expression changes could correspond to more specific changes that are relevant to RD pathology. In vivo analysis of the striatal-enriched genes and biological systems with dominant expression levels in the striatum  --  in the context of full-length HTT protein  --  has been the  focus of my Masters of Science project and has provided me with insight into the complexities of striatal transcriptome and its differences with other brain regions. My expression analysis of these striatal-enriched transcripts has demonstrated, that by focusing on this important group of genes, one will be able to find clues towards understanding pathogenic mechanisms leading to selective neurodegeneration in HD. Using the power of SAGE technology and other available public gene expression databases in my project, as tools for both transcript profiling and gene discovery and its use in examining changes in HD brain gene expression patterns, this project has enabled me to identify interesting potential genes that may be involved in RD pathogenesis, candidate biomarkers that may be used to follow disease progression, as well as identifying potential new therapeutic targets for HD.  60  4.1.1 Potential pitfalls As mentioned previously, based on the selection criteria used for generating the list of novel striatal-enriched genes, there is a possibility that several striatal-enriched genes were filtered out. For example, in order to narrow down the massive number of tag-sequences selected in our SAGE analysis, I eliminated all SAGE tags with only a single count, despite the fact that they may represent valid (albeit expressed at a low level) transcripts which could provide valuable information about striatal-specific gene expression. This however, does not imply that these ‘singletons’ will not be of future research interest. I acknowledge that there are no ‘fixed’ ways of selecting for these genes, and not being able to select for all striatal-enriched genes was an anticipated outcome. In this MSc project, in order to maximize the number of specific striatal enriched genes in our selection, I set the selection criteria based on those selection conditions which gave me the largest number of well-known striatal-enriched genes (positive controls) but was still manageable in scope. This way, I assured that I selected for a large number, if not all, of striatal-enriched genes in the brain. Having generated a subset of candidate genes using transcriptome-wide techniques, one should realize that due to limited amount of information on many of these genes, functional and biochemical characterization of any of them would require an exhaustive amount of in silico and bench work. Therefore, prioritizing the study of one gene over another will be a crucial decision to make. For this reason, I will have to come up with priority criteria that would help me call a gene “more interesting” than the rest. Another probable obstacle in studying these genes will be evident when dealing with proteins corresponding to these genes. Even for many known genes in our list, having access to useful antibodies is unlikely. One example is Apis], a gene with altered levels of expression in both YAC 128 and human HD brain, whose mouse antibody is not publicly available. This makes the protein analysis for this particular gene more challenging. One option would be to generate a 61  mouse antibody against this protein in our lab, which if successful, would provide us with the necessary tool to investigate protein expression albeit in a much extended timeframe. Working with human post-mortem tissue is always a challenge, due to the nature of the samples available. The definition of the best human brain sample could vary depending on the purpose of the study. Ideally, these experiments should include large number of samples and age-matched controls where variables such as grade of the disease, CAG size, age of death, and post-mortem interval (PMI) are held constant. RNA degradation in these samples is an inevitable issue, especially if the tissue had been thawed and frozen more than once. In our study, only samples with PMI < 10 hours were used. This criterion however, selected for samples with a wide spectrum of other variables. A multifactorial ANOVA analysis was done to make sure that the variability in age, CAG size, and grade was not affecting the results. However, further work is required to determine which variables affect our gene expression study the most and remove them from our analysis. 4.2 FUTURE DIRECTIONS: FURTHER COMPUTATIONAL AND BIOCHEMICAL ANALYSES OF CANDIDATE STRIATAL-ENRICHED MARKERS AND THEIR IMPLICATIONS IN HD 4.2.1 Introduction This project has proven to have the potential to add knowledge to our understanding of HD pathogenic mechanisms. Tissue-specific transcriptome analysis of the striatum, the brain region predominantly affected by HD, has been useful in understanding more about the unique physiological pathways in this brain region. Further examination of the tissue-specific transcripts I have identified and their related functional pathways in the context of HD models and post-mortem samples will be useful in characterizing novel cellular changes that could potentially explain the selective striatal neurodegeneration in HD. 4.2.2 Detailed characterization of novel candidate striatal-enriched transcripts  62  Protein-encoding striatal-enriched genes Having in hand a list of novel striatal-enriched genes, I can lead this project towards many new directions. Analysis of interesting striatal-enriched protein-encoding genes will be the next step in this part of the project. In case of availability of corresponding antibodies, immunohistochemistry will be performed to monitor protein localization and expression patterns of the novel striatal-enriched protein in different regions of the brain. At the cellular level, MSNs form  -  90% of the neuronal population in the striatum. This makes the striatum relatively  homogenous in terms of cellular population. However, in our analysis, a gene can also become selected as striatal-enriched if it is highly expressed in other striatal neuronal populations (ie, intemneurons) or in striatal immune cells such as microglia and astrocytes. A future study will include performing immunocytochemistry for cell-type-specific expression of the novel striatal enriched candidates to monitor the protein expression level in different striatal cell types. Striatal-specijic pathways/processes The identification of biological processes and cellular pathways to which these novel striatal genes belong, will be an important focus of my future studies. In general, enrichment of a specific pathway member in a specific tissue can lead to a more pronounced involvement of that pathway in that tissue. Tissue-specificity in expression of a certain pathway member can even cause that pathway to be “turned on” specifically in that tissue. Similarly, understanding how striatal enrichment of our candidate genes gives striatum its “signature” characteristics may lead to the development of new hypotheses regarding striatal pathophysiology in HD. For instance, the predominant striatal expression of transmembrane protein 158 (Tmeml58), a gene implicated in cancer (6) can elicit new mechanisms in striatal physiology and its susceptibility to neuronal loss in HD. Similarly, striatal-enrichment of the 3 variants of the phosphatase and actin regulator 1 (Phactri) gene and its role as the potent modulator of protein phosphatase 1 (PP 1), a multifunctional enzyme with diverse roles in the nervous system, could provide more clues 63  regarding striatal-specific phosphorylation pathways. Other examples of candidates with constant high expression throughout striatal development are genes of immune system such as Indo and Cd4 may explain the potential vulnerability to neuroinflammation implicated in HD (7). The above are all examples of known striatal transcripts whose biological pathway are easier to pin-point. For unknown transcripts however, this will be a much more difficult task which requires an exhaustive effort in gene characterization. In silico and in situ characterization of the ‘unknown’ striatal-enriched transcripts More detailed characterization of the novel unknown striatal transcripts will be done as both in silico and in situ analyses of transcript sequences using public genomic databanks. The genomic information obtained from these databases will provide the basis for ‘sequenced-based biology’, whereby sequence data can be used more effectively to design and interpret experiments at the bench (8). Using available genomic information such as SAGE and EST sequences, gene prediction datasets as well as information on inter-species orthology and intergene homology has allowed us design primers in expressed regions of the genome. Having performed the qRT-PCR analysis for these transcripts, I will use the amplified region to design probes for northern blot analysis. This experiment can reveal the exact size of the transcriptional unit products and its corresponding variants. Having the sequence information in hand, I will design in situ hybridization probes to perform a detailed analysis of unknown transcripts expression patterns in the mouse brain. Whether these novel striatal genes express as non-coding functional RNAs or translate into proteins will also be an important aspect of gene characterization in this project. A good example of a potential novel striatal gene to be characterized through these steps is the transcriptional unit on the negative strand of mouse Chromosome 19 found by two potentially related tag-sequences in our SAGE analysis (tag # 1, table 2.2 and tag # 29, table 2.3). 64  Functional studies of candidate striatal-enriched transcripts Understanding the function of novel striatal-enriched genes is this project’s long-term goal. Striatal-specific expression of these genes and their expression changes in HD are only interesting if one obtains clues regarding the function of the corresponding genes. The majority of the novel striatal-enriched candidates selected in this project are genes whose functions in the brain have not been extensively studied. These include annotated genes with known pathways, annotated genes with unknown pathways, and un-annotated genes with unknown pathways. A precise literature search for any knockout or mutation model of these genes will be an initial step towards understanding the relevant functional pathways associating with annotated candidate genes. My future plans will also include in vitro studies of annotated and un-annotated striatal-enriched genes. The generation of specific knockout and over-expression constructs as well as microRNA/short hairpin RNA-based RNAi constructs against the candidate genes and testing of these knock-down constructs in an appropriate cell culture system will be the primary focus of this part of my project. 4.2.3 Study of expression changes of candidate striatal-enriched genes in more detail Experiments such as northern blotting and in situ hybridization will be performed in order to confirm changes in mRNA level observed in the YAC 128 mouse model of HD. Although many specific-enriched genes failed to show specific expression changes in the YAC 128 mice compared to wild-type littermates, further work has to be done to rule out the possibility that subtle mRNA expression changes were missed and that the changes that were identified were not simply due to the striatal neuronal loss which occurs in this mouse model. For this purpose, a reasonable experiment will be to perform single-cell laser capture microdissection from YAC and wild-type mice and monitor expression changes of these specific genes. For protein-encoding striatal-enriched genes, it is crucial to investigate whether mRNA expression changes are also translated at protein level. The studies at the mRNA level are 65  largely based on the assumption that mRNA expression is informative in predicting protein expression level in relation to gene function. Due to inconclusive results of the only few studies investigating the correlation of mRNA and protein expression levels (9-11), we will perform our own protein analyses of expression changes observed in the YAC 128 model. Techniques such as western blotting and immunohistochemistry will be used to compare the abundance of these proteins in YAC128 vs. wild-type littermates. To gain a deeper understanding of disease pathogenesis in HD, an age-dependent analysis of these expression changes will be crucial. The YAC 128 mouse manifests cognitive dysfunction as early as 2 months in the form of learning disabilities which progressively worsen with age (12). Similar to HD, cognitive phenotypes precedes the motor dysfunction in YAC 128 mice. Behavioral changes occur first at 3 months of age evident as hyperactivity and progress to hypoactivity by 12 months. Motor deficit is evident on the rotarod beginning at 6 months of age and progressively worsening with age. As mentioned before, neurodegeneration of specific brain regions also develops in the mice with striatal atrophy beginning at 9 months of age and cortical atrophy arising by 12 months (13, 14). This wide spectrum of cognitive and motor phenotypes in the YAC 128 mice makes us interested in learning more about gene expression changes throughout the course of the disease in the mice. For this purpose, an initial expression analysis of 3, 6, 9 months of the candidate gene with altered expression levels at 12 months will be a necessary step to provide more clues about the involvement of these genes during the course of pathogenesis. The results of these analyses can provide us with ways to identify markers for disease progression. 4.3 EXPECTED OUTCOMES For the future of this project, I anticipate that identification and characterization of novel striatal-specific genes and their expression changes in HD will elucidate disease pathways that  66  have important roles in the physiological processes and integrity of striatal neurons and may represent novel targets for therapeutic development in HD.  67  4.4 BIOBLIOGRAPHY 1.  2.  3.  4. 5.  6.  7.  8. 9.  10. 11. 12.  13.  Kuhn, A., Goldstein, D.R., Hodges, A., Strand, A.D., Sengstag, T., Kooperberg, C., Becanovic, K., Pouladi, M.A., Sathasivam, K., Cha, J.H. et at. (2007) Mutant huntingtin’s effects on striatal gene expression in mice recapitulate changes observed in human Huntington disease brain and do not differ with mutant huntingtin length or wildtype huntingtin dosage. Human molecular genetics, 16, 1845-61. Graham, R.K., Deng, Y., Slow, E.J., Haigh, B., Bissada, N., Lu, G., Pearson, J., Shehadeh, I., Bertram, L., Murphy, Z. et at. (2006) Cleavage at the caspase-6 site is required for neuronal dysfunction and degeneration due to mutant huntingtin. Cell, 125, 1179-9 1. Luthi-Carter, R., Strand, A., Peters, N.L., Solano, S.M., Hollingsworth, Z.R., Menon, A.S., Frey, A.S., Spektor, B.S., Penney, E.B., Schilling, G. et al. (2000) Decreased expression of striatal signaling genes in a mouse model of Huntington disease. Human molecular genetics, 9, 1259-7 1. Thomas, E.A. (2006) Striatal specificity of gene expression dysregulation in Huntington disease. Journal of neuroscience research, 84, 1151-64. Chan, E.Y., Luthi-Carter, R., Strand, A., Solano, S.M., Hanson, S.A., DeJohn, M.M., Kooperberg, C., Chase, K.O., DiFiglia, M., Young, A.B. et al. (2002) Increased huntingtin protein length reduces the number of polyglutamine-induced gene expression changes in mouse models of Huntington disease. Human molecular genetics, 11, 193951. Barradas, M., Gonos, ES., Zebedee, Z., Kolettas, E., Petropoulou, C., Delgado, M.D., Leon, J., Hara, E. and Serrano, M. (2002) Identification of a candidate tumor-suppressor gene specifically activated during Ras-induced senescence. Experimental cell research, 273, 127-37. Bjorkqvist, M., Wild, E.J., Thiele, J., Silvestroni, A., Andre, R., Lahiri, N., Raibon, E., Lee, R.V., Benn, C.L., Soulet, D. et at. (2008) A novel pathogenic pathway of immune activation detectable before clinical onset in Huntington disease. The Journal of experimental medicine, 205, 1869-77. Wolfsberg, T.G., Wetterstrand, K.A., Guyer, M.S., Collins, F.S. and Baxevanis, A.D. (2002) A user’s guide to the human genome. Nat Genet, 32 Suppi, 1-79. Guo, Y., Xiao, P., Lei, S., Deng, F., Xiao, G.G., Liu, Y., Chen, X., Li, L., Wu, S., Chen, Y. et al. (2008) How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes. Acta biochimica et biophysica Sinica, 40, 426-36. Greenbaum, D., Colangelo, C., Williams, K. and Gerstein, M. (2003) Comparing protein abundance and mRNA expression levels on a genomic scale. Genome biology, 4, 117. Anderson, L. and Seilhamer, J. (1997) A comparison of selected mRNA and protein abundances in human liver. Electrophoresis, 18, 533-7. Van Raamsdonk, J.M., Murphy, Z., Slow, E.J., Leavitt, B.R. and Hayden, M.R. (2005) Selective degeneration and nuclear localization of mutant huntingtin in the YAC 128 mouse model of Huntington disease. Human molecular genetics, 14, 3823-35. Slow, E.J., van Raamsdonk, J., Rogers, D., Coleman, S.H., Graham, R.K., Deng, Y., Oh, R., Bissada, N., Hossain, S.M., Yang, Y.Z. et at. (2003) Selective striatal neuronal loss in a YAC 128 mouse model of Huntington disease. Human molecular genetics, 12, 155567.  68  14.  Van Raamsdonk, J.M., Pearson, J., Slow, E.J., Hossain, S.M., Leavitt, B.R. and Hayden, M.R. (2005) Cognitive dysfunction precedes neuropathology and motor abnormalities in the YAC 128 mouse model of Huntington disease. J Neurosci, 25,4169-80.  69  

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