University of Alberta
Identification of differentially expressed genes in chronic wasting disease infected elk
by
Sandor C. Dudas //M
A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of
Master of Science in Animal Science
Department of Agricultural, Food and Nutritional Sciences
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Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy
(TSE) of mule deer, white tailed deer, moose and elk. This invariably fatal neurologic disease has spread across North America infecting a growing number of fanned and free ranging cervids. The increasing prevalence of CWD could potentially increase the risk of transmission to traditional domestic livestock or humans. A limited understanding of CWD neurodegeneration at a molecular level has severely limited new approaches to treating, diagnosing and controlling this disease. In this study, we have used a bovine DNA microarray to identify genes which are differentially expressed in CWD infected elk. Altered regulation of genes involved in cytoskeleton formation, synapse function and immune signaling, all of which have been suggested as important in neurodegeneration, were identified in the CWD elk. These results improve our understanding of
CWD induced neurodegeneration at the molecular level and could potentially identify diagnostic or therapeutic targets. Preface
Chronic wasting disease is a transmissible spongiform encephalopathy which is gaining more attention due to its increasing incidence, geographic range and potential threat to animal and human health. Due to the lack of a well characterized model systems to study this disease little research has been done specifically on CWD to answer one of the most important unknowns of TSE diseases, how they cause neurodegeneration. The first two chapters of this thesis review the experimental evidence implicating various causes in prion disease induced neurologic disease. The first chapter presents the current state of knowledge regarding causes of cell death in TSE diseases and which proteins, molecules and cellular functions are potentially involved. In chapter 2, the contributions that DNA microarray gene expression analysis have made to improving our understanding of molecular mechanisms involved in TSE induced cytotoxicity are discussed. Finally, chapter 3 describes CWD and the work we have done on CWD elk using microarray gene expression analysis to increase our knowledge of this disease. Acknowledgments
I would like to thank the Alberta Prion Research Institute, Prionet Canada and the Canadian Food Inspection Agency for their financial support.
Thank you to the University of Alberta Bovine Genomics Laboratory staff, especially Dr. Masaaki Taniguchi, Yan Meng, Dr. Leluo Guan and Dr. Stephen
Moore. Your guidance and assistance both during my time up in Edmonton as well as when I was back in Lethbridge is much appreciated.
To my current and past coworkers including Dr. Anne Beeston, Dr. Stephanie
Booth, Renee Clark, Nancy Herman, Dr. Catherine Graham, John Gray, Dr.
Klaus Jericho, Dr. Lome Jordan, Roberta Quaghebeur, Greg Tiffin and Jianmin
Yang, thank you for your help and advice both in the lab and while writing my thesis. A special thank you to Dr. Stefanie Czub, your support and guidance have been instrumental in helping me complete this project.
And finally to my wife Gina and my sons Donte and Dion, your love, support and encouragement kept me going even when this task seemed impossible.
Words can not express how much this means to me or how much I love you for this. I will never forget it. Table of Contents
Page
1.0. Literature Review: Transmissible spongiform encephalopathies (TSE) and neurologic disease 1.1. Introduction 1 1.2. TSE disease background 1 1.2.1. Discovery of TSE diseases 1 1.2.2. The aetiological agent 2 1.2.3. The infectious prion protein 3 1.3. Causes of TSE induced neurologic disease 4 1.3.1. Neuronal impairment 5 1.3.1.1. TSE induced synaptic changes 5 1.3.1.2. Neurotransmitter dysfunction 6 1.3.2. Neuronal cell death 7 1.3.2.1. Loss of functional PrPc 7 1.3.2.2. Toxicity of PrPsc 9 1.3.2.3. Other PrP species 9 1.3.2.4. PrPc relocalization and accumulation 10 1.3.2.5. Cell system and organelle disruption 11 1.3.2.6. Involvement of autophagy 12 1.3.2.7. Immune/inflammatory involvement 13 1.4. Summary 14 1.5. References 16
2.0. Literature Review: Microarrays for understanding TSEs 2.1. Introduction 24 2.2. Microarrays for understanding disease and infection 26 2.3. Microarrays for understanding prion diseases 28 2.3.1. Protein folding, trafficking and degradation 28 2.3.2. Immune and inflammatory response 30 2.3.3. Cellular metabolism 31 2.3.4. Ion and cytoskeletal related genes 33 2.3.5. Cell signalling 34 2.3.6. Findings from unique experimental designs 35 2.4. Summary 37 2.5. References 38 3.0. Project: Identification of differentially expressed genes in CWD infected elk 3.1. Introduction 46 3.2. Materials and methods 50 3.2.1. Animals 50 3.2.2. RNA extractions 51 3.2.3. Amplification and labelling of mRNA 52 3.2.3.1. Target preparation 52 3.2.3.2. Target labelling 52 3.2.3.3. Hybridization and washing 53 3.2.4. DNA Microarray expression analysis 54 3.2.5. Real time qRT-PCR verification 55 3.2.5.1. Primer design and testing 55 3.2.5.2. Selection of house keeping genes for qRT-PCR analysis 56 3.2.5.3. qRT-PCR testing and efficiency 57 3.2.5.4. qRT-PCR analysis of CWD elk 57 3.3. Results 3.3.1. Confirmation of disease status 59 3.3.2. Microarray results 59 3.3.2.1. Population comparison microarray results 59 3.3.2.2. Selected animal comparison microarray results 60 3.3.2.3. Genetic effects on microarray results 61 3.3.3. qRT-PCR results 62 3.3.3.1. House keeping gene selection and efficiency determination 62 3.3.3.2. Validation of candidate genes selected from microarray analysis using qRT-PCR 63 3.4. Discussion 64 3.4.1. Disease status at sampling 64 3.4.2. Microarrays 64 3.4.2.1. Effects of analyzing a genetically diverse population 66 3.4.2.2. Effects of the prion protein genotype on the differential expression of genes 66 3.4.3. Quantitative RT-PCR 67 3.4.4. Significance of differentially expressed genes 69 3.4.4.1. Structural genes 70 3.4.4.2. Immune/inflammatory response and cell signalling 72 3.4.4.3. Synapse function 73 3.4.4.4. Calcium ion related genes 75 3.4.4.5. Functional groups missing from our study 76 3.5. Conclusion 77 3.6. References 103 Appendix Appendix 1: Total RNA extraction protocol 109 Appendix 2: DNA Microarray protocol 111
Appendix 3: Quantitative real time PCR (qRT-PCR) protocol 116 Appendix 4: Formulas used for data analysis 117 List of Tables Page
Table 3-1: Experimental animals used for the identification of differentially expressed genes in CWD infected brains. 80
Table 3-2: House keeping genes identified in previous brain qRT-PCR studies which were tried on our elk cDNA. 81
Table 3-3: Real time PCR primers/probes and associated amplification efficiencies. 82
Table 3-4: The genes identified as differentially expressed by comparing the CWD negative population to the CWD positive population (n = 287). 83
Table 3-5: Genes identified as differentially expressed in 4, 5 or 6 out of 6 CWD positive versus CWD negative elk by microarray analysis. Genes missing a comparison(s) involving animal #28 (132LM) are highlighted. 90
Table 3-6: Microarray results of differentially expressed genes in 5 or 6 out of 6 CWD elk versus control elk. Genes are grouped based on functional annotation.
93
Table 3-7: Genes from the 55 differentially expressed in all of the CWD positive animals that are associated with one or more KEGG pathway(s). 95 List of Figures Page
Figure 3-1: Microarray slide comparing the CWD infected brain stem gene expression profiles to the CWD negative brain stem gene expression profiles (n = 10). 96
Figure 3-2: Genesifter molecular function grouping of differentially expressed genes in the CWD negative population versus the CWD positive population compared to the molecular function grouping of all probes represented on the microarray. 97
Figure 3-3: Genesifter biological function grouping of differentially expressed genes in the CWD negative population versus the CWD positive population compared to the biological function grouping of all probes represented on the microarray. 98
Figure 3-4: Genes identified as differentially expressed when comparing the individual negative animals against different combinations of the positive animals. For each group the genes common and unique to both comparisons are shown. 99
Figure 3-5: GeNorm flow chart showing the progression used to identify the most stable genes for normalizing results from our candidate gene qRT-PCRs. 100
Figure 3-6: Average real time PCR expression change of eight genes in CWD infected elk compared to the population versus population expression change identified by microarray analysis. 101
Figure 3-7: Real time PCR expression change of eight genes in the 6 individual animal comparisons. 102 List of Abbreviations
A2M Alpha-2-macroglobulin ABCA1 ATP-binding cassette, sub-family A (ABC1), member 1 AIF1 Allograft inflammatory factor 1 Bax Bcl2-associated X protein Bcl-2 B-cell CLL/lymphoma 2 BSE Bovine spongiform encephalopathy CaMKII Calcium/calmodulin-dependant protein kinase II alpha CDK5 Cyclin-dependant kinase 5 CDK5R1 Cyclin-dependent kinase 5, regulatory subunit 1 (p35) cDNA Complementary deoxyribonucleic acid Cebp CCAAT/enhancer binding protein CJD Creutzfeldt-Jakob disease CNP 2,3-cyclic nucleotide 3 phosphodiesterase ctmPrP Transmembrane prion with c-terminal end located in the endoplasmic reticulum CWD Chronic wasting disease dsDNA Double stranded deoxyribonucleic acid Elk-1 Member of ETS oncogene family ER Endoplasmic reticulum Erg-1 Transcriptional factor early growth response 1 ERK1/2 Extracellular signal-regulated kinases 1/2 GABA Gamma-aminobutyric acid GABBR2 Gamma-aminobutyric acid B receptor, 2 GSS Gerstmann-Straussler Scheinker syndrome HNT Neurotrimin ICSBP Interferon concensus sequence binding protein IHC Immunohistochemistry L Leucine M Methionine MAP kinase Mitogen-activated protein kinase mRNA Messenger ribonucleic acid NSF N-ethylmaleimide- sensitivity factor PCR Polymerase chain reaction PrPc Cellular prion protein prpsc Prion disease associated protein RANTES Regulated upon Activation, Normal T-cell Expressed, and Secreted RBP1 Retinol binding protein 1, cellular RNA Ribonucleic acid RT-PCR Real time polymerase chain reaction S100A10 SI00 calcium-binding protein A10 Sc4mol Sterol-C4-methyl oxidase-like Scrgl Scrapie responsive gene 1 SNAP25 Synaptosomal-associated protein, 25kDa SNARE Acronym derived from SNAP and NSF attachment receptors TGF-beta 1 Transforming growth factor-beta 1 TREM2 Triggering receptor expressed on myeloid cells 2 TSE Transmissible spongiform encephalopathy TYROBP TYRO protein tyrosine kinase binding protein VIM Vimentin 1.0. Literature Review: Transmissible spongiform encephalopathies (TSE) and
neurologic disease
1.1. Introduction
Transmissible spongiform encephalopathies (TSEs) are invariably fatal neurodegenerative diseases which affect both humans and animals. The degradation resistant non-traditional aetiological agent and its transmissible nature within and across species have made understanding, treating and controlling these diseases extremely challenging. Knowledge of how the infectious agent leads to the observed changes of the central nervous system still remains limited. This chapter will review the current state of knowledge on prion diseases and the proposed mechanisms responsible for the characteristic changes in the central nervous system as a result of infection.
1.2. TSE disease background
1.2.1. Discovery of TSE diseases
Transmissible spongiform encephalopathies (TSEs), also known as prion diseases are a family of unusual diseases currently believed to be caused by an infectious prion protein, scrapie of sheep was the first recorded TSE dating back to 1732. Research on scrapie pathology and its transmissible nature was important in the identification of prion diseases in humans, scrapie pathology was determined to be similar to that seen in some human neurodegenerative diseases (Hadlow, 1959; Klatzo et al., 1959). These findings prompted work to confirm the transmissible nature of these human neurodegenerative conditions, ultimately grouping kuru and Creutzfeldt-Jacob disease with scrapie as TSEs
(Gajdusek et al., 1966; Gibbs et al., 1968). Since this discovery, Gerstmann-Straussler-
Scheinker syndrome (Masters et al., 1981) and fatal familial insomnia (Lugaresi et al.,
1986) in humans and chronic wasting disease (Williams and Young, 1980), transmissible
1 mink encephalopathy (Burger and Hartsough, 1964), bovine spongiform encephalopathy
(Wells et al., 1987) and feline spongiform encephalopathy (Wyatt et al., 1991) in animals have been added to this group. Despite years of research into the various aspects of these diseases many unknowns still exist making prevention, diagnosis and treatment difficult.
1.2.2. The aetiological agent
Since the identification of scrapie as a transmissible disease, research has been focused on isolating the infectious entity responsible for prion diseases. Suggestions that these diseases were caused by slow or unconventional viruses (Gajdusek et al., 1967), retroviruses (Manuelidis and Manuelidis, 1989) or spiroplasma bacterium (Bastian, 1979,
Bastian, 1993) have not been substantiated by experimental studies. There has also been no success in detecting a polynucleotide linked with infectivity (Alper et al., 1966, Alper etal., 1967, Alperetal., 1977).
In 1982, Stanley Prusiner proposed that the infectious agent responsible for TSEs was a misfolded isoform of a normally occurring cellular protein. Based on experimental results he concluded that infectivity was associated exclusively with the amount of this misfolded protein present in an inoculum and no nucleic acid entity, viral or bacterial components were present or necessary (Prusiner, 1982). Prusiner called the misfolded infectious protein a "prion" and defined it as a "proteinacious infectious particle".
Although this concept does not adhere to the conventional association of nucleic acids with infectivity, the prion hypothesis is the prevailing theory for the cause of TSEs in humans and animals.
2 1.2.3. The infectious prion protein
Prion diseases can be classified into sporadic, genetic or transmissible disease forms.
Sporadic and genetic are the most common forms of prion diseases in humans. Prion diseases related to transmission are most common in animals. Sporadic prion diseases have not yet been linked to a specific cause whereas those of genetic origin are linked to specific abnormalities in the host's prion gene. Transmissible disease can occur by several means including exposure to contaminated equipment or biologicals during medical procedures, ingestion and uptake through the alimentary tract or uptake through surface fissures of the gums, skin and conjunctiva (Kovacs and Budka, 2008).
The infectious protein has an altered secondary structure compared to its normal cellular isoform. The cellular prion protein (PrPc) is composed of approximately 40% alpha- helices and little or no beta-sheet structure whereas the disease associated prion protein
(PrPsc) has a 30% alpha-helices and 45% beta-sheet structure (Pan et al., 1993). This structural change results in a protein that has a propensity to aggregate with itself and is resistant to traditional disinfection methods such as irradiation, heat or chemical treatments (Prusiner, 1998). While the exact mechanisms of misfolding are not well understood, it is believed that the progressive conversion occurs through an interaction between PrPsc and PrPc, possibly by nucleation-polymerization or template assisted misfolding (Prusiner, 2004). This newly formed, protease resistant PrPsc, accumulates in the central nervous system where it is associated with degenerative changes and is used for the diagnosis of disease.
3 1.3. Causes of TSE induced neurologic disease
Prion diseases have a variety of clinical presentations, but they most often involve disturbances in motor coordination and/or cognitive function. These clinical symptoms of prion diseases are a result of the disruption and eventual changes that occur in the central nervous system. The locations and degree of these changes are variable with different prion diseases. All do involve spongiosis, neurodegeneration and microglial and astrocyte activation (Prusiner, 1982). Central nervous system PrPsc accumulation and in some cases aggregation into amyloid plaques also occurs during prion disease (Aguzzi, 2006).
Intensive research into the prion protein, its normal physiological function and how changes to this protein affect normal cellular processes have provided some insights into possible causes of disruption and cell death in prion diseases. Neuronal impairment, involving synaptic changes and neurotransmitter dysfunction, has been shown to occur early in these diseases followed by apoptotic neuronal cell death (Crozet et al., 2008).
Many mechanisms have been suggested to be involved in impairment and neurodegeneration. These mechanisms include oxidative stress, regulated activation of complement, ubiquitin-proteosome and endosomal-lysosomal systems disruption, synaptic alteration and dendritic atrophy, corticosteroid response and endoplasmic reticulum stress (Kovacs and Budka, 2008). Identifying a definitive cause for these changes would be a major breakthrough and provide invaluable information for research in areas such as therapeutics.
4 1.3.1. Neuronal impairment
1.3.1.1. TSE induced synaptic changes
Impairment of neuronal function occurs prior to cell death in a variety of models used to study prion diseases. Studies have shown a loss of dendritic spines in the synapses of human and animal PrPsc infected cells (Fraser et al., 2002). Fraser and his colleagues suggest that these changes disrupt the transmission of electrical signals which are important trophic factors. Further support for this comes from work that has associated
Notch activation, a protein involved in dendrite growth and maturation, with PrPsc infection. It was shown that Notch-1 protein levels increased in relation with PrPsc levels and induced cleavage and other regressive changes to dendritic processes (Ishikura et al.,
2005). PrPc is also implicated in neurite outgrowth through a signal transduction pathway involving the activation of Fyn kinase (Chen et al., 2003). This pathway is dependent on the association of PrPc with caveolin-1 (Mouillet-Richard et al., 2000). During scrapie infection the localization of misfolded PrP is the same as cellular PrP, but changes in the distribution of synaptophysin and caveolin-1 away from PrPc associated lipid rafts is noted (Russelakis-Carneiro et al., 2004). The alteration of this complex could greatly affect its role in signalling neurite outgrowth and disrupt normal neuronal function.
Early synaptic changes induced by prion diseases are also supported by the significant reduction of synaptophysin. Reduction in levels of this vital synaptic protein have been detected in scrapie mouse models as early as 13 weeks post infection (Cunningham et al.,
2003). Reduction of synaptophysin and a number of other crucial synaptic proteins have been identified in sporadic Creutzfeldt-Jakob disease (CJD) (Crozet et al., 2008).
5 Exposure of human embryonic cell lines to prion fibrils have resulted in degeneration of neuronal processes likely associated with changes in synaptophysin localization, microtubule collapse and cytoskeletal protein aggregation (Novitskaya et al., 2007).
Early synaptic alteration in animal and cell culture models in addition to altered expression of crucial proteins for synaptic function in human prion disease provides strong support for the importance of these changes in prion disease progression.
1.3.1.2. Neurotransmitter dysfunction
Damaged neurotransmitter systems have been implicated in neuronal impairment seen in early prion disease infections. Changes to GABAergic neurons, responsible for inhibitory neurotransmissions, have been observed in prion infected cells. A decrease in the release of neurotransmitter GABA (gamma-aminobutyric acid) (Bouzamondo-Bernstein et al.,
2004) and a reduction of GABA receptor-mediated physiological functions associated with PrPsc accumulation suggest the importance of these changes (Collinge et al., 1994).
Altered levels of a number of chemical messengers and neuropeptides important for normal neuronal function have also been noted. Changes in dopamine, norepinephrine and serotonin have been seen in animal models of scrapie (Bassant et al., 1986).
Enhanced activity of the serotonergic system could result in the release of serotonin which can cause attention abnormalities and hypersensitivity (Fraser et al., 2003). Both of which have been seen as early symptoms in some prion diseases. Increases in neuropeptide Y, enkephalin and dynorphin-like immunoreactivities and decreased cholecystokinin-like immunoreactivities noted in scrapie infected mice may also suggest abnormal neuronal activity (Diez et al., 2007).
6 1.3.2. Neuronal cell death
The main cause of neuronal loss in prion diseases is apoptosis (Crozet et al., 2008). The question remains whether this programmed cell death is occurring as an effect of PrPsc
accumulation, a loss of functional PrPc, involvement of some other PrP species or the characteristic immune response. Based on our current knowledge, it has been suggested that neurodegeneration may be linked to disruption of the physiological balance of prion propagation, synthesis and degradation (Weissmann, 2004). Experimental results have shown the presence of neurotoxic markers before PrPsc accumulation (Jamieson et al.,
2001) as well as PrPsc accumulation in microglial cells with no loss of neurons when they do not express PrPc (Mallucci et al., 2003). Others have reported the death of cultured neuronal cells when exposed to PrPsc like proteins (Novitskaya et al., 2006). Conflicting experimental findings such as these have complicated the identification of a specific underlying cause of TSE induced neurodegeneration.
1.3.2.1. Loss of functional PrPc
To determine the role of functional PrPc in neuronal survival, several laboratories created prion protein deficient mouse lines. Initial PrP free transgenic mouse lines developed normally and showed no signs of severe pathology (Bueler et al., 1992, Manson et al.,
1994). These mice were also resistant to prion disease inoculation. A conflict came when a second set of PrP null transgenic mice developed ataxia and Purkinje cell loss (Moore et al., 1999; Rossi et al., 2001; Sakaguchi et al., 1996). This discrepancy was explained by the discovery that the cloning methods used in these transgenic mice did not just knockout PrPc but also placed a downstream prion doppel gene under the control of the
7 prion gene promoter. This caused over-expression of the doppel protein resulting in increased oxidative stress and the observed neuropathology (Aguzzi et al., 2008). With this resolved it is now accepted that functional PrPc is not vital for the survival of mice and that the loss of functional PrPc is likely not the primary cause for neurodegeneration in prion diseases.
With the variety of possible functions of PrPc, the loss or interference of functional PrPc is still considered as a possible contributor to neurodegeneration. The suggestion of an anti-apoptotic role for normal prion proteins is supported by PrPc induced protection of
PrP null mouse cell lines (Bounhar et al., 2001). This protection is believed to be related to a Bax mediated anti-apoptotic role due to the Bcl-2 like properties of PrPc. Proteins of the Bcl-2 family interact with Bax preventing it from signalling the onset of apoptosis
(Burchert et al., 2003). Others have also shown the involvement of PrPc in the control of apoptosis by the mitigation of Bax-mediated apoptosis in human primary neurons
(Roucou et al 2003). Further support for the anti-apoptotic role of PrPc comes from the detection of significant up-regulation of PrPc in breast cancer cells resistant to tumour necrosis factor alpha induced apoptosis (Diarra-Mehrpour et al., 2004). This data suggests that a loss of functional PrPc will likely increase cell susceptibility to apoptosis.
Involvement in copper binding and regulation has prompted suggestions of a protective function of PrPc against metal toxicity or against oxidative stress by enhancing copper/zinc superoxide dismutase activity (Brown and Besinger, 1998). The protective function of PrPc against oxidative stress has been demonstrated by the increased
8 sensitivity of PrP null neurons to oxidative stress (Brown et al., 1997). In addition,
markers of oxidative stress, including nitrotyrosine, heme-oxygenase-1 and lipid
oxygenase markers have been identified in scrapie infected mice and cell culture which
show decreased antioxidant levels and increased susceptibility to free radicals (Brown et
al., 2005). The marked increase in reactive oxygen species linked to prion disease
(Milhavet et al., 2002) may be related to the loss of functional PrPc. These results suggest
that the loss of functional PrPc could increase cell susceptibility to oxidative substances
and jeopardize cell viability.
1.3.2.2. Toxicity of PrPsc
For years it was generally accepted that there is a spatial and temporal association between PrPsc accumulation and neurodegeneration. This supported the idea that this molecule was neurotoxic (Forloni et al., 1993). The development of a transgenic mouse model with successful extracellular secretion of full length PrPc has resulted in re-
evaluation of this theory (Chesebro et al., 2005). These transgenic mice showed no ill
effects from the redirection of PrPc and did not develop neurologic disease when infected with a number of different strains of scrapie despite accumulation of PrPsc. This disease free prion protein accumulation/aggregation suggests that PrPsc accumulation is more an end product of protein misfolding rather than the cause of pathology (Chesebro et al.,
2005).
1.3.2.3. Other PrP species
The discovery of a topological conformer of PrPc involved in the human genetic prion disease Gerstmann-Straussler-Scheinker (GSS) syndrome sparked interest in this protein and its potential involvement in all TSEs. The genetic mutation in the prion protein gene
9 (PRNP), associated with GSS, leads to increased expression of the topological PrP conformer, denoted ctmPrP. This protein, whose c-terminus is located in the lumen of the endoplasmic reticulum can induce cell death in the absence of PrPc expression (Haik et al., 2000). This study demonstrated that peptides which mimic the transmembrane domain of ctmPrP are cytotoxic and this effect is independent of fibril formation.
Normally found at low levels in mice, ctmPrP expression can be induced by exchanging alaninie to valine at positions 113,115 and 118 as well as exchanging lysine 109 and histidine 110 for isoleucine (Hedge et al., 1998, Prusiner and Scott, 1997). Mice expressing the mutated PrP protein develop fatal neurologic disorders. This toxic effect was produced without the formation of enzyme resistant PrPsc, but it has also been shown that induction of prion disease with PrPsc can trigger ctmPrP production (Hegde et al.,
1999; Harrison et al., 2007). Cell death caused by ctmPrP could be attributed to its disruption of cell membrane stability (Sponne, 2004). Reduced apoptosis with the administration of antioxidants to ctmPrP exposed cell culture models suggest that oxidative stress injuries are a contributing factor to cytotoxicity (Sponne, 2004).
Continued research on the importance of ctmPrP may help to provide a feasible explanation for both pre- and post-PrPsc accumulation apoptosis (Mallucci et al., 2007).
1.3.2.4. PrPc relocalization and accumulation
Accumulation of prion proteins in the cytosol is another possible mechanism of prion disease neurodegeneration. A number theories exist for the cause of cytosolic PrP accumulation as well as its effect on cell viability. Compromised proteosome activity has been proposed as a means of cytosolic prion proteins accumulation (Ma and Lindquist,
2001). Exclusion of endoplasmic reticulum involvement in PrPc processing in transgenic
10 mice resulted in PrPc accumulation in the cytosol and ataxia due to degeneration of the
cerebellum (Ma et al., 2002). In the human prion disease GSS, an increase in cytosolic
PrP provides further support for this concept. Individuals with this prion disease have
mutations in the prion protein C terminal domain known to be important for import into
the endoplasmic reticulum (Heske et al., 2004).
It is also possible that impaired protein degradation mechanisms may lead to prion protein accumulation. Support for this stems from notable accumulation of PrPsc in and resulting impairment of the ubiquitin-proteosome systems in prion-infected mice
(Kristiansen et al., 2007). An overall proteosome inhibition has also been suggested as a
cause of intracellular prion protein accumulation (Fioriti et al., 2005). This accumulation may contribute to cell death by binding and inactivating the anti-apoptotic protein Bcl-2, leading to Bax mediated apoptosis (Rambold et al., 2006). However, conflicting theories
exist on the effects of cytosolic PrPc on cell viability. As mentioned above, experimental results have been generated to support the idea that cytosolic PrP actually protects against
apoptosis (Roucou and Leblanc, 2005). More work is needed to resolve these conflicting results and elucidate the relevance of cytosolic PrP in prion disease pathogenesis.
1.3.2.5. Cell system and organelle disruption
Stress of the endoplasmic reticulum (ER) has been implicated in neuronal cell death associated with prion diseases. Exposure to PrPsc has been shown to induce ER stress based on calcium release and caspase-12 activation (Hetz and Soto, 2006). Further support has come from a study which demonstrated ER calcium homeostasis disruption in response to a PrPsc-like peptide and suggests that this is important as a cell death
11 message in prion diseases (Ferreiro et al., 2008). Applying stress to the ER also results in the generation of altered prion isoforms more susceptible to PrPsc replication (Hetz et al.,
2007) as well as misdirection leading to cytosolic prion protein accumulation (Orsi et al.,
2006). These results indicate ER dysfunction as a possible contributor to prion disease pathogenesis.
Cellular endosomal/lysosomal systems are involved in cellular prion trafficking and alterations to these systems as a result of prion infection may contribute to pathogenesis.
Ultra-structural, immunohistochemical, enzymatic activity and gene expression analysis studies of prion disease have all identified alterations to these systems (Aguzzi et al.,
2008). Inhibition of lysosomal movement or of the enzymatic processes which naturally occur in these structures leads to the inhibition of PrPsc accumulation (Doh-Ura et al.,
2000). Increased volumes of cathepsin D-immunoreactive lysosomes have been found in neurons in areas of prominent prion disease related tissue damage (Kovacs et al., 2007).
This evidence suggests the importance of these systems in the progression of disease and the changes observed may contribute to functional disruption and cell death.
1.3.2.6. Involvement of autophagy
The common presence of autophagic vacuoles has led to the suggestion that this process plays a major role in prion diseases (Liberski et al., 2004). The true role of these autophagic vacuoles is yet to be determined and it is possible they could be either harmful or helpful, depending on the condition and timing in which they appear (Rubinsztein et al., 2005). Prion disease associated up regulation of Scrgl (Scrapie responsive gene 1), a protein associated with the golgi apparatus and autophagic vacuoles of degenerative
12 neurons, was suggested to be involved in response to stress and/or neuronal cell death
(Dron et al., 2005). Support for a protective role of these vacuoles in prion diseases
comes from the fact that autophagy in Huntington's disease is believed to be an
alternative mechanism to a compromised ubiquitin-proteosome system to remove
aggregated huntingtin proteins (Menzies et al., 2006). Autophagy may provide a backup
to primary proteasome systems, which are altered during prion disease, in an attempt to reduce protein-related cell toxicity (Menzies et al., 2006; Rubinsztein et al., 2005).
1.3.2.7. Immune/inflammatory involvement
The role of microglial activation in prion disease is poorly understood. Chemokine
RANTES up-regulation through mitogen-activated protein kinase/extracellular signal- regulated kinases 1/2 (MAPkinase/ERKl/2) pathways inducing Elk-1 phosphorylation (a member of ETS oncogene family) and RANTES transcription factor early growth response 1 (Erg-1) expression due to PrPsc exposure in cell culture and animal models results in rapid and early microglial migration (Marella et al., 2005). It has been suggested that activation of these resident immune cells may result in the release of pro inflammatory cytokines, reactive oxygen species, proteases and complement proteins all of which may be cytotoxic (Chiarini et al., 2006). Support for this comes from cell culture studies which have shown that prion like peptides induce the pro-inflammatory cytokines interleukin-1 beta and interleukin-6 (Peyrin et al., 1999) as well as reactive oxygen species such as nitric oxide synthase (Brown et al., 1996). The increased activation of neuronal surface receptor p75 neurotrophin receptor, important for alerting cells of the presence of cytokines and oxidative species, confirms that up-regulation of these molecules may play a role in cell death (Della-Bianca et al., 2001). There is also
13 support for a protective role of microglial cells in prion diseases. When these cells are
ablated from brain tissue slices, the tissue displays an increased susceptibility to infection
as well as an increase in PrPsc levels (Falsig et al., 2008). Taken together, these results
suggest that microglial activation could be an attempt to fend off the infection but when
in combination with some of the other identified changes or when the infection reaches a
certain level this activation may contribute to cell death.
1.4. Summary
Since the initial discovery of scrapie in sheep, the prion disease family has grown to
include a number of neurodegenerative diseases in humans and other animals.
Identification of an infectious protein as the underlying cause of these diseases
contradicts some of the basic principles of molecular biology and traditional infectious
agents and has led to controversial discussions regarding the causative agent and its pathogenesis. Perhaps the most important question which remains unresolved is the underlying mechanisms leading to neurodegeneration in prion diseases. The loss of function of PrPc, toxicity of PrPsc or other prion species, alteration in protein degradation pathways, ER stress, endosomal/lysosomal changes and microglial activation have all been suggested as possible contributors to the dysfunction and subsequent death of neurons in prion diseases. Elucidation of the exact role of each of these changes in prion disease pathogenesis could allow for a more targeted approach to exploring new therapeutic agents and identifying biomarkers for early ante-mortem diagnosis which could make treatment, control and eradication of prion diseases a possibility.
14 This information and research is extremely important for chronic wasting disease.
Increasing endemic areas and incidence rates and a relatively unknown potential to infect other animals and/or humans are of great concern. With our limited knowledge of the mechanisms of neurodegeneration involved in CWD, controlling this disease is a formidable task. The experimental work of this thesis has attempted to elucidate molecular changes occurring in CWD-infected elk. These results will hopefully provide support for the involvement of some previously proposed mechanisms of TSE induced neurologic disease and potentially identify host and/or CWD specific mechanism, if they exist.
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23 2.0. Literature Review: Microarrays for understanding TSEs
2.1. Introduction
Gene expression profiling is used to identify the differential expression of various genes in a given cell, tissue or organism at certain times and/or under certain conditions.
Genome-wide expression profiles have the potential to significantly improve current knowledge about gene functions and provide insight into genetic networks and biological pathways altered under certain conditions (Breyne et al., 2003). Most current gene expression profiling techniques measure messenger RNA (mRNA) levels, also known as transcript quantification, to indirectly determine the levels at which genes are differentially expressed. Some of these technologies include differential display, high throughput sequencing of cDNAs, and DNA microarray analysis.
DNA microarrays use gene specific probes which are immobilized in a known location on a solid surface to measure mRNA levels. Sample transcripts are labelled and hybridized to the immobilized probes and the intensity of each probe spot is measured with a high resolution scanner. The amount of each particular transcript in a sample is determined based on the intensity generated by varying amounts of transcript in the sample. Since first described in the 1990's (Fodor et al., 1991; Schena et al., 1995,
Lockhart et al., 1996), DNA microarrays have evolved substantially. Initially, most laboratories performed DNA microarray experiments on in house slides by spotting cDNA probes often hundreds of base pairs long (Wheelan et al., 2008). With increased affordability, spot density and consistency, it is now more common to use commercially produced oligonucleotide arrays for microarray experiments (Ahmed, 2006). Pre-
24 synthesized oligonucleotides immobilization by companies including ABI and Codelinks
allow for the analysis of tens of thousands of features at a time (Wolber et al., 2006). This
number has been substantially increased by Illumina, which uses pre-synthesized
oligonucleotides immobilized to three micron beads (Fan et al., 2006). This platform
allows for the analysis of over a million features in a single test run. Affymetrix and
Nimblegen utilize in-situ oligonucleotide synthesis and the resulting platforms can
analyze several million features per test run.
Each of the above mentioned platforms is conducive to a specific hybridization scheme.
Both individual and competitive sample analysis are common hybridization approaches
in microarray experiments. Competitive sample analysis can reduce individual
slide/hybridization variability to improve consistency and is useful for the direct comparison of two samples (Alba et al., 2004). This is also the most common approach used for microarray gene expression analysis. Some companies (e.g. Affymetrix) have developed individual sample analysis platforms and by including numerous controls, these have been shown to be extremely consistent as well (Roberts, 2008).
While the affordability has improved, the cost of microarray experiments is still a major disadvantage of this method. Specific reagents and kits, laborious methods requiring significant hands-on time and the purchase of specialized equipment and software add to the cost of this platform (Wheelan et al., 2008). Microarrays have also been criticized for their lack of reproducibility on an intra- and inter-laboratory basis. Lot to lot variability of reagents and slides, different equipment and individual analyst intricies could be potential
25 sources leading to the variability of results (Irizarry et al., 2005; Ahmed, 2006). Finally, microarray analysis has been viewed to be limited to analyzing organisms with extensive sequence information available.
The main advantage of DNA microarrays is its potential to produce expression profiles for entire genomes in a single test run with small sample amounts (Ding and Cantor,
2004). Attaining such a large amount of information from limited sample input often eliminates the need for amplification, which can be a major source of bias (Stoughton,
2005; Roberts, 2008). As technology continues to evolve and more genes are included on a single platform the cost per gene analyzed is decreasing. Standardization of protocols as well as the information included in publications has helped to rectify the consistency problems of microarray experiments (Brazma et al., 2001). Cross species microarray analysis, which involves using a microarray platform designed for one species to analyze samples from a similar species could greatly increase the applications of this technology
(Shah et al., 2004; Yee et al., 2008).
2.2. Microarrays for understanding disease and infection
High throughput microarray gene expression analysis has been used to better understand a variety of biological phenomena at the molecular level. This platform has been well utilized to elucidate mechanisms and pathways involved in a host's response to infection or disease, as well as in chronic conditions. This information can be extremely useful in the selection and development of treatments as well as for diagnosis. Shortly after the first publications describing DNA microarrays, cDNA arrays were used to identify genes with altered expression leading to the tumorigenic properties of the human melanoma cell
26 line UACC-903 (DeRisi et al., 1996). By compiling microarray data showing altered
expression of groups of genes and/or genetic pathways, molecular signatures for
lymphoma (Savage and Gascoyne, 2004), anaplastic thyroid cancer (Onda et al., 2004),
lung cancer (Kuhn et al., 2004), pancreatic cancer (Mahalamaki et al., 2004), gastric
cancer (Wang et al., 2004) and breast cancer have been identified (Sorlie et al., 2000).
By examining gene expression changes in hosts with viral or bacterial pathogen invasion,
we can identify the molecular mechanisms affected by the infection. This information is useful in advancing current knowledge about the host immune response as well as about possible host pathogen interactions which could lead to observed pathology. Recent
studies on unique and dangerous pathogens, including avian influenza and human immunodeficiency virus, have demonstrated the utility of this platform to understand the molecular mechanisms behind various pathologic changes as well as potential means to prevent or minimize these changes (Cameron et al 2008; Bosinger et al 2004; Butchar et al 2008).
Microarrays have also demonstrated utility for elucidating molecular changes involved in a number of neurodegenerative diseases. Array results for Alzheimer's disease (Loring et al., 2001; Colangelo et al., 2002; Emilsson et al., 2006), Parkinson's disease (Hauser et al., 2005; Mandel et al., 2005; Duke et al., 2006), amyotrophic lateral sclerosis (ALS)
(Tanaka et al 2006) and Huntington's disease (Luthi-Carter et al., 2002) have helped to better understand these diseases at the molecular level. The results have provided insight
27 into key mechanisms involved in neurodegeneration and identified potential targets for diagnosis and therapy.
2.3. Microarrays for understanding prion diseases
Until recently, traditional research methods studying transgenic mouse models and cell culture have provided most of our knowledge about prion diseases and the mechanisms involved in neurodegeneration. With the advent of high throughput microarray gene expression analysis, a number of groups have utilized these platforms in attempts to better understand the underlying molecular mechanism(s) which might be responsible for prion disease pathogenesis. These studies have identified genes from a number of pathways altered in the prion disease models examined. While most of these pathways have already been suggested to be involved in prion disease pathogenesis, the microarray studies have significantly improved our understanding of the possible molecular changes that may lead to neuronal dysfunction and neurodegeneration.
2.3.1. Protein folding, trafficking and degradation
Protein misfolding, accumulation and relocalization have all been proposed as important in prion disease neurodegeneration. Genes involved in ubiquitin-mediated protein degradation functions have been shown to be altered in prion disease microarray studies
(Xiang et al., 2004; Sawiris et al., 2007). Analysis of mice inoculated with the 301V BSE strain identified 18 of 296 differentially expressed genes to be involved in the ubiquitin cycle (Sawiris et al., 2007). Gene expression changes in scrapie-infected mouse models have also demonstrated the disruption of the ubiquitin system possibly a result of oxidative stress related to mitochondrial dysfunction (Xiang et al., 2007). These findings are further supported by work which has found aberration of the ubiquitin proteolytic
28 system in other protein related neurodegenerative disorders (Ciechanover and Brundin,
2003; Menzies et al., 2006).
Microarray analysis of scrapie-infected mice and hamsters has demonstrated altered regulation of a number of proteases and protease inhibitors, many of which are linked to lysosomes. This is consistent with the previous observation that activation and alteration of the lysosomal system have been shown as prominent features in prion disease pathology (Laszlo et al., 1992; Aguzzi et al., 2008). Up-regulation of members of the lysosomal protease cathepsin family and of the cysteine protease inhibitor cystatin family have been noted in several prion disease gene expression analysis studies (Xiang et al.,
2004; Brown et al., 2004; Riemer et al., 2004; Sawiris et al., 2007). It is believed that the induction of cathepsins may be a response to the accumulation of PrPsc and cystatin protease inhibitors are then up regulated in response to the increased levels of these proteolytic enzymes (Brown et al., 2004). A similar process may also be occurring during
Alzheimer's disease pathogenesis, which has been shown to significantly involve lysosomal enzyme activation (Nixon et al 2001). Additional support for the importance of altered regulation of these proteases/protease inhibitors in prion disease pathology comes from the fact that cathepsins are capable of inducing programmed cell death (Schotte et al., 1998; Bidere et al., 2003) and cystatins can regulate apoptosis (Nishio et al., 2000;
Nagai et al., 2002), both of which could play a role in prion disease cell death.
Microarray analysis has identified a number of protein folding chaperones with altered regulation in prion infections. Chaperone proteins such as calnexin, clusterin, alpha
29 crystalline B and several heat shock proteins have been shown to be differentially
regulated during a prion disease infection (Brown et al., 2004; Sawiris et al., 2007;
Brown et al., 2005; Booth et al., 2004). These molecular chaperones, along with the
previously mentioned ubiquitin system, are important for the identification and removal
of dangerous abnormal proteins (Finely et al., 2004). The protective function of
molecular chaperones may explain the altered regulation of these proteins during a prion
infection. With accumulating PrPsc or other potentially harmful protein isoforms, cells may be attempting to deal with these potentially dangerous molecules using chaperone
mediated processes.
2.3.2. Immune and inflammatory response
Microglial and astrocyte activation is a local immune response which occurs in all prion diseases (Prusiner, 1982). Scrapie mouse models used for microarray gene expression
analysis have shown altered expression of beta-2-microglobulin, of members of
complement Clq family as well as of other major histocompatability complex proteins which likely play a role in this characteristic immune response (Riemer et al., 2000;
Xiang et al., 2004; Brown et al., 2005). Many of the immune/inflammatory genes which have been identified as differentially expressed in prion disease models are commonly associated with a typical interferon cellular response (Riemer et al.,2000; Riemer et al.,
2004). Interferon gamma has been identified as an important protein in Alzheimer's disease as it appears to be involved in CD40-CD40L activation of microglial cells (Tan et al., 1999). Identification of interferon inducible transcription factor up regulation provides evidence of the interferon response in prion disease infections. One of these, interferon consensus sequence binding protein (ICSBP), down regulates Bcl-2 expression
30 which normally binds with Bax and prevents its pro-apoptotic signalling due to homodimerization (Burchert et al., 2003). A notable increase in the Bax/Bcl2 ratios in prion infected brain tissue (Park et al., 2000) provides support for the possible role for
ICSBP in neuronal cell loss during prion disease. Unfortunately, early prion pathogenesis studies do not support interferon involvement in neuronal degeneration (Gresser et al.,
1983) and no up-regulation of interferons has been detected (Baker et al., 2004). Other mechanisms may be leading to this interferon like immune/inflammatory response.
Further work is warranted to determine the importance and involvement of interferons and microglial activation in prion diseases.
2.3.3. Cellular metabolism
Alteration in the expression of genes involved in normal cellular metabolism has also been noted in studies evaluating the molecular changes associated with prion diseases.
The metabolic process most often altered during a prion disease infection is the cholesterol/lipid metabolic pathway. It has been shown that cholesterol is essential for
PrPc trafficking and subcellular localization, which are necessary for PrPc to convert to
PrPsc (Gilch et al., 2006). Several gene expression analysis studies have identified a general down regulation of genes involved in cholesterol metabolism at late stages of prion disease (Riemer et al., 2004; Brown et al., 2005; Xiang et al., 2007; Sorensen et al.,
2008). Up-regulation of genes involved in cholesterol metabolism were identified in the early stages of scrapie-infected hippocampus (Brown et al., 2005). Research suggesting a link between activated cholesterol biosynthesis and the endoplasmic reticulum stress- associated unfolded protein response (Li et al., 2004; Lee et al., 2004) could relate this increase in metabolism with neurodegeneration. Further support for this is provided by
31 the up-regulation of sterol-C4 methyl oxidase and sterol-C5-desaturase (Brown et al.,
2005). These proteins are vital in the downstream processing of squalene, a cholesterol metabolic intermediate. The significance in the accumulation of this intermediate comes from previous research showing that inhibition of squalene formation prevents PrPsc accumulation and protects neurons from degeneration (Bate et al., 2004). Up-regulation of these enzymes to further process and prevent squalene accumulation may be a cellular response to prevent neurodegeneration and PrPsc accumulation.
The above result of cholesterol metabolism activation was found in only one TSE microarray study. Other studies have demonstrated a general reduction of lipid metabolism and cellular cholesterol levels. The common observation of ATP-binding cassette, sub-family A, member 1 (ABCA1) up regulation is of particular interest (Reimer et al., 2004; Xiang et al., 2007). This gene, which is a potential marker to predict the age of onset in Alzheimer's disease (Wollmer et al., 2003), promotes the efflux of cholesterol out of cells. Down-regulation of enzymes required for cholesterol synthesis, such as sterol-C4-methyl oxidase-like protein (Sc4mol) coupled with ABCA1 up-regulation moving cellular cholesterol out could lead to a major lipid deficits which may be linked to cell death (Xiang et al., 2007). Similarly disrupted cholesterol metabolism in
Alzheimer's disease suggests that the prion disease gene expression analysis studies may have identified a common factor involved in amyloid related neurodegenerative diseases
(Riemer et al., 2004; Brown et al, 2005).
32 2.3.4. Ion and cytoskeletal related genes
Differential expression of genes involved in the regulation and homeostasis of metal and
calcium ion levels have been observed in microarray gene expression analysis studies on
prion diseases (Brown et al., 2005; Skinner et al., 2006; Sawiris et al., 2007). Changes in
ion-related gene expression can lead to altered levels of key physiological molecules
resulting in apoptosis of the affected cells (Skinner et al., 2006). Genes involved in
calcium binding, transport and homeostasis have been shown to be altered in the both
scrapie and BSE infected rodent models (Brown et al., 2005; Sawiris et al., 2007).
Research has shown that PrPc plays a role in both limiting endoplasmic reticulum calcium release as well as mitochondrial calcium uptake (Leo et al., 2005). Aberration in calcium homeostasis in cells inoculated with a scrapie-like PrP peptide also supports the
importance of altered regulation of these genes (Ferreiro et al., 2008). Calcium binding proteins have also been shown to play a role in Alzheimer's disease (Sawiris et al., 2007).
Taken together, this evidence suggests that calcium deregulation may play a central role in the events leading to the characteristic pathology seen in prion diseases.
Members of the SI 00 protein family have been identified to have altered gene expression
levels during a scrapie infection (Xiang et al., 2004). Along with their affinities for calcium, zinc and copper ions, proteins from this family are involved in the regulation of cellular processes including cell cycle progression and differentiation (Heizmann and
Cox, 1998). Proteins of the SI 00 family have recently been shown to be associated with a number of neurodegenerative diseases including Parkinson's and Alzheimer's disease
(Griffin et al., 1989; Muramatsu et al., 2003). Annexins and calponins are SI00 protein
33 interaction partners which are known to be associated with cytoskeletal filaments such as
GFAP and vimentin (Plantier et al., 1999). These genes are frequently found to be differentially expressed in prion gene expression analysis studies (Xiang et al., 2004;
Skinner et al., 2006; Sorensen et al., 2008). This information, combined with the fact that altered expression of vimentin and GFAP profoundly affects neuronal cell maintenance
(Privat, 2003), suggests that altered regulation of cytoskeleton genes could be involved in prion disease neurodegeneration.
2.3.5. Cell signalling
Signalling pathways controlling cell growth, cell differentiation and cell progression have been shown to be disrupted in scrapie-infected rodents. High throughput microarray expression analysis has identified the transcription factor early growth response 1 (ERG1) as being significantly down regulated in the late stages of prion disease (Booth et al.,
2004). This study also reported down-regulation of two other important genes linked to cell differentiation and tissue development (CCAAT/enhancer binding protein (CEBP) and transthyretin) 21 days post infection suggesting an early involvement of these systems in scrapie.
Advanced bioinformatics software has been used to identify important gene networks based on the microarray gene expression analysis results of scrapie-infected tissues
(Sorensen et al., 2008). This software assigns a score to determine the importance of the networks identified based on criteria such as the number of differentially expressed genes involved and the relevance of these genes compared to the rest of the genes identified as differentially expressed. The highest ranked network identified in this study was
34 associated with tissue development and the major interaction partner was transforming
growth factor-beta 1 (TGF-beta 1). This multifunctional cytokine regulates injury and
inflammatory response and is likely up-regulated in response to prion disease
neurodegeneration in an attempt to suppress inflammation (Sorensen et al., 2008). This is
supported by previous findings that TGF-beta 1 induction protects against
neurodegeneration in brain injury (Buckwalter et al., 2006) and suppression leads to
increased neuronal loss and gliosis (Ruocco et al., 1999; Brionne et al., 2003).
Other high throughput gene expression analysis studies have shown changes in genes that
are involved in cellular signalling (Xiang et al., 2004; Xiang et al., 2007). Many of these
signalling proteins are likely of astrocytic origin and part of the immune/inflammatory response. It is therefore difficult to decipher if the altered expression is important for neuronal cell death initiation or whether this is a physiologic response to the damage in attempts to minimize the effects on normal functions. Utilization of new scientific approaches, such as laser capture micro-dissection or single cell gene expression analysis, could determine the source and significance of these differentially expressed signalling genes.
2.3.6. Findings from unique experimental designs
Laboratories using microarray gene expression analysis to study prion infections have devised unique experimental designs to explore the effects of different model systems, of different times post infection and of normal age associated changes on influencing their results. Several studies have analyzed different scrapie strains using microarray analysis.
Genes that are differentially expressed in all of the strains may be important in all prion
35 diseases (Booth et al., 2004b; Skinner et al., 2006). These studies have also identified gene expression changes specific to certain strains which suggests that different strains involve unique pathogenic mechanisms. These strain specific changes were shown to have utility in strain typing and may have useful future applications (Booth et al., 2004b).
Previous authors have acknowledged that gene expression profiles from animals at terminal disease may be influenced by the decreased population of neurons and an increase in astrocytes and glial cells (Booth et al., 2004; Skinner et al., 2006; Sorensen et al., 2008). The expression alterations seen due to the above phenomenon may mask subtle molecular events that are important in pathogenesis. This has been addressed by performing expression analysis on animal models at various time points during the disease incubation (Booth et al., 2004; Riemer et al., 2004; Skinner et al., 2006; Xiang et al., 2007). Groups which focused on time points prior to histopathologic and behavioural changes detected a relatively low number of differentially expressed genes at these preclinical time points compared to clinical disease. This suggests a minimal disruption of normal cell and brain function during disease incubation followed by a very short transition period of rapid and pronounced change (Skinner et al., 2006). Although the number of deregulated genes is minimal during disease incubation these may be important targets for early detection and/or treatment of prion diseases.
TSE microarray studies have compared changes of aging brains with those induced by prion infections. One study found 21% of the differentially expressed genes in scrapie infected brains corresponded with genes found to be altered in aging mice (Lee at al
36 2000; Riemer et al., 2004). This association between prion disease and age-related gene
expression changes suggests common molecular pathway involvement. Further support
for the association of age related central nervous system changes and prion diseases has come from experiments showing a number of genes up-regulated with age are down- regulated in prion disease (Xiang et al., 2007). These findings suggest that the TSE related suppression of genes usually induced to maintain and promote normal brain function could possibly lead to increased neurodegeneration in aging hosts (Xiang et al.,
2007).
2.4. Summary
High throughput microarray analysis has been used to identify altered host gene expression profiles in response to infection with a variety of pathogens and chronic diseases. In TSE research, this method has provided support at the molecular level for involvement of the immune / inflammatory system, protein folding / trafficking / degradation, cell signalling, metabolic function and ion regulation in neurodegeneration.
In addition, these studies have identified altered expression of genes involved in pathways not previously believed to be important. Results from these TSE microarray studies have provided an improved understanding of potential mechanisms for the induction of neurologic disease. They have also identified new targets for therapeutics as well as potential biomarkers for ante-mortem diagnostic tests.
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45 3.0. Project: Identification of differentially expressed genes in CWD infected elk
3.1. Introduction
Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy (TSE) of captive and free ranging mule deer (Odocoileus virginianus), white tailed deer
{Odocoileus hemionus), moose (Alces alces) and elk (Cervus elaphus nelsoni). The clinical features of CWD include a loss of body condition, listlessness, decreased herd interactions, changes in behaviour towards humans, hyper-excitability, nervousness and walking in repetitive patterns (Williams and Young, 1992). Many cervids with CWD also exhibit changes in posture, hypersalivation, polydipsia/polyuria and occasionally signs of ataxia and head tremors (Williams and Young, 1992). Like other prion diseases, CWD has a long incubation period usually lasting from 2 to 4 years followed by a clinical progression lasting a few weeks to months (Salman, 2003). The histologic changes observed in the central nervous system of CWD infected animals include spongiform transformation of the neuropil (vacuolation), neurodegeneration and astrocyte/microglial activation associated with the accumulation of degradation resistant misfolded prion protein (PrPsc). (Williams and Young, 1980; Mabbott and MacPherson, 2006; Aguzzi,
2006). These changes are well defined and are important to confirm the diagnosis of
CWD, however, the pathogenic mechanisms are poorly understood.
Chronic wasting disease (CWD) was first observed in a captive population of mule deer at a Colorado research facility in 1967. The disease was diagnosed as a TSE based on its clinical signs and histopathologic presentation in 1978 (Williams and Young, 1980).
Several years later, elk at the same research facility were also diagnosed with CWD
46 (Williams and Young, 1982). In 1981, CWD was diagnosed for the first time in a free ranging elk in Colorado (Belay et al., 2004). Detection of CWD in a captive elk in 1996 was Canada's first case and the disease was found in free ranging mule deer in Canada a few years later (Belay et al., 2004). CWD testing of hunter killed animals and suspect farmed animals are continually redefining the limits of the infected area which, as of
September 2008, included 15 U.S. states and 2 Canadian provinces
(http://www.aphis.usda.gov/animal_health/animal_diseases/cwd/). A number of characteristics of CWD have contributed to this growing endemic area. These include poorly understood modes of transmission in both captive and free ranging animals and persistence of the infectious agent in the environment.
Although horizontal transmission has been identified as important (Miller, 1998; Watts et al., 2006), a limited understanding of the specifics of transmission makes designing and implementing effective control programs difficult. The characteristic long incubation periods of CWD may result in increased shedding and thus increased potential for transmission. An increase in the preclinical phase of disease in elk with certain prion genotypes could also increase these risks (Hamir et al., 2006). Persistence of infectious
TSE agents in the environment (Georgsson et al., 2006) creates a risk of future outbreaks
(Sigurdson, 2008). Infection of both captive and free ranging animals (Salman, 2003) means that both natural migration and the transport of farmed animals are possible means of CWD dispersion (Watts et al., 2006). Without the implementation of more effective control measures these factors will likely lead to the continued spread and increased incidence of CWD in cervids in North America.
47 At present, the risk of CWD transmission to humans is relatively unknown. Although no definitive evidence has been found for higher levels of human TSE occurrence in CWD endemic areas where hunting and venison consumption are common (Anderson et al
2007), research to define the zoonotic potential of CWD is ongoing (Belay et al., 2004).
As the prevalence and distribution of CWD increases, there is also the risk that the disease may be transmitted to other species such as cattle. CWD infection in a different species could reduce the species barrier which may currently be preventing CWD transmission to humans. With our knowledge of bovine spongiform encephalopathy
(BSE) transmission to humans (Will et al., 1996), CWD in cattle could increase risks to human health. Given these uncertainties, an increased understanding of this disease and the mechanism underlying its pathogenesis is vital. Utilization of new molecular techniques to study CWD will increase current knowledge of this neurologic disease and identify potential targets for early diagnosis and treatment. This insight could be crucial to protect wildlife, livestock and human health.
Molecular techniques such as gene expression analysis provide a global picture of genes and/or functions which are altered due to infection or disease. A number of groups have used microarray technology to elucidate changes at the molecular level due to prion infections (Reimer et al., 2000; Booth et al., 2004; Booth et al., 2004b; Brown et al.,
2004; Riemer et al., 2004; Xiang et al., 2004; Brown et al, 2005; Sawiris et al., 2007;
Xiang et al., 2007; Sorensen et al., 2008). These groups have identified hundreds of genes which are differentially expressed in infected versus normal tissue. Results from these
48 studies have been extremely useful in providing molecular evidence for the
neurodegenerative involvement of mechanisms previously identified as well as
identifying some novel ones. They do, however, have some inherent limitations. All of these studies have analyzed the brains of TSE infected rodents with identical genetic backgrounds often infected by an unnatural means. These model systems have provided a
foundation of knowledge but these findings need to be confirmed in natural hosts infected by natural routes.
In the present study we have examined the gene expression changes resulting from the
CWD infection of a natural host (Rocky mountain elk). Animals were exposed orally and brain stem tissue was analyzed for expression changes. To our knowledge, our study is the first to perform high throughput gene expression analysis on CWD in a natural host infected via a natural route. Several limitations have likely prevented other groups from using microarrays to assess CWD induced gene expression changes in elk. The first of these is that no inbred elk are available for experiments. Previous TSE studies have used inbred rodents which are genetically identical to minimize potential genetic effects on the results. Another limitation is that no DNA microarray chips specific for elk exist and insufficient elk sequence data is available to develop one. To address this problem, we have used a cross species microarray analysis with elk samples analyzed on a bovine
DNA microarray to identify differentially expressed genes in response to the TSE infection. The results of this study provide insight into the effects of genetically distinct animals on microarray results and the potential to use elk sample/bovine microarray cross species microarray analysis. Our results also determine the degree of similarity between
49 microarray results from rodents infected with scrapie or BSE and those generated from
CWD infected natural hosts.
3.2. Materials and methods
3.2.1. Animals
Animal experiments were approved by the Canadian Food Inspection Agency (CFIA)
Burnaby/Lethbridge Laboratory Animal Care Committee and followed the guidelines of the Canadian Council for Animal Care (CCAC). Animal screening, inoculation, clinical observation and sampling were done by Dr. Catherine Graham (CIFA Lethbridge
Laboratory) as part of her doctoral thesis project. Female elk (Cervus elaphus nelsoni) from two farms in central Alberta were used. All animals were transported to the CFIA
Lethbridge Laboratory and placed in the biosaftey level 3 (BSL3) large animal holding facility. The animals were kept in the BSL 3 facility for the whole duration of the experiment. Blood samples were taken to determine their prion genotype (Dr. Katherine
O'Rourke, Washington State University, USA). Codon 132 homozygous methionine (M) and heterozygous methionine (M)/leucine (L) animals were identified. One 132MM homozygote and one 132LM heterozygote were selected for the negative control group.
Three animals in total were infected with CWD, one of which was 132LM and two were
132MM (Table 3-1, page 80). The animals were acclimatized for one week after which they were orally inoculated with lOmL of a 10% homogenate brain homogenate (1 gram tissue equivalent). The two control animals received normal elk brain tissue homogenate while the infected group was fed brain homogenate from confirmed CWD positive elk
(the tissues were kindly provided by Dr. Aru Balachandran, CFIA Ottawa Lab
Fallowfield). Once clinical signs had reached a predetermined level, the animals were
50 euthanized by an overdose of pentobarbital injected intravenously into the jugular vein
and an extensive post-mortem examination was performed. Brain stem tissue
immediately adjacent to the obex was sampled quickly and placed in RNAlater (Ambion,
Cat. No. 7021) to preserve the mRNA profile. After a 24 hour incubation at 4°C the
RNAlater was poured off the samples and the preserved tissues were stored at -80°C until
further examination. Brain tissue from each animal was tested using rapid surveillance
tests including the Prionics Check Priostrip (Prionics AG, Cat. No. 30000) and the
Prionics Check Western (Prionics AG, Cat. No. 12000). The CWD status was confirmed
by histology and immunohistochemistry.
3.2.2. RNA Extractions
Extraction of total RNA from infected and control tissue was carried out using a protocol
provided by Dr. Stephanie Booth (Canadian Science Center for Human and Animal
Health, Winnipeg). Tissues were removed from the -80°C freezer, thawed on ice,
trimmed, weighed and then placed in Trizol (Invitrogen, Cat. No. 15596-018) at a 1:10
w/v ratio. The Trizol/tissue mixture was homogenized using an Omni Tissue
Homogenizer TH115 (Omni International, USA) with Hard Tissue disposable tips (Omni
International, Cat. No. 32750H). The tubes were kept on ice during the homogenization process to ensure a stable temperature. An aliquot of the homogenate was transferred to a new tube and spun to remove cellular debris. These supernatants were mixed well with
chloroform (Sigma, Cat. No. C2432) in a ratio of 1 part chloroform to 10 parts
supernatant and centrifuged to separate the aqueous layer. The aqueous phase was transferred to new tubes and an equal volume of 70% ethanol (Commercial Alcohols,
Cat. No. 432526) was added. RNA from this mixture was extracted using the Qiagen
51 RNeasy Extraction Kit (Qiagen, Cat.No. 74106) following the manufacturers instructions.
The kit column bound total RNA was eluted in 50uL of nuclease free water and immediately quantified using a Nanodrop 1000 Spectrophotometer (Thermo Fisher
Scientific, Canada). Based on the quantification, 2p.g aliquots of RNA were prepared and stored at -80°C until needed.
3.2.3. Amplification and labelling of mRNA
3.2.3.1. Target preparation
Messenger RNA (mRNA) from the purified total RNA samples were amplified and labelled using the Ambion Amino Allyl Message Amp II aRNA kit following the manufacturer's instructions (Ambion, Cat. No. AMI753). Briefly, mRNA was converted to single stranded complementary DNA (cDNA) by reverse transcription using T7 oligo dT primers. This product was used as the template to generate double stranded DNA
(dsDNA) in a second strand synthesis reaction. The dsDNA was column purified and used as the template for in-vitro transcription to produce antisense RNA (aRNA) copies of the dsDNAs. In this reaction amino allyl modified uracil residues were incorporated into the in-vitro transcribed RNA molecules to use for dye labelling the targets. The aRNA was column purified and then quantified using a Nanodrop 1000
Spectrophotometer.
3.2.3.2. Target labelling
Based on the quantification results, 15ug of aRNA was transferred to a new tube and vacuum dried. For samples that were pooled for comparison, a total amount of 15ug was obtained by combining equal amounts of the pool components (2 sample pool = 7.5ug sample 1 and 7.5ug sample 2). Once completely dry, the aRNA was resuspended in a
52 coupling buffer (Ambion, Cat. No. AMI753) and mixed with NHS conjugated Cy3 or
Cy5 dye (GE Healthcare, Cat. No. Ql3104 or Ql5104) in DMSO (Ambion, Cat. No.
AMI753). Samples were incubated in the dark to allow the NHS-dye to bind to the amino allyl groups on the uracil nucleotides of the aRNA. The coupling reaction was quenched with 4M hydroxylamine (Ambion, Cat. No. AM 1753). Dye-labelled aRNA samples were then column-purified. To determine the amount of aRNA recovered and evaluate the efficiency of dye coupling, the purified samples were analyzed using the Nanodrop 1000
Spectrophotometer.
Using the dye labelled aRNA quantification results, 5ug of each labelled target was transferred to a new set of tubes. Samples were fragmented using Ambion fragmentation reagents (Ambion, Cat. No. AM8740). The 5ug aliquots of fragmented, dye labelled aRNA were diluted in hybridization buffer. For comparative hybridization, two selected samples were mixed together and applied to a single pre-treated bovine oligonucleotide microarray slide under a clean lifter slip. In total, 10 hybridizations were set up. These included each of the individual CWD elk labelled with Cy 3 versus the pooled control elk labelled with Cy 5 and the dye swaps of these comparisons (6 hybridizations). Then each of the individual control elk labelled with Cy 3 versus the pooled CWD elk labelled with
Cy 5 and the dye swaps of these comparisons of these were run (4 hybridizations) (Figure
3-1, page 96).
3.2.3.3. Hybridization and washing
Microarray slides with comparative samples loaded were placed in a sealed hybridization chamber (Genetix, Cat. No. X2530) and incubated for 20 hours at 42°C. The bovine
53 specific oligonucleotide DNA microarray used in this study was Bos taurus (bovine)
AROS™ Vl.l containing 70-mer oligos representing 8329 genes from the Bos taurus
genome (OPERON Biotechnologies, Inc., Huntsville, AL, USA). Gene sequences used
for probe design were obtained from TIGR Cattle Gene Index Release 11
(http://www.tigr.org/tigr-scripts/tgi/T_index.cgi?species=cattle) and GenBank. This array
was printed in duplicate on each slide by the University of Alberta Bovine Genomics
Laboratory. After the hybridization incubation, the slides were washed in three wash
solutions with increasingly stringent conditions. Washes 1 and 2 were done in SSC/SDS
(Ambion, Cat. No. AM9763/AM9820) buffers. The slides were washed twice in each
buffer with a gentle agitation. The slides were then washed in a final SSC wash solution
containing no SDS, dried by spinning, and then scanned and analyzed.
3.2.4. DNA Microarray expression analysis
Slides were scanned using the Genetix aQuire microarray scanner (Genetix, U.S.A.) at
5um resolution. The photomultiplier tube (PMT) gain was set at 55% for Cy5 and 60%
for Cy3. Spot finding, background determination and preliminary statistical data
generation were performed using Qscan software version 1.1 (Genetix, U.S.A.).
Statistical analysis to identify differentially expressed genes was done using Genesifter
version 2.4.1 (VizX Labs, U.S.A.). Background-subtracted median fluorescence intensities ratios were uploaded for each slide. The data was normalized using intensity- dependent lowess normalization (Yang et al., 2002) and grouped to compare CWD elk gene expression profiles against normal gene expression profiles. The comparisons were limited to probes with a quality value of 10 or greater (signal to noise ratio). Only genes
54 with a p-value less then or equal to 0.001 as determined by t-test analysis and a
differential expression level of 1.5 or greater were identified.
The same criteria were used when comparing select control and CWD elk. By comparing
each control elk (n=2) against each CWD elk (n=3), six additional differentially
expressed gene lists were generated. These six lists were colour-coded, combined into
one list and sorted to determine genes differentially expressed in all of the CWD elk.
Comparisons involving the individual prion protein geneotypes were also done. These
included comparing the 132MM control animal versus the 132LM control animal, as well
as comparing each of these individual control animals against the 132LM CWD elk, the
combined and individual 132MM CWD elk and against all of the CWD elk. The genes
differentially expressed in all of the CWD elk (from the above mentioned compiled 6 lists) were grouped based on functional annotation provided by Genesifter and PubMed database searches (www.ncbi.nlm.nih.gov/sites/entrez). Involvement of these genes in important cellular pathways was identified by KEGG (Kyoto encyclopaedia of genes and genomes) pathway analysis using Genesifter. Genes were selected for quantitative real time polymerase chain reaction (qRT-PCR) verification based a possible functional relevance to or involvement in neurologic disease.
3.2.5. Real time qRT-PCR verification
3.2.5.1. Primer design and testing
Eight genes were selected for qRT-PCR verification of the microarray data. Sequence data from a variety of species for the selected genes were obtained from PubMed searches (www.ncbi.nlm.nih.gov/sites/entrez) and aligned using Clone Manager version 9
55 (Scientific and Educational Software, U.S.A.). With only limited information available
for elk gene sequences, these alignments served mainly to identify conserved regions in a number of species. These regions were then used to design primers and Taqman MGB
(minor groove binding) probes for qRT-PCR analysis with the help of Allele ID version
4.0 (Premier Biosoft International, U.S.A.).
Once primers and probes were designed the primer pairs were tested on elk cDNA to determine if they would generate a specific product of the proper size. If the primer pair successfully amplified the elk template, the PCR product was gel purified using the
Qiagen Gel Extraction kit (Qiagen, Cat. No. 28106) following the manufacturers instructions. The purified products were sent for sequencing to the University of Alberta
Bovine Genomics Laboratory (Dr. Stephen Moore, University of Alberta, Edmonton) to ensure the amplified product was in fact from the gene of interest. This sequence information was also used to verify that the previously identified qRT-PCR probe sequence matched exactly with its template target. If differences existed between the elk sequence and the original probe sequence, the probe was modified before it was ordered.
If the original primer pair was unsuccessful, the sequence alignments were revisited and alternative primers were designed and tested.
3.2.5.2. Selection of house keeping genes for qRT-PCR analysis
A number of candidate genes for normalization of the qRT-PCR analysis were obtained by reviewing qRT-PCR studies on brain tissue (Garcia-Crespo et al., 2006; Bonefeld et al., 2008; Coulson et al., 2008) (Table 3-2, page 81). A similar process was used to design and test primers and probes for the housekeeping genes as was used for the
56 differentially expressed genes. Successful primer/probe sets were then tested in qRT-PCR on cDNA generated from the 3 CWD elk and 2 control elk brain stems. Each gene was analyzed in triplicate with each cDNA and the individual cycle threshold (Ct) results were averaged. These average Ct values were converted to quantities using the comparative Ct method. These values were input into GeNorm version 3.5 (Ghent
University Hospital Center for Medial Genetics, UK) (Vandesompele et al., 2002) to determine which genes were the most stable in the uninfected and infected elk. After the analysis was complete, the identified genes were included to normalize the differential expression in the selected genes of interest identified by the microarray analysis.
3.2.5.3. qRT-PCR testing and efficiency
All primer and probe sets were run in the qRT-PCR system to ensure their performance.
After this verification, a dilution series of elk cDNA template was tested using qRT-PCR with each of the primer/probe sets. The results were graphed and the equation of the line was generated using Microsoft Excel version 10 (Microsoft, Canada). The slope of this line was used to determine the efficiency of the PCR. These values can affect qRT-PCR expression analysis results and are necessary to calculate the level of differential expression using the Pfaffl method (Pfaffl et al., 2001).
3.2.5.4. qRT-PCR analysis of CWD elk
Quantitative RT-PCR verification was carried out using separate cDNA synthesis and qRT-PCR reaction steps. Superscript II reverse transcriptase (Invitrogen, Cat. No. 18064-
014) and oligo dT primers (Invitrogen, Cat. No. 18418-012) were used to generate cDNA copies of the mRNA from each of the total RNA samples. This cDNA was used as the template for the qRT-PCR reactions.
57 Real time PCR product amplification was primed by gene specific oligonucleotide primers (Invitrogen) and detected using gene specific FAM or VIC labelled TaqMan
MGB probes (Applied Biosystems). Reactions were set up using Universal TaqMan PCR master mix (Applied Biosystems, Cat. No. 4304437) and reactions were run using a
Stratagene MX3005p instrument (Stratagene, USA). Each gene to be analyzed was tested in triplicate and the average Ct value was used for subsequent analysis. Quantitative RT-
PCR reactions for housekeeping genes were also included in triplicate and these values were used to normalize the Ct values for the differentially expressed genes. Eight genes were analyzed using qRT-PCR to verify the DNA microarrays results. These genes included alpha-2-macroglobulin (A2M), vimentin (VIM), TYRO protein tyrosine kinase binding protein (TYROBP), SI00 calcium-binding protein A10 (S100A10), allograft inflammatory factor 1 (AIF1), 2,3-cyclic nucleotide 3 phosphodiesterase (CNP), neurotrimin (HNT) and retinol binding protein 1 cellular (RBP1) (Table 3-3, page 81).
The first four genes have been identified to be differentially expressed in previous TSE microarray studies while the second four have not been. CNP and HNT were identified as down-regulated and the other six genes were identified as up-regulated in the CWD elk by microarray analysis. Quantitative RT-PCR results were analyzed using the Pfaffl method (Pfaffl et al., 2001) with the assistance of REST 2005 version 1.9.12 (Corbett
Life Science, Australia) (Pfaffl et al., 2002).
58 3.3. Results
3.3.1. Confirmation of disease status
Animals challenged with CWD positive brain homogenate were allowed to incubate until the development of overt clinical signs. These included a slight decrease in body condition and/or neurologic signs indicative of CWD or animals that posed a risk to themselves or handlers due to behavioural changes. Animals # 8 (132MM) and animal
#28 (132LM) were euthanized 738 days post inoculation and animal #37 (132MM) was euthanized 640 days post inoculation. At day 738 post inoculation animal #28 (132LM) was not presenting the same degree of clinical signs as the 132MM animals when they were euthanized. Due to animal care guidelines, which prohibited isolating single animals, it was euthanized at the same time as its last pen mate (#8 (132MM)). Control animals #6 (132MM) and #31 (132LM) were euthanized 752 days post inoculation
(Table 3-1, page 80). Rapid tests for TSEs detected PrPsc in all of the CWD inoculated animals and none in the control elk. CWD status was also confirmed by histological and immunohistochemical (IHC) examination (Dr. Catherine Graham, CFIA Lethbridge
Laboratory). Animal #28 (132LM) had a considerably lower amount and less wide spread distribution of PrPsc based on rapid test results and IHC examination.
3.3.2. Microarray results
3.3.2.1. Population comparison microarray results
When all the control elk expression profiles were grouped and compared to the grouped
CWD elk expression profiles, 287 genes were identified as differentially expressed
(Table 3-4, page 83). Of these 287 differentially expressed genes 147 were up-regulated and 140 were down-regulated. These genes were grouped based on their common
59 molecular and biological functions using Genesifter. The percentage of these 287 genes involved in various molecular and biological functions strongly correlates with the percentage of genes involved in these different functions represented on the microarray
(Figure 3-2, page 97; Figure 3-3, page 98).
3.3.2.2. Selected animal comparison microarray results
Six differentially expressed gene lists were generated, one for each of the individual animal comparisons. Each of the individual gene lists was colour-coded and compiled into a single list. The lists were sorted based on gene name and the genes common to 6 of
6, 5 of 6 and 4 of 6 comparisons were used to create a new list. The actual values of differential expression were not included in the tables listing genes identified in multiple individual animal comparisons because these values may be affected by factors such as genetic homology. This could result in values which are not exclusively representative of altered transcript levels. With genetic effects potentially influencing the values, we focused our attention on genes differentially expressed in multiple comparisons, in the same direction and > 1.5 fold. For the genes identified in 5 of 6 or 4 of 6 comparisons, the individual animal comparison(s) not identifying the gene as differentially expressed were noted (Table 3-5, page 90).
The above analysis resulted in some interesting patterns. Of the 55 genes identified in 5 or 6 of 6 comparisons, 22 were found in 5 of 6 comparisons. When identifying which comparison was missing from each of these 22 genes, 15 were missing one of the animal
#28 (132LM) comparisons. When analyzing the 52 genes identified as differentially expressed in 4 of 6 comparisons, significant differences between the 132MM animals and
60 the 132LM animal were also seen. Genes differentially expressed only in the two 132MM
CWD elk accounted for 36 of the 52 genes while only 8 genes were common in #28
(132LM) and one of the two 132MM CWD animals (#8 or #37).
Further analysis was performed on the 55 genes identified as differentially expressed in 5 or 6 of 6 comparisons. When looking at the direction of differential expression, 27 of 55 genes were up-regulated and 28 of 55 genes were down-regulated in the CWD elk.
Sorting these genes by specific function resulted in the identification of several groups with multiple genes affected including cytoskeletal genes, immune/inflammatory genes, calcium related genes and genes important to neuronal function (Table 3-6, page 93). In addition, eight of the 55 genes were also identified to be involved in one or more KEGG pathways (Table 3-7, page 95).
3.3.2.3. Genetic effects on microarray results
Comparisons of elk with different prion protein genotypes were done to identify the significance of the different genetic backgrounds on the genes identified as differentially expressed. Comparing microarray results from the two control animals against each other,
19 genes were identified that were differentially expressed (Figure 3-4, page 99). Control animal #31 (132LM) was compared to the different CWD positive groups, including the
132LM CWD elk, the 132MM CWD elk individually and combined and all of the positive animals grouped together. These results identified more genes as differentially expressed than when comparing the 132MM control animal. Comparing the individual control animals against the different CWD elk prion genotype groups identified approximately 50% less differentially expressed genes in the LM CWD elk. When
61 comparing the particular genes identified as differentially expressed from each of the 4
CWD elk prion genotype groups it was noted that around 70% of the genes specific to each group were common in comparisons to the 132MM and the 132LM control animals
(Figure 3-4, page 99).
3.3.3. qRT-PCR results
3.3.3.1. House keeping gene selection and efficiency determination
Eight genes were selected as possible housekeeping genes based on their sequence availability, but only 4 of the 8 primer sets amplified a specific product with the elk cDNA template (Table 3-2, page 81). These four candidate housekeeping genes were tested in qRT-PCR and the results were analyzed using GeNorm (Vandesompele et al.,
2002). Housekeeping genes included ribosomal protein L12 (RPL12), ATP synthase, cyclophilin and 18s ribosomal RNA. GeNorm identified cyclophilin and RPL12 as the most stable in the elk experimental model (Figure 3-5, page 100). Both genes were used to normalize the results from the differentially expressed genes of interest.
The Pfaffl method was selected to calculate gene expression changes from the qRT-PCR data. This method incorporates and accounts for the differences in PCR efficiencies for each primer/probe/template combination (Pfaffl et al., 2001). Amplification efficiencies were determined for all differentially expressed genes of interest and the housekeeping genes. The efficiencies ranged from 0.90 to 0.98 and were incorporated into the calculations to determine levels of differential expression (Table 3-3, page 82).
62 3.3.3.2. Validation of candidate genes selected from microarray analysis using qRT-PCR
Validation of a subset of differentially expressed genes identified using the DNA microarray was performed with taqman qRT-PCR assays. When the qRT-PCR fold change results were averaged for all six comparisons, all eight of the candidate genes corresponded with the results from the microarray analysis (Figure 3-6, page 101).
However, the degree of differential expression did vary from those produced by the microarray analysis. This is a common finding in studies comparing microarray results with those generated by qRT-PCR (Etienne et al., 2004).
The qRT-PCR results for all eight differentially expressed genes in each individual animal were also evaluated. These results corresponded well with the microarray results in terms of the general direction of differential expression with some variation in the levels of differential expression compared to the microarrays. The comparisons of each of the negative animals against CWD positive animal #28 (132LM) were the ones with the least agreement. In comparing CWD negative animal #6 (132MM) with animal #28
(132LM), 3 of the 8 genes showed altered expression in the direction expected and only 2 had a greater than 1.5 fold change. This was also the case when the control elk #31
(132LM) was compared with animal #28 (132LM) in which 6 of 8 qRT-PCR results demonstrated differential expression in the expected direction with 5 of 8 greater than a
1.5 fold change.
Result from the qRT-PCR analysis of the 132MM CWD elk (#8 and #37) were more in agreement with the microarray results. When comparing CWD elk #37 against the two
63 control animals (#6 and #31)7 of the 8 genes tested agreed with the microarray results.
The qRT-PCR results generated by comparing CWD elk #8 against the control animals
(#6 and #31) showed agreement for all 8 of the genes tested (Figure 3-7, page 102).
3.4. Discussion
3.4.1. Disease status at sampling
All CWD inoculated animals displayed some clinical signs of CWD and the disease status was confirmed by the detection of PrPsc in molecular tests and immunohistochemical staining and by histological examination. Animals with different prion protein genotypes were included into the primary project (Dr. Catherine Graham's doctoral thesis project) studying CWD pathogenesis. In a natural situation it has been found that 132MM elk represent the highest proportion of animals infected with CWD
(O'Rourke et al., 1999). In experimental infection, longer incubation periods for 132LM animals were demonstrated (Hamir et al., 2006). In this pathogenesis project, the 132LM animal was included to determine the impact of genotype on the pathogenesis including a reduced susceptibility or extended incubation period (Table 3-1, page 80). However, our animal care guidelines prevented letting this animal progress to a similar level of disease as the 132MM animals. This has made it difficult to determine if unique gene expression changes seen in the LM or MM animals might be due to the different stage of disease. In addition, it is unclear if these changes are a genotype specific CWD response or if they are related to other genetic factors.
3.4.2. Microarrays
The list of genes identified as differentially expressed when comparing the combined
CWD elk microarray profiles against the combined control elk profiles contained 65% of
64 the genes identified in 4 or more of the 6 individual elk comparisons. Over 70% of the
differentially expressed genes identified in these lists have been assigned a functional
annotation which could provide further insight into this disease. The remaining genes,
although currently not well understood, may be important in the future as the number of
annotated genes and the amount of functional information is always increasing.
In comparing the annotated genes identified in this microarray experiment to previous
animal TSE microarray studies, we found that over 50% are the same or similar. This
provides support for the validity of our results as well as for the use of the cross species microarray analysis for this application. The differences in the findings of our study
compared to previous work could be attributed to several factors. To our knowledge, this
is the first study to attempt microarray gene expression analysis on chronic wasting
disease and one of the few experiments in a natural host. Other animal TSE microarray
studies have used inbred rodent models infected with scrapie or BSE, different protocols
and different microarray platforms with varying sensitivities and genes. Others have also
found that different model systems, protocol and platform might be possible sources of unique results (Brown et al., 2004; Xiang et al., 2004; Xiang et al., 2007). Specific factors which may explain our results are related to the genetic similarity/disparity of elk to the bovine microarray probes and the genetic differences between the elk. Each of these factors could result in unrepresentative signal intensities leading to the identification of unaltered genes or missing some altered genes all together. In other studies, these variables are addressed by using genetically identical rodents and species specific microarrays, neither of which was available for our study. But by focusing on genes
65 differentially expressed in most or all of the individual elk combined with the degree of similarity to previous results, provides some confidence in the validity of our data.
3.4.2.1. Effects of analyzing a genetically diverse population
To determine the potential impact of the genetically diverse elk population on our microarray results, a number of expression profile comparisons were done. Comparing the microarray profiles of the two control elk demonstrated differences in the uninfected gene expression baseline. The effects of this became evident when comparing the individual control elk against the 132LM CWD elk, the 132MM CWD elk and the combined CWD positive elk. These results support the idea that genetic background of each elk does have an effect on the total number and specific genes identified as differentially expressed. The higher total numbers of differentially expressed genes identified in the 132MM CWD elk is probably due to their advanced disease stage compared to the 132LM CWD elk. However, only 70% of the differentially expressed genes were common to each of the different CWD positive group comparisons (Figure 3-
4, page 99). This suggests that at least 30% of the genes identified could be related to genetic effects and may not be disease specific.
3.4.2.2. Effects of the prion protein genotype on the differential expression of genes
The inclusion of different prion protein genotypes in the animal experiment did not only affect the level and distribution of PrPsc in the elk, but also generated interesting results in the microarray comparisons. Compiling genes differentially expressed in 4 or more of the
6 individual animal comparisons resulted in the identification of 107 genes. Prion genotype specific effects become apparent when determining the specific elk comparisons missing in the list of genes identified in 4 of 6 and 5 of 6 comparisons
66 (Table 3-5, page 90). When we determined which of the comparisons were missing from these two groups of genes, almost 70% were missing one or both of the animal #28
(132LM) comparisons. This result demonstrates a distinct difference in the results generated in the two 132MM CWD elk compared to the 132LM CWD elk. The similarities observed between the two MM animals may be representative of the comparable stage of disease when these animals were euthanized. Another possibility is that the molecular mechanism of disease progression and/or the response to the pathologic changes are generally different in MM elk compared to LM elk. The extended incubation period seen in our 132LM animal is similar to other studies (Hamir et al.,
2006) and supports the potential involvement of other mechanisms or pathways leading to disease. A final factor contributing to these differences is the fact that each elk could have a different degree of homology to the bovine probes as well as to each of the other elk. Analysis of a greater number of elk with different prion protein genotypes, euthanized at the same stage of clinical disease and comparing the samples individually on microarrays could decipher the importance of each of the discussed factors.
3.4.3. Quantitative RT-PCR
Quantitative RT-PCR verification of a small sample of genes is generally accepted as confirmation of microarray results. With the possible genetic factors affecting the elk microarray results, we used eight genes for real time PCR verification. Quantitative RT-
PCR confirmed the microarray results for all eight genes tested when averaging the results for the six individual animal comparisons (Figure 3-6, page 101). When taken separately, the individual animal qRT-PCR results also support the microarray results
(Figure 3-7, page 102). Only 2 of 8 and 5 of 8 genes were identified as differentially
67 expressed >1.5 fold when comparing the expression levels in the two control animals against CWD elk #28 (132LM). For the two CWD positive 132MM elk, 7 of the 8 genes matched the microarray results in their direction of differential expression in all comparisons. Only S100A10 generated an unexpected result when comparing control elk
#31 and CWD elk #37. Results of the individual animal comparisons for S100A10 and neurotrimin seem to be very dependant on which control animal they are compared against. Different base line expression levels or animal specific sequence variations are possible explanations for these results. Sequencing of these genes in the individual animals could provide explanations for this result.
The level of differential expression of retinol binding protein in the qRT-PCR was low in most of the comparisons compared to the microarray results. However, the agreement between qRT-PCR and microarray expression analysis results can be variable and are gene dependant (Etienne et al 2004). It should also be noted that in the microarray experimental design, expression changes were determined by comparing pools of positive or negative samples against an individual sample from the opposite group, whereas in the qRT-PCR each sample was analyzed individually. Taking this into account could explain the differences seen between the two methods.
The qRT-PCR results provide support for the validity of the elk microarray results and provide further support for the utility of a bovine DNA microarray for gene expression studies in elk. By using a balance of previously identified differentially expressed genes, genes unique to this study and up- and down-regulated genes we were able to show
68 support for these results generated by the microarray analysis. The qRT-PCR results generated for S100A10, neurotrimin and retinol binding protein 1 reaffirms the critical importance of this step to verify microarray results. This is especially true when using genetically unique animals and cross species microarray analysis.
3.4.4. Significance of differentially expressed genes
Annotation and grouping of altered genes with similar functions provides insight into the molecular mechanisms affected during a CWD infection. Two lists of genes were identified, one from the population comparison and the other from the individual animal comparisons. The sorting of population versus population gene list into broad biological and molecular functional groups did not provide much information about specifically altered functions. This could be due to the fact that the microarray platform used contains probes representing a large number of genes involved in cell and metabolic processes, but a relatively small number of genes likely to be involved in CWD or disease in.general.
The use of a microarray containing more probes representing genes thought to be involved in prion diseases might better identify specifically altered categories. Additional factors including the cellular diversity of brain tissue and pooling samples for analysis may have also contributed to the results generated when comparing the control elk group against the CWD elk group.
To minimize the effects of these limitations, we have focused on the implications of the genes with altered expression in 5 or 6 of 6 individual animal comparisons. By focusing on these altered genes, the confidence increases that these changes are due to disease and not identified because of probe/animal or animal/animal genetic effects. When organized
69 according to function, the majority of these differentially expressed genes were involved with cytoskeleton formation, immune/inflammatory response, synaptic function/neurotransmitter release and calcium ion regulation/response.
3.4.4.1. Structural genes
Of the 55 genes with a 1.5 fold or greater expression change, 12 are cytoskeleton related genes. These include previously identified genes such as vimentin, members of the neurofilament and thymosin families as well as several unique genes including syndecan binding protein, stathmin-like 4, coactosin-like 1 and members of the tubulin family.
These genes are linked to microtubule and actin complex formation either as components or regulators of their formation. Actin cytoskeleton is involved in counteracting the toxicity of yeast prion proteins, Sup35 by promoting aggregation of the accumulating prion protein and thus reducing the cytotoxicity of these proteins (Ganusova et al., 2006).
The up-regulation of actin cytoskeleton promoting proteins in infected elk could provide support for similar mechanisms in animal prion diseases.
Microtubules are important structural proteins involved in intracellular transport, metabolism and cell division, and their disruption can cause cell cycle arrest and apoptosis (Mollinedo, 2003). Prion proteins are known to interact with the tubulin components of microtubules (Keshet et al., 2000) and a recent study suggests that these interactions may be vital for cell survival (Dong et al., 2008). These studies have shown that the prion protein binds tubulin and can alter the formation of microtubules. With evidence that prion proteins accumulate in the cytosol in human prion disease (Heske et al., 2004) and that prion disease related endoplasmic reticulum dysfunction leads to
70 increased cytosolic PrP (Ma et al., 2001), an alteration of the microtubules could occur to
such a degree that they can no longer function. Further support for alteration of microtubule dynamics in prion diseases comes from the finding that a PrPc point mutation
linked to a human prion disease (Gerstmann-Straussler-Scheinker syndrome) disrupts the binding affinity of the prion protein for tubulin (Brown, 2000). Our microarray results demonstrated an altered regulation of several tubulin/microtubule-regulating genes, including tubulin beta and stathmin-like 4. The possibility exists that this could be in response to changes in CWD infected cells and the disruption of this system could be important in elk CWD induced neurodegeneration.
Cyclin-dependent kinase 5, regulatory subunit 1 (p35) (CDK5R1) is a neuronal kinase which phosphorylates a number of neuronal cytoskeletal proteins. This enzyme plays a role in the hyperphosphorylation of micro tubule-associated tau proteins which leads to aggregation and eventual neuronal death in Alzheimer's disease (Pei et al., 1998). The activation of cyclin-dependant kinase 5 (CDK5) requires its regulatory subunit CDK5R1.
Cleavage of this subunit by calpain results in a degradation resistant fragment (p25) which can prolong activation of CDK5 (Kusakawa et al., 2000). We detected a down- regulation of p35 in the CWD elk brains. This suppression may be a cellular response to p25 hyperactivation of CDK5 during disease and an increase in the activation of CDK5 could lead to hyperphosphorylation of vital cytoskeletal components. This may alter their ability to form functional end products and could contribute to cell death.
71 3.4.4.2. Immune/inflammatory response and cell signalling
A distinct immune/inflammatory response has been identified in previous prion diseases microarray studies (Reimer et al., 2000; Booth et al., 2004; Brown et al., 2004; Reimer et
al., 2004; Xiang et al., 2004; Brown et al., 2005). Some suggest that these results are due to microglial and astrocyte activation in response to neurodegeneration, whereas others believe that this activation is a key element in neurodegeneration (Crozet et al., 2008).
Activation of these resident immune cells can trigger the release of proinflammatory cytokines, reactive oxygen species, proteases and complement proteins, all of which can be cytotoxic and lead to neurodegeneration (Chiarini et al., 2006). Our microarray results determined that 8 of the 55 differentially expressed genes are involved in immune and/or inflammatory response. Several of these genes play a role in sending and/or receiving signals critical to immune and inflammatory responses. More specifically, the induction of chemokine (C-X-C motif) ligand 14 was noted in the CWD infected tissues. This signalling protein is released to attract cells of the immune system, including natural killer (NK) cells (Starnes et al., 2006). These NK cells can induce enzyme mediated apoptosis and may contribute to immune system mediated death of neurons infected with
CWD.
We found that TYRO protein tyrosine kinase binding protein (TYROBP) was also induced in the CWD infected elk. This protein forms a complex with TREM2 (triggering receptor expressed on myeloid cells 2) which plays a role in maturation and survival of dendritic cells (Bouchon et al., 2001). Dysfunctions associated with this protein are known to lead to neurodegeneration and dementia (Kiialainen et al., 2005). Up-regulation
72 of TYROBP in CWD elk could be a protective response elicited by cells of the host in an
attempt to mitigate factors jeopardizing neuronal function and survival. In addition, the
KEGG pathway analysis determined that TYROBP is involved in the natural killer
mediated cytotoxicity pathway. This provides further support for the potential
involvement of natural killer cell mediated apoptosis in CWD induced cell death.
Up-regulation of osteopontin/secreted phosphoprotein 1, has been identified in our as
well as in other TSE microarray studies (Sorensen et al., 2008). Involvement of this
protein in oxidative stress, apoptosis and mitochondrial impairment (Sodek et al., 2000)
as well as evidence of elevated levels in other neurodegenerative diseases (Iczkiewics et
al 2006, Wung et al 2007) suggest a potential role in TSE associated neurodegeneration.
Osteopontin also plays a role in pathways related to cell communication, ECM receptor
interaction and focal adhesion. With its wide array of functional involvement, more research is needed to determine if and how up-regulation of this protein may contribute to
cell death.
3.4.4.3. Synapse function
Cellular prion proteins are found in high concentrations in synaptic regions (Fournier et
al., 1995) and it has been suggested that altered localization of PrPc is an early cause of neuronal dysfunction in prion diseases (Wang et al., 2006). The expression of genes
involved in various synaptic functions were altered in the CWD infected elk, which could be an indication of disrupted neuronal function. The microarray results identified the
down-regulation of gamma-aminobutyric acid (GABA) B receptor, 2 (GABBR2). GAB A receptors are responsible for mediating inhibitory neurotransmission. A reduction of
73 GABA receptor mediated physiological functions has been shown to be associated with
PrPsc accumulation (Collinge et al., 1994) which could be explained by the suppression of
these receptor proteins found in our study. Activation of GABA B receptors have been
shown to be neuro-protective during ischemic insults of the central nervous system (Dave
et al., 2005) and suppression of these receptor proteins has been linked to prolonged
neuronal excitation and epileptic seizures (Furtinger et al., 2003). This suggests that
CWD-related suppression of GABBR2 could increase the susceptibility of neurons to cell
death and lead to neurotransmitter related dysfunctions.
The SNARE protein complex (acronym derived from "SNAP" and "NSF" attachment receptors) is important for efficient vesicle transport within neurons and two key proteins involved in SNARE complex function were differentially expressed in the CWD elk. The
SNARE complex is made up of synaptosomal-associated protein, 25kDa (SNAP25), syntaxin, and synaptobrevin which play an essential role in neurotransmitter release
(Asuni et al., 2008). Reduction of SNAP25 and other crucial synaptic proteins have been observed in sporadic CJD (Crozet et al., 2008) The down-regulation of SNAP25 in the
CWD elk concurs with the detection of reduced levels of monomeric SNAP25 protein in a neuronal cell line infected with the RML scrapie strain (Sandberg and Low, 2005).
Recent results suggest that this change may be a result of PrPsc accumulation rather than a change resulting in early synaptic dysfunction (Asuni et al., 2008). These results warrant further exploration into the involvement of SNAP25 at earlier stages of prion disease infection.
74 Disruption of the SNARE complex function as a contributor to prion disease is also
supported by the findings of altered regulation of N-ethylmaleimide-sensitivity factor
(NSF) in the CWD elk. This protein facilitates the disassembly of SNARE complexes that form when the membranes of cellular compartments, such as synaptic vesicles and the presynaptic membrane, fuse to exchange contents. In addition, this protein has been
shown to interact with and affect the cell surface stability of GABA receptors (Goto et al.,
2005). Expression of these receptors was also altered in the CWD elk. Based on these characteristics, altered regulation of NSF may lead to the disturbance of vesicle transport and to neurotransmitter release and function. These changes could compromise the normal functions of neurons ultimately leading to cell death.
3.4.4.4. Calcium ion related genes
Altered expression of calcium binding, transport and homeostasis genes has been identified in TSE infected mouse cell lines (Greenwood et al., 2005) and mouse models
(Brown et al., 2005; Sawiris et al., 2007). In the CWD elk, 8 of the 55 genes identified as differentially expressed were related to calcium ion levels. This altered regulation could contribute to or be in response to altered calcium ion levels which have been proposed to be important in TSE induced cell death (Ferreiro et al., 2008). Alterations in calcium levels have been implicated in the increased oxidative stress on the endoplasmic reticulum (ER) (Brown et al., 2005), and might be a cause of cell death in prion diseases
(Hetz et al., 2003, Hetz et al., 2006). Additional support for the involvement of calcium deregulation in prion diseases comes from the evidence that PrPc limits the release and uptake of calcium from the endoplasmic reticulum (ER) and mitochondria, respectively
(Brini et al., 2005). An absence of functional PrPc would thus lead to a disruption of
75 calcium ion homeostasis. This problem could also be compounded by the ER unfolded protein response which could be activated by the accumulation of PrPsc. Recent research has demonstrated that neurodegeneration induced by a PrPsc-like prion protein peptide is a result of ER stress and calcium ion release resulting in cytosolic Ca2+ elevation, cytochrome C release, caspase 3 activation and apoptosis (Ferreiro et al., 2008).
Calcium/calmodulin-dependent protein kinase II alpha (CaMKII) is one of the calcium- related genes identified as down-regulated in the CWD elk. This protein is involved in several pathways related to neuronal function and survival including long-term potentiation, and calcium and epidermal growth factor receptor (EGFR or ErbB) signalling pathways. Evidence of suppressed CaMKII mediated phosphorylation, due to a combination of reduced activity and down-regulation has been shown in the presence of elevated cellular calcium levels (Hiestand et al., 1992). Induction of CaMKII has been shown to be neuro-protective following ischemic insult. Ischemic insults involve similar modes of oxidative stress as prion diseases (Uno et al 1999). Therefore, suppression of this protein during a prion infection could result in an increase of ER oxidative stress induced cell damage. CaMKII activity has also been shown to increase GABA receptor sensitivity (Churn and DeLorenzo 1998), which suggests a relationship with the reported down-regulation of the GABA receptor B2.
3.4.4.5. Functional groups missing from our study
Some functional groups that were previously found to be altered in other animal TSE microarray experiments were not identified in our individual animal comparison results.
We did not determine a differential expression of genes involved in lysosomal/endosomal
76 function or cholesterol metabolism. Genes from these groups were identified in 4 of 6
individual animal comparisons as well as in the population comparison. The main reason
that these groups were not represented in 5 or 6 of the 6 individual animal comparisons
might be related to the different stage of disease at euthanasia. The use of a bovine
microarray to analyze elk transcripts, the utilization of experimental animals with unique
genetic backgrounds and differences in the experimental design could be additional
factors contributing to our results. In addition, it is possible that, as seen in the expression
analysis of different scrapie strains, some mechanism are only involved in certain prion
diseases or strains. If animal #28 (132LM) could have progressed to a similar stage of
disease as the 132MM animals, it is conceivable that some of the missing genes could
have been identified in all animals.
3.5. Conclusion
Chronic wasting disease in cervids is a prion disease that is of major concern in North
America. Control programs are relatively ineffective in preventing the spread of CWD
into new areas. The potential for this disease to infect other free-ranging or domestic
animal species and/or humans is relatively unknown. Gaining a better understanding of
prion diseases in general and of CWD specifically, will shed light on the unknowns that
are hampering the effective control and management of this disease. Identification of
genes and pathways that are altered in CWD infection using high throughput molecular techniques such as DNA microarrays could provide direction for new research to
diagnose, treat and prevent this disease.
77 In this study, we have used a bovine oligonucleotide microarray to perform a high throughput gene expression analysis to identify changes in the brain stems of chronic wasting disease infected elk. Assignment of these altered genes into functional groups resulted in the identification of genes related to cytoskeleton formation, immune/inflammatory response, neuronal/synaptic functions and calcium ion levels.
Some of the identified genes have not been identified in animal TSE microarray research to date. These genes include several cytoskeletal and calcium-ion related genes. Due to the limited sample size of this study, it is difficult to determine which results are due to the different genetic backgrounds of the elk, the degree of homology between the bovine probes and elk template or the experimental design. By focusing on the genes identified as differentially expressed in all of the CWD infected elk, we have tried to minimize the confounding effects of these factors and provided information about important molecular changes occurring in CWD infected elk brains.
There are several potential projects which could provide clarification and additional insight into the findings of this study. Examination of gene expression in larger animal cohorts for the different prion protein genotypes could flush out some of the results related to different genetic backgrounds. It may also be useful to analyze each individual sample against each of the other samples to prevent the dilution of sample specific phenomenon. Analysis of samples on a different microarray including more genes likely related to prion disease and analysis on a species-specific microarray could provide further insight. Studying earlier times post infection, determining the cellular source of genes differential expressed and identifying changes in other tissues or blood are also
78 some possible future projects which could build on this work. This additional work would help clarify the relevance of the genes identified in terms of their role in neurodegeneration and their potential as targets for therapy or diagnosis in easily attainable sample matricies.
79 Table 3-1: Experimental animals used for the identification of differentially expressed genes in CWD infected brains.
PrP Inoculum: Days P.I. Animal ID 132 10% (weight/volume) Euthanized Normal elk brain Animal #6 MM 752 homogenate Normal elk brain Animal #31 LM 752 homogenate CWD elk brain Animal #8 MM 738 homogenate CWD elk brain Animal #28 LM 738 homogenate CWD elk brain Animal #37 MM 640 homogenate
80 Table 3-2: House keeping genes identified in previous brain qRT-PCR studies which were tried on our elk cDNA.
House keeping gene name3 Primer Sequence Elk cDNA amplification ATP synthase CAG CCT GCC AGA GAC TAT GC YES TCC ACC ACT GCA CCA ATG AC Beta-actin CTG GGA CGA CAT GGA GAA GAT C NO TGA AGG TCT CGA ACA TGA TCT GG Beta-2-microglobulin CGG ATA GTT AAG TGG GAT CGA GAC NO TTA GTC CAA CCC AAA TGA GGC ATC Cyclophilin A GAT TTA TGT GCC AGG GTG GTG YES AAG ATG CCA GGA CCT GTA TGC Glyceraldehyde 3 phosphate CTC TCT GCT CCT GCC CGT TC NO dehydrogenase CAC TCC GAC CTT CAC CAT CTT G Ribosomal protein L12 AGG GTC TGA GGA TTA CAG TGA AAC YES GAT CAG GGC AGA AGC AGA AGG Succinate dehydrogenase CAG CAT GGC AGA CAG GAA CC NO complex, sub. A, flavoprotein ACC ACC ACG GCA TCA AAC TC 18s ribosomal RNA CCG CGG TTC TAT TTT GTT GGT YES CGG CCG CCC CTC TTA A Genes in bold were evaluated for stability using GeNorm
81 Table 3-3: Real time PCR primers/probes and associated amplification efficiencies.
Name Sequence PCR Efficiency A2MF1 AAC TGT GAA GGT CAA GGT CAG G A2MR1 CCG AAC AGA AGT TAG CAA CAA CC 0.93 A2MPr TTG TCA CCT TAT CCA G AIF 1 Fl GCA ACT CAA AGA TAG CTT TCT TGG AIF 1 Rl TGA CTT TCT CAA GAT GAT GTT GGG 0.93 AIF 1 Pr CGT TGG CTT CTC CTG TT CNPF1 GCG GTC TCT TCC CGA AGT AGG CNPR1 GGA GCT GGG CAA TCA CAA GGG 0.91 CNPPr AGC AGT TCA ATC TTC TC HNTF1 CCC TCC CTC GAA TTT GTG TCC HNTR1 TGG CAA CCG ACA GCA ACA AG 0.96 HNTPr TTG GTG GGA TCG TGT VIMF1 TCA GCA GGT CTT GGT ATT CGC VIMR1 GCC AGA TGC GTG AAA TGG AAG 0.94 VIMPr TGT TCT GAA TCT CAT CCT TYRF1 TTG ATG GGA TTA GGA GTA GAG TGC TYRR1 CCA AGG CCA GAG GAC AGA TG 0.90 TYRPr TAC AGC GAC CTC AAC A S100F1 TCT TAT CAG GGT GGG AGT AAT TGC S100R1 AGT GTC GAG ACG GCA AAG TG 0.95 SlOOPr CAA TGC TGC CTA CTT RBPF1 ATT GGG ATG GCT TCG TTT GAA G RBPR1 TCT CTA GGA ATG AGT GAC ATG ACG 0.92 RBPPr AAG AGA CAA CAC CAA G CycFl GAT TTA TGT GCC AGG GTG GTG CycRl AAG ATG CCA GGA CCT GTA TGC 0.91 CycPr CTT CAC ACG CCA TAA RPLF1 AGG GTC TGA GGA TTA CAG TGA AAC RPLR1 GAT CAG GGC AGA AGC AGA AGG 0.98 RPLPr ACC ATT CAG AAC AGA C Note: House keeping genes are in grey text
82 Table 3-4: The genes identified as differentially expressed by comparing the CWD negative population to the CWD positive population (n = 287).
Fld.Chg. Direc. Gene ID Gene Name 2.15 Up CK771623 Actin, alpha 2, smooth muscle, aorta 3.1 Up CK848733 Adipose differentiation-related protein 2.22 Up CK849144 Aldehyde dehydrogenase 1 family, member A1 2.75 Up CK770649 Allograft inflammatory factor 1 2.94 Up CK848171 Alpha-2-macroglobulin 1.91 Up CK775173 Ankyrin repeat and zinc finger domain containing 1 4.62 Up CK849435 Annexin A1 1.83 Up CK846148 antigen NY-CO-41, Similar to 1.6 Up CK774758 Arginine-rich, mutated in early stage tumors 1.98 Up CK848925 B-cell translocation gene 1, anti-proliferative 1.64 Up CK849269 Beta-2-m icroglobu I i n Bone morphogenetic protein receptor, type II 1.85 Up CK772987 (serine/threonine kinase) 2.66 Up CK778444 Bromodomain adjacent to zinc finger domain, 1A 6.87 Up CK773557 Calponin 1, basic, smooth muscle 1.5 Up CK848878 Catalase Catenin (cadherin-associated protein), delta 2 (neural 2.04 Up CK776225 plakophilin-related arm-repeat protein) 1.72 Up CK848620 Cathepsin C 4.94 Up CK771673 Cathepsin Z 1.9 Up CK848618 Caveolin 1, caveolae protein, 22kDa 1.89 Up CK771162 CD2 (cytoplasmic tail) binding protein 2 1.79 Up CK772167 CD44 molecule (Indian blood group) 2.74 Up CK849015 CD53 molecule 1.79 Up CK849512 CD63 molecule 2.99 Up CK774183 CD68 molecule 1.72 Up CK951301 Cell division cycle 26 homolog (S. cerevisiae) 3.98 Up CK776566 Chemokine (C-X-C motif) ligand 14 2.61 Up CK849462 Chloride intracellular channel 1 1.6 Up CK771231 Chondroitin sulfate synthase 1 1.57 Up CK775156 Chromosome 11 open reading frame 75 ortholog 1.6 Up CK940456 Chromosome 6 open reading frame 62 ortholog 2.7 Up CK773445 CK773445 1.63 Up CK777924 CK777924 1.67 Up CK957721 Coactosin-like 1 (Dictyostelium) 1.89 Up CK848275 Coiled-coil domain containing 80 1.59 Up CK771390 Complement component 1, q subcomponent, C chain 1.69 Up CK849121 Cyclin G1 2.45 Up CK777865 Cysteine-rich, angiogenic inducer, 61 2.68 Up CK772605 Cytochrome P450, family 4, subfamily F, polypeptide 3 3.21 Up CK771764 D component of complement (adipsin) 3.67 Up CK776497 Dentin matrix acidic phosphoprotein 1.98 Up CK945018 Deoxyribonuclease II, lysosomal 1.93 Up CK771914 Dual specificity phosphatase 1
83 2.23 Up CK945807 Dystrobrevin, alpha 2.65 Up CK770177 Endothelin 1 1.62 Up CK953743 Eukaryotic translation initiation factor 6 1.55 Up CK772527 F-box and leucine-rich repeat protein 12 5.29 Up CK849494 Fc fragment of IgG, low affinity He, receptor for (CD32) 3.48 Up CK847753 Fibrinogen-like 1 1.76 Up CK775470 Filamin A, alpha (actin binding protein 280) 3.39 Up CK769412 Filamin C, gamma (actin binding protein 280) 16.54 Up CK942582 FK506 binding protein 5 1.75 Up CK956404 Forkhead box F1 1.81 Up CK775692 Frizzled-related protein 1.53 Up CK771140 Fumarylacetoacetate hydrolase (fumarylacetoacetase) 3.75 Up CK950794 Glycerol-3-phosphate dehydrogenase 1 (soluble) 1.97 Up CK775922 Guanine nucleotide binding protein (G protein), gamma 5 2.67 Up CK950637 Histamine N-methyltransferase 2.9 Up CK848234 HORMA domain containing 1 2.06 Up CK770626 HtrA serine peptidase 1 1.52 Up CK846618 Hypothetical LOC506974 1.52 Up CK940416 Hypothetical LOC534574 1.61 Up CK848819 Hypothetical LOC539299 3.27 Up CK948092 Hypothetical LOC616414 Hypoxia-inducible factor 1, alpha subunit (basic helix-loop- 1.88 Up CK770475 helix transcription factor) 2.31 Up CK775895 Immediate early response 3 Inhibitor of DNA binding 3, dominant negative helix-loop-helix 4.05 Up CK770014 protein 1.63 Up CK771770 Integrator complex subunit 5 Integrin, beta 1 (fibronectin receptor, beta polypeptide, 1.68 Up CK947644 antigen CD29 includes MDF2, MSK12) 1.7 Up CK774934 Interferon induced transmembrane protein 1 (9-27) 2.12 Up CK771035 Interferon regulatory factor 5 2.49 Up CK773310 Lectin, galactoside-binding, soluble, 1 (galectin 1) 3.86 Up CK849698 Legumain 1.59 Up CK778054 Leptin receptor overlapping transcript-like 1 1.58 Up CK775761 Leucine rich repeat containing 33 3.39 Up CK774666 Lysosomal associated multispanning membrane protein 5 Major histocompatibility complex, class II, DM beta-chain, 1.71 Up CK773078 expressed 3.62 Up CK848945 Major histocompatibility complex, class II, DR alpha 5.49 Up CK847070 Metallothionein 1E 1.53 Up CK774208 Myotubularin related protein 15 1.89 Up CK774614 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 13 2.47 Up CK771210 PDZ and LIM domain 1 (elfin) 2.24 Up CK943739 Phytanoyl-CoA 2-hydroxylase 1.64 Up CK847656 Poly(A) binding protein, cytoplasmic 1 1.57 Up CK773307 P-Rex1 protein, Similar to 2.08 Up CK769609 Procollagen-lysine 1, 2-oxoglutarate 5-dioxygenase 1 1.52 Up CK948242 protein 4.1-G, Similar to 4.56 Up CK769753 Pygopus homolog 2 (Drosophila)
84 1.59 Up CK777209 Pyridoxal (pyridoxine, vitamin B6) kinase 3.14 Up CK848939 Pyruvate dehydrogenase kinase, isozyme 4 1.96 Up CK847536 RAB13, member RAS oncogene family 1.56 Up CK772011 Regulator of G-protein signaling 2, 24kDa 3.12 Up CK849788 Retinol binding protein 1, cellular 1.54 Up CK953239 Ribosomal protein S15a 3.59 Up CK771779 RNA binding motif protein 18 2.26 Up CK847450 S100 calcium binding protein A11 (calgizzarin) 6.36 Up CK778370 S100 calcium binding protein A4 2.63 Up CK770632 S100 calcium-binding protein A10 Secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, 3.1 Up CK849100 early T-lymphocyte activation 1) 2.11 Up CK848906 Secreted protein, acidic, cysteine-rich (osteonectin) 1.92 Up CK769826 Single-stranded DNA binding protein 2 1.6 Up CK846508 SLC4A11, Similar to 2.17 Up CK950732 Solute carrier family 38, member 2 1.79 Up CK848821 SP140 nuclear body protein 1.61 Up CK775956 Supervillin 3.65 Up CK958274 Syndecan binding protein (syntenin) Syntrophin, beta 2 (dystrophin-associated protein A1, 59kDa, 1.99 Up CK849800 basic component 2) 6.42 Up CK771553 TC277342 1.8 Up TC289234 1.58 Up CK943641 Tetraspanin 3 5.2 Up CK846362 TGFB-induced factor homeobox 1 2.09 Up CK951350 Thrombomodulin 3.52 Up CK770743 Thymosin beta 10 2.19 Up CK770281 TIMP metallopeptidase inhibitor 1 1.92 Up CK769176 TLCK769176 1.68 Up CK769300 TL CK769300 3.48 Up CK769497 TL CK769497 2.76 Up CK770297 TL CK770297 1.59 Up CK772443 TL CK772443 1.68 Up CK772902 TL CK772902 2.58 Up CK773493 TL CK773493 1.69 Up CK775422 TL CK775422 2.02 Up CK775447 TL CK775447 3.75 Up CK776184 TLCK776184 1.8 Up CK848257 TL CK848257 1.76 Up CK849102 TLCK849102 1.57 Up CK942041 TL CK942041 1.89 Up CK944380 TL CK944380 1.67 Up CK954554 TL CK954554 1.52 Up CK956493 TL CK956493 20.75 Up CK958929 TL CK958929 Transcribed locus, strongly similar to NP_001020096.1 G 2.14 Up CK776540 protein-coupled receptor 34 [Rattus norvegicus] Transcribed locus, strongly similar to NP_446013.2 2.51 Up CK849133 nucleosome assembly protein 1-like 1 [Rattus norvegicus]
85 Transcribed iocus, strongly similar to NP_976249.1 1.96 Up CK943697 hypothetical protein LOC387758 [Homo sapiens] 2.39 Up CK769510 Transmembrane 4 L six family member 1 1.55 Up CK848825 Transmembrane emp24-like trafficking protein 10 (yeast) 1.63 Up CK774254 Transmembrane protein 123 1.63 Up CK772196 TSP-EAR protein, Similar to 2.82 Up CK777907 Tubulin, beta 6 7.4 Up CK776187 TYRO protein tyrosine kinase binding protein 1.64 Up CK776845 Ubiquitin domain containing 1 1.83 Up CK848959 Ubiquitin fusion degradation 1 like 1.51 Up CK774369 UDP-glucose ceramide glucosyltransferase 3.01 Up CK770896 Vacuolar protein sorting 28 homolog (S. cerevisiae) 3.58 Up CK777734 Vimentin 1.52 Up CK848230 Vinculin 1.9 Up CK771514 V-yes-1 Yamaguchi sarcoma viral related oncogene homolog 2.06 Up CK777350 Zinc finger protein 36, C3H type, homolog (mouse) 3.93 Down CK769663 2,3-cyclic nucleotide 3 phosphodiesterase 1.88 Down CK771287 Acetylcholinesterase (Yt blood group) 1.68 Down CK769403 Adenylate cyclase 8 (brain) 1.7 Down CK774301 ADP-ribosylation factor 5 1.9 Down AF176811 1.66 Down CK953255 Aldehyde dehydrogenase 2 family (mitochondrial) 1.88 Down CK846492 Amylase, alpha 2A (pancreatic) 2 Down CK847375 Ankyrin repeat domain 50 ATP synthase, H+ transporting, mitochondrial F0 complex, 1.57 Down CK774431 subunit G 1.64 Down CK770793 Brain expressed, X-linked 1 Calcium/calmodulin-dependent protein kinase (CaM kinase) II 5.82 Down CK770723 alpha Calcium/calmodulin-dependent protein kinase (CaM kinase) II 1.74 Down CK774884 gamma 1.71 Down CK950816 Calmodulin binding transcription activator 1 1.56 Down CK776023 CAP-GLY domain containing linker protein 3 4.06 Down CK772754 Carbonic anhydrase II 1.78 Down CK845964 Carnitine palmitoyltransferase 1C 1.76 Down CK849384 CDGSH iron sulfur domain 1 1.79 Down CK848149 Chloride channel 2 1.53 Down CK772654 Chromosome 10 open reading frame 97 ortholog 1.59 Down CK849501 Chromosome 14 open reading frame 124 ortholog 1.57 Down CK771044 Chromosome 17 open reading frame 59 ortholog 1.62 Down CK771559 Coiled-coil domain containing 59 3.54 Down CK769445 Collagen, type XI, alpha 1 1.59 Down CK771412 Crystallin, alpha B 1.52 Down CK958531 C-type lectin domain family 3, member B 1.71 Down CK777416 CUE domain containing 1 2.43 Down CK778581 Cyclin-dependent kinase 5, regulatory subunit 1 (p35) 1.51 Down CK847040 DCP1 decapping enzyme homolog B (S. cerevisiae) 1.67 Down CK849167 DnaJ (Hsp40) homolog, subfamily C, member 12 2.87 Down CK769930 Dual specificity phosphatase 26 (putative)
86 1.51 Down CK771998 Enoyl Coenzyme A hydratase 1, peroxisomal 2.62 Down CK776658 Erythrocyte membrane protein band 4.1-like 3 1.52 Down CK778304 Family with sequence similarity 134, member B 1.52 Down CK773582 Fibroblast growth factor 13 G protein-coupled receptor 37 (endothelin receptor type B- 2.51 Down CK772342 like) 2.58 Down CK770135 Gam ma-am inobutyric acid (GABA) B receptor, 2 1.96 Down CK771996 Gap junction protein, alpha 1, 43kDa 2.1 Down CK849057 Glyceraldehyde-3-phosphate dehydrogenase G-protein coupled receptor-associated sorting protein 1 1.94 Down CK769580 (GASP-1), Similar to 1.73 Down CK770867 GTPase activating Rap/RanGAP domain-like 1 1.84 Down CK848361 Heat shock 70kDa protein 9 (mortalin) 2.17 Down CK776282 Homeobox A5 Hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl- Coenzyme A thiolase/enoyl-Coenzyme A hydratase 2.07 Down CK849508 (trifunctional protein), alpha 3.66 Down CK776494 Hypothetical LOC514162 1.52 Down CK777413 Hypothetical LOC515954 Inhibitor of DNA binding 2, dominant negative helix-loop-helix 1.9 Down CK848916 protein 1.55 Down CK945029 Isocitrate dehydrogenase 2 (NADP+), mitochondrial 2.09 Down CK772898 KIAA0888 protein, Similar to 2.42 Down CK772248 Kinesin family member 21A 2.61 Down CK845912 Kv channel interacting protein 2 1.55 Down CK848078 Lethal giant larvae homolog 1 (Drosophila) 1.83 Down CK846264 Leucine rich repeat containing 3B 1.71 Down CK846491 Leucine rich repeat neuronal 1 1.91 Down CK771175 Mannosidase, alpha, class 1C, member 1 3.6 Down CK770742 MAP7 domain containing 2 1.62 Down CK848754 Melanoma antigen family D, 2 1.89 Down CK848526 Methylmalonyl Coenzyme A mutase 1.65 Down CK773867 Microtubule-associated protein 1A 1.59 Down CK945558 Mitochondrial ribosomal protein S18B 1.63 Down CK778033 Muscleblind-like 2 (Drosophila) 1.9 Down CK777670 Naked cuticle homolog 2 (Drosophila) 2.18 Down CK773574 N-ethylmaleimide-sensitive factor 3.41 Down CK777635 Neurensin 1 6.28 Down CK849634 Neurofilament triplet M protein 1.7 Down CK847752 Neuroplastin 4.68 Down CK776016 Neurotrimin 2.59 Down CK770315 Nuclear receptor subfamily 4, group A, member 2 1.57 Down CK771439 Nucleolar protein 5A (56kDa with KKE/D repeat) 1.63 Down CK778002 Peptidylprolyl isomerase F (cyclophilin F) 1.57 Down CK848304 Peroxiredoxin 1 1.62 Down CK847077 Peroxiredoxin 3 1.61 Down CK776960 Phosphatidylinositol glycan anchor biosynthesis, class Y 1.7 Down CK771342 Phosphomannomutase 1 1.7 Down CK772441 poly-(ADP-ribose) polymerase II, Similar to
87 1.7 Down CK770176 Polymerase (DNA-directed), delta interacting protein 2 Proopiomelanocortin (adrenocorticotropin/ beta-lipotropin/ alpha-melanocyte stimulating hormone/ beta-melanocyte 1.65 Down CK771295 stimulating 1.62 Down CK777240 Proteasome (prosome, macropain) subunit, beta type, 6 1.51 Down CK845979 Protein tyrosine phosphatase, receptor type, D 1.62 Down CK947676 Protein-L-isoaspartate (D-aspartate) O-methyltransferase 1.78 Down CK774929 Proteolipid protein 1.69 Down CK957083 PTK2 protein tyrosine kinase 2 2.39 Down CK772843 Pyrophosphatase (inorganic) 1 1.55 Down CK776947 RAB3A, member RAS oncogene family 2.48 Down CK778427 Receptor accessory protein 1 3.09 Down CK775452 Regeneration associated muscle protease 1.92 Down CK846982 Regulator of calcineurin 2 1.8 Down CK846092 Regulator of G-protein signaling 3 2.25 Down CK776858 RNA binding motif (RNP1, RRM) protein 3 1.58 Down CK773523 RNA terminal phosphate cyclase domain 1 1.86 Down CK773518 Saccharopine dehydrogenase (putative) 1.65 Down CK959624 Secretogranin V (7B2 protein) 1.86 Down CK777177 Selenoprotein P, plasma, 1 3.18 Down CK778765 Serpin peptidase inhibitor, clade I (neuroserpin), member 1 1.66 Down CK769544 SET and MYND domain containing 2 1.53 Down CK771448 SFRS protein kinase 3 2.02 Down CK777078 SH3-domain GRB2-like 2 2.1 Down CK847189 SLC2A4 regulator, Similar to 1.62 Down CK778403 Small nuclear ribonucleoprotein polypeptide N 1.69 Down CK847849 Solute carrier family 22, member 17 Solute carrier family 6 (neurotransmitter transporter, GABA), 2.85 Down CK769571 member 1 1.98 Down CK849369 SPARC-like 1 (mast9, hevin) 2.53 Down CK846084 Spastic paraplegia 3A (autosomal dominant) 4.23 Down CK775031 Stathmin-like 2 1.84 Down CK769703 Stathmin-like 4 1.57 Down CK774894 Suppressor of Ty 5 homolog (S. cerevisiae) 6.35 Down CK776632 Synaptosomal-associated protein, 25kDa 2.15 Down TC289232 1.76 Down CK775741 TC293596 1.83 Down CK771650 TLCK771650 1.84 Down CK772405 TL CK772405 3.07 Down CK846663 TL CK846663 1.87 Down CK955069 TL CK955069 1.92 Down CK847245 TOX high mobility group box family member 3 Transcribed locus, moderately similar to NP_001028508.1 ATP-binding cassette, sub-family C (CFTR/MRP), member 4 1.67 Down CK776984 [Mus musculus Transcribed locus, moderately similar to XP_001502874.1 PREDICTED: similar to Discoidin, CUB and LCCL domain- 1.64 Down CK769381 containing prote
88 Transcribed locus, strongly similar to NP055516.2 1.58 Down CK846448 hypothetical protein LOC9728 [Homo sapiens] Transcribed locus, strongly similar to NP_060528.3 cartilage 1.62 Down CK769444 acidic protein 1 [Homo sapiens] Transcribed locus, strongly similar to NP_659155.1 SH3- domain GRB2-like (endophilin) interacting protein 1 [Mus 1.62 Down CK776141 musculus] Transcribed locus, strongly similar to XP_001254005.1 2.08 Down CK771691 PREDICTED: similar to KRAB box [Bos taurus] Transcribed locus, strongly similar to XP_594051.1 PREDICTED: similar to Potassium channel tetramerisation 1.77 Down CK849759 domain containing 1.9 Down CK770721 Transducer of ERBB2, 1 1.57 Down CK769566 Transgelin 3 1.8 Down CK848935 Transketolase (Wernicke-Korsakoff syndrome) Translocase of outer mitochondrial membrane 20 homolog 1.62 Down CK849180 (yeast) 1.51 Down CK848729 Transmembrane protein 199 1.84 Down CK772250 Tripartite motif-containing 2 2.32 Down CK849835 TSPY-like 4 3.11 Down CK772448 Tubulin, beta 2A 1.69 Down CK770563 Tubulin, gamma 1 3.68 Down CK847699 Tumor rejection antigen (gp96) 1 Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase 1.98 Down CK769514 activation protein, gamma polypeptide 1.51 Down CK776644 Ubiquitin specific peptidase 8 UDP-GlcNAc:betaGal beta-1,3-N- 1.53 Down CK769803 acetylglucosaminyltransferase 1 1.52 Down CK849164 UQCRC1 protein 2.06 Down CK771983 Vacuolar protein sorting 52 homolog (S. cerevisiae) 5.86 Down CK775970 Visinin-like 1 2.12 Down CK774182 WD repeat and SOCS box-containing 2 1.52 Down CK847887 WW domain binding protein 5 1.74 Down CK847645 Zinc finger protein 576 2.15 Down CK773619 Zinc finger protein 618
89 Table 3-5: Genes identified as differentially expressed in 4, 5 or 6 out of 6 CWD positive versus CWD negative elk by microarray analysis. Genes missing a comparison(s) involving animal #28 (132LM) are highlighted.
Status Direction3 Gene ID Gene Name Up CK770649 Allograft inflammatory factor 1 Up CK776566 Chemokine (C-X-C motif) ligand 14 Up CK769412 Filamin C, gamma (actin binding protein 280) Up CK950794 Glycerol-3-phosphate dehydrogenase 1 (soluble) Inhibitor of DNA binding 3, dominant negative helix- Up CK770014 loop-helix protein Up CK847070 Metallothionein 1E Secreted phosphoprotein 1 (osteopontin, bone Up CK849100 sialoprotein I, early T-lymphocyte activation 1) Up CK950732 Solute carrier family 38, member 2 Up CK958274 Syndecan binding protein (syntenin) Up CK771553 TC277342 Up CK777907 Tubulin, beta 6 Up CK776187 TYRO protein tyrosine kinase binding protein Up CK770896 Vacuolar protein sorting 28 homolog (S. cerevisiae) Down CK769663 2,3-cyclic nucleotide 3 phosphodiesterase Down AF176811 Down CK846492 Amylase, alpha 2A (pancreatic) Down CK950816 Calmodulin binding transcription activator 1 Down CK772754 Carbonic anhydrase II Down CK769445 Collagen, type XI, alpha 1
D E i n 6 o f Comparison s Down CK769930 Dual specificity phosphatase 26 (putative) Down CK776658 Erythrocyte membrane protein band 4.1-like 3 G protein-coupled receptor 37 (endothelin receptor type Down CK772342 B-like) Down CK777635 Neurensin 1 Down CK849634 Neurofilament triplet M protein Down CK776016 Neurotrimin Down CK775452 Regeneration associated muscle protease Down CK769703 Stathm in-like 4 Down CK776632 Synaptosomal-associated protein, 25kDa Down TC289232 Down CK772448 Tubulin, beta 2A Down CK847699 Tumor rejection antigen (gp96) 1 Down CK775970 Visinin-like 1 Up CK773557 Calponin 1, basic, smooth muscle Up CK771764 D component of complement (adipsin) Up CK945807 Dystrobrevin, alpha Up CK848906 Secreted protein, acidic, cysteine-rich (osteonectin) Down CK778427 Receptor accessory protein 1
comparisons ) Down CK773619 Zinc finger protein 618
5 o f 6 (missin g 1 M Down CK848149 Chloride channel 2
90 4 of 6 (missing both #28(LM) comparisons) 5 of 6 (missing #28(LM) comparisons) i Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Up Down Up Down Down Down Down Down Up Up Up Up Up Up Up Up Up Up CK848959 CK849133 CK776540 CK770281 CK772196 CK778370 CK847536 CK848939 CK769609 CK770314 CK943739 CK771210 CK774666 CK771035 CK774934 CK770626 CK953743 CK945018 CK849015 CK775439 CK849494 CK849625 CK772987 CK849269 CK849435 CK777078 CK773574 CK777670 CK770135 CK778581 CK770723 CK770632 CK770743 CK849788 CK771099 CK848945 CK776497 CK848925 CK957721 CK849462 CK774183 TIMP metallopeptidaseinhibitor1 Transcribed locus,strongly similartoNP_446013.2 Transcribed locus,strongly similartoNP_001020096.1 TC289234 Ubiquitin fusiodegradatio n 1like norvegicus] nucleosome assemblyprotei n 1-like1[Rattus G protein-coupledreceptor 34[Rattusnorvegicus] S100 calciumbindingproteinA4 Poly(A) bindingprotein,cytoplasmic4(inducibleform ) 5 transducer 1) Annexin A1 Similar toTSP-EARprotein Pyruvate dehydrogenaskinase,isozym4 Procollagen-lysine 1,2-oxoglutarat5-dioxygenas Phytanoyl-CoA 2-hydroxylase PDZ andLIMdomain1(elfin) Lysosomal associatedmultispanningmembraneprotein CD53 molecule SH3-domain GRB2-like2 RAB13, memberRASoncogenefamily Interferon regulatoryfactor5 Interferon inducedtransmembraneprotei1(9-27) HtrA serinepeptidas1 Fc fragmentofIgG,lowaffinityliereceptorfo(CD32) Eukaryotic translationinitiatiofactor6 Deoxyribonuclease II,lysosomal Cathepsin H (serine/threonine kinase) Bone morphogeneticproteinreceptor,typI Beta-2-microglobulin Cyclin-dependent kinase5,regulatorysubuni1(p35) Calcium/calmodulin-dependent proteinkinase(CaM Thymosin beta10 Interferon gammareceptor2(interfero N-ethylmaleimide-sensitive factor Naked cuticlehomolog2(Drosophila) Gamma-aminobutyric acid(GABA)Breceptor,2 kinase) Ialpha S100 calcium-bindingproteinA1 Methyltransferase lik9 Major histocompatibilitycomplex,classIIDRalpha Dentin matrixacidicphosphoprotei Retinol bindingprotein1,cellular Coactosin-like 1(Dictyostelium) Chloride intracellularchannel1 CD68 molecule B-cell translocationgene1,antiproliferativ 91 a Allgenesinthitablewerdifferentiallyexpressed1.5folorgreatethdirectioindicated. 4 of 6 (missing both #8 4 of 6 (missing 2 random 4 of 6 (missing both #28(LM) or #37 MM comparisons) comparisons) cont. comparisons) Down Down Down Down Down Down Down Down Down Up Up Up Up Down Down Up Up Down Down Down Down Down Down Down Down Up CK769803 CK778765 CK776960 CK771175 CK848078 CK849384 CK847450 CK775692 CK771673 CK771623 CK771983 CK948691 CK953159 CK849194 CK942582 CK849863 CK769514 CK849835 CK775741 CK769571 CK847077 CK770171 CK777319 CK771996 CK771412 CK774369 acetylglucosaminyltransferase 1 Serpin peptidaseinhibitor,cladI(neuroserpin) Y Actin, alpha2smoothmuscleaort member 1 CDGSH ironsulfurdomai1 S100 calciumbindingproteinA11(calgizzarin) Vacuolar proteinsorting52homolo(S.cerevisiae) UDP-GlcNAc:betaGal beta-1,3-N- Phosphatidylinositol glycananchorbiosynthesis,class Mannosidase, alphaclass1Cmember1 Lethal giantlarvaehomolog1(Drosophila) Cathepsin Z SCAN domaincontaining1 Glutathione S-transferasA4 Tyrosine 3-monooxygenase/tryptophan5- TSPY-like 4 TC293596 Frizzled-related protein Mitochondrial ribosomaproteinL48 3-hydroxybutyrate dehydrogenase,type2 Solute carrierfamily6(neurotransmittetransporter, FK506 bindingprotein5 monooxygenase activationprotein,gammapolypeptid GABA), member1 Peroxiredoxin 3 Crystallin, alphaB Neurocan Latent transforminggrowthfactorbetabindinprotein2 Gap junctionprotein,alpha143kD UDP-glucose ceramidglucosyltransferas 92 Table 3-6: Microarray results of differentially expressed genes in 5 or 6 out of 6 CWD elk versus control elk. Genes are grouped based on functional annotation.
Group Direction Gene ID Gene Name Up CK848925 B-cell translocation gene 1, anti-proliferative Up CK957721 Coactosin-like 1 (Dictyostelium) Up CK769412 Filamin C, gamma (actin binding protein 280) Up CK958274 Syndecan binding protein (syntenin) Up CK770743 Thymosin beta 10 Up CK777907 Tubulin, beta 6 Down CK769445 Collagen, type XI, alpha 1 Down CK778581 Cyclin-dependent kinase 5, regulatory subunit 1 (p35) Down CK776658 Erythrocyte membrane protein band 4.1-like 3 Down CK849634 Neurofilament triplet M protein Cytoskeleta l relate d gene s Down CK769703 Stathmin-like 4 Down CK772448 Tubulin, beta 2A Up CK945807 Dystrobrevin, alpha Down CK770723 Calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha Down CK778581 Cyclin-dependent kinase 5, regulatory subunit 1 (p35) Down CK770135 Gamma-aminobutyric acid (GABA) B receptor, 2 Down CK773574 N-ethylmaleimide-sensitive factor gene s Down CK777635 Neurensin 1 Down CK849634 Neurofilament triplet M protein Down CK776016 Neurotrimin Neuron/Synaps e relate d Down CK776632 Synaptosomal-associated protein, 25kDa Up CK770014 Inhibitor of DNA binding 3, dominant negative helix-loop-helix protein Up CK771099 Methyltransferase like 3 Up CK770632 S100 calcium-binding protein A10 Up CK776187 TYRO protein tyrosine kinase binding protein Down CK950816 Calmodulin binding transcription activator 1 Down CK772342 G protein-coupled receptor 37 (endothelin receptor type B-like) Down CK776016 Neurotrimin
Cel l signallin g gene s Down CK778427 Receptor accessory protein 1 Down CK775970 Visinin-like 1 Up CK770649 Allograft inflammatory factor 1 Up CK774183 CD68 molecule Up CK776566 Chemokine (C-X-C motif) ligand 14 Up CK771764 D component of complement (adipsin) Up CK848945 Major histocompatibility complex, class II, DR alpha gene s Secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T- Up CK849100 lymphocyte activation 1) Up CK776187 TYRO protein tyrosine kinase binding protein Immun e / inflammator y Down CK778427 Receptor accessory protein 1
93 Ion related Other / unknown genes Calcium related genes genes Down Down Down Down Down Down Down Down Down Down Up Up Up Up Up Down Down Up Up Down Down Down Down Up Up Up Up Up CK847699 CK777078 CK775452 CK777670 CK769930 CK772754 CK846492 CK769663 CK770896 CK771553 CK849788 CK950794 CK776497 CK773619 CK848149 CK950732 CK847070 CK849462 CK775970 CK776632 CK950816 CK770723 CK848906 CK770632 CK945807 CK773557 Tumor rejectionantige(gp96)1 TC289232 Amylase, alpha2A(pancreatic) AF176811 SH3-domain GRB2-like2 Carbonic anhydraseI Vacuolar proteinsorting28homolo(S.cerevisiae) TC277342 Regeneration associatedmuscleproteas Naked cuticlehomolog2(Drosophila) Dual specificityphosphatase26(putative) 2,3-cyclic nucleotide3phosphodiesteras Zinc fingerprotein618 Solute carrierfamily38,membe2 Glycerol-3-phosphate dehydrogenas1(soluble) Dentin matrixacidicphosphoprotei Chloride channel2 Visinin-like 1 Retinol bindingprotein1,cellular Metallothionein 1E Chloride intracellularchannel1 Synaptosomal-associated protein,25kDa Calmodulin bindingtranscriptioactivator1 Calcium/calmodulin-dependent proteinkinase(CaMkinase)Ialpha Secreted protein,acidiccysteine-rich(osteonectin) S100 calcium-bindingproteinA1 Dystrobrevin, alpha Calponin 1,basicsmoothmuscle 94 Table 3-7: Genes from the 55 differentially expressed in all of the CWD positive animals that are associated with one or more KEGG pathway(s).
Gene Name KEGG Pathway Wnt signalling pathway Long term potential Calcium signalling pathway Calcium/calmodulin-dependent protein kinase Olfactory transduction (CaM kinase) II alpha GnRH signalling pathway Glioma EGFR signalling pathways Melanogenesis D component of complement (adipsin) Complement and coagulation cascade Gam ma-am inobutyric acid (GABA) B receptor, Neuroactive ligand receptor interaction 2 Inhibitor of DNA binding 3, dominant negative TGF beta signalling helix-loop-helix protein Cell communication Secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymphocyte activation 1) ECM receptor interaction Focal adhesion Synaptosomal-associated protein, 25kDa SNARE interaction in vesicular transport (SNAP-25) Tubulin, beta 2A GAP junction TYRO protein tyrosine kinase binding protein Natural killer mediated cytotoxicity
95 Figure 3-1: Microarray slide comparing the CWD infected brain stem gene expression profiles to the CWD negative brain stem gene expression profiles (n = 10).
#8 #8 Cy5 Cy3
Neg Pool Neg Pool #28 #28 6 + 31 6 + 31 Cy5 Cy3 Cy3 Cy5
#37 #37 DYE SWAPS Cy5 Cy3
#6 #6 Cy3 Pos Pool Cy5 Pos Pool 8+28+37 8+28+37 Cy3 Cy5 #31 #31 Cy5 K Cy3
96 Figure 3-2: Genesifter molecular function grouping of differentially expressed genes in the CWD negative population versus the CWD positive population compared to the molecular function grouping of all probes represented on the microarray.
45
40
35
30-H
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98 Figure 3-4: Genes identified as differentially expressed when comparing the individual negative animals against different combinations of the positive animals. For each group the genes common and unique to both comparisons are shown.
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C? ^ , RPL12 Cyclophilin 18s rRNA ATP synthase Animal #6 23.00 23.74 26.36 37.66 Animal #31 23.31 23.62 26.03 33.06 Animal #8 23.16 24.10 23.63 33.58 Animal #28 22.66 23.45 23.15 32.71 Animal #37 22.76 23.67 24.27 33.32 | Animal #N Genel = Genel Eff.(Lowest RPL12 Cyclophilin 18s rRNA ATP synthase Animal #6 0.79 0.84 0.18 0.04 Animal #31 0.64 0.90 0.21 0.80 Animal #8 0.71 0.68 0.77 0.58 Animal #28 1.00 1.00 1.00 1.00 Animal #37 0.93 0.88 0.54 0.68 i Input values into GeNorm to calculate stability (M) RPL12 Cyclophilin 18s rRNA ATP synthase Animal #6 0.79 0.84 0.18 0.04 Animal #31 0.64 0.90 0.21 0.80 Animal #8 0.71 0.68 0.77 0.58 Animal #28 1.00 1.00 1.00 1.00 Animal #37 0.93 0.88 0.54 0.68 GeNorm M 1.03 1.07 1.19 1.69 i Removed least stable gene and recalculated stability (M) RPL12 Cyclophilin 18s rRNA Animal #6 0.79 0.82 0.11 Animal #31 0.64 0.89 0.14 Animal #8 0.71 0.64 0.72 Animal #28 1.00 1.00 1.00 Animal #37 0.93 0.86 0.46 GeNorm M 0.62 0.70 1.08 i Removed least stable gene calculated final stability (M) RPL12 Cyclophilin Animal #6 0.79 0.82 Animal #31 0.64 0.89 Animal #8 0.71 0.64 Animal #28 1.00 1.00 Animal #37 0.93 0.86 GeNorm M 0.24 0.24 Figure 3-6: Average real time PCR expression change of eight genes in CWD infected elk compared to the population versus population expression change identified by microarray analysis. 0 Microarray DReal Time PCR CM I m (N 1 °! O *o- |o o CO -r- AIF1 CNP HNT TYROBP S100A10 Gene Figure 3-7: Real time PCR expression change of eight genes in the 6 individual animal comparisons. 3 2.75 2.5 2.25 2 Hi D6v8 m 1-75 "H c D31 v8 (Q D6v28 § 1.5 D31 v28 •o o f D6v37 "• 125 -H D31 v37 1 M J 0.75 0.5 K 0.25 r^|rM[cMnoj(olcol h~ nfcHoKopo] B9h-B>2l L8bE bbbBbfc 0 lal°lsl' A2M AIF1 VIM TYRPBP S100A10 RBP1 Gene 102 3.6. References Aguzzi, A. 2006. Prion diseases of humans and farm animals: epidemiology, genetics, and pathogenesis. J. Neurochem. 97: 1726-39. Anderson, C.A., Bosque, P., Filley, CM., Arciniegas, D.B., Kleinschmidt-Demasters, B.K., Pape, W.J., Tyler, KL. 2007. Colorado surveillance program for chronic wasting disease transmission to humans: lessons from 2 highly suspicious but negative cases. Arch. Neurol. 64: 439-41. Asuni, A.A., Cunningham, C, Vigneswaran, P., Perry, V.H., O'Connor, V. 2008. Unaltered SNARE complex formation in an in vivo model of prion disease. Brain Res. 1233: 1-7. 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Nucleic Acid Res. 30: el5. 108 Appendix Appendix 1: Total RNA extraction protocol Tissue extraction 1. Preserve tissues as quickly as possible after euthanasia to maintain an intact mRNA profile. 2. Place tissues (no thicker then 0.5cm) into 5 to 10 volumes of RNAlater and store at 4°C overnight. 3. Carefully drain the RNAlater fluid off the preserved tissue and trim the tissue for homogenization or freeze at -80°C until further examination. Tissue homogenization 4. Trim tissues to a 1:10 (weight/volume) ratio of tissue to Trizol when homogenizing (ex. 0.3g of tissue would require 3mL of Trizol). 5. Add tissue and required amount of Trizol to a clean tube large enough to allow for efficient homogenization and place on ice. 6. Using a disposable Omni Hard Tissue homogenization probe, disrupt the tissue 3 x 20 seconds on setting 6 (high) of the Omni Tissue Homogenizer TH115. Keep the tube on ice as much as possible to ensure the temperature of the sample does not become elevated. 7. Place the homogenized sample back on ice and allow the foam to settle for ~30 minutes before proceeding to the next step. RNA extraction 8. Transfer 1.1 mL of Trizol/tissue into a 2mL microfuge tube and spin the tube at 10,000 rpm for 10 minutes at 4°C. 9. Transfer lmL of the cleared supernatant to a new 2mL micro fuge tube leaving the cellular debris and ~100uL behind to discard, add lOOuL of chloroform to each sample and mix well by vortexing. 10. Allow the mixture to incubate for 10 minutes at room temperature and then spin samples at 10,000 rpm for 10 minutes. 11. Carefully remove the tubes from the centrifuge and pipette the aqueous layer of each sample into new tubes without disturbing the organic layer. 109 12. Add an equal volume of 75% ethanol to each sample, mix well and transfer the mixture to a Qiagen RNeasy extraction column and spin at 10,000 rpm for ~30 seconds. 13. Wash the column one time with 700uL of buffer RW1, and one time with 500uL of buffer RPE spinning the wash solutions through the column for each wash at 10,000 rpm for -30 seconds. 14. Do a second RPE buffer wash of 500 uL but spin at 10,000rpm for 2 minute, then spin the column empty for 1 minute at 14,000 rpm to remove residual buffer. 15. Add 50uL of nuclease free H2O to the center of the column filter and incubate at room temperature for 2 minutes before spinning at 14,000 rpm for 1 minute to elute the total RNA. 16. Place the eluted sample on ice, use 1 uL to quantify the RNA using the Nanodrop 1000 spectrophotometer and assess the quality of the sample using the Agilent Bioanalyzer. 17. Based on the quantification results, aliquot the total RNA 2ug/tube and record the volumes. Proceed with experiment or store the RNA at -80°C until needed. 110 Appendix 2: DNA Microarray protocol Sample preparation and labelling were done using the Ambion Amino Allyl Message Amp II aRNA kit. First strand synthesis 1. Add luL of T7 oligo dT primer to 2ug of total RNA, bring the volume of each reaction up to 12 (^ L with nuclease free (NF) H2O. 2. Incubate the reactions at 70°C for 10 minutes, then remove, mix, pulse spin and place them on ice. 3. Add 8uL of reverse transcriptase master mix to each reaction, mix, pulse spin and place the reactions at 42°C for 2 hours. Reverse transcriptase master mix: -2uL 10 x first strand buffer -4uL dNTP mix -1 uL RNase inhibitor -1 uL Array Script reverse transcriptase Second strand synthesis 4. After the 2 hour incubation, pulse spun and placed tubes on ice. 80uL of second strand master mix is added to each tube, mix, pulse spin and place the tubes at 16°C for 2 hours. Second strand master mix: -63uLNFH20 -lOuL 10 x second strand buffer -4uL dNTP mix -2uL DNA polymerase -luL RNase H cDNA purification 5. Transfer completed second strand synthesis reactions to 1.5mL tubes, add 250uL cDNA binding buffer, mix well and transfer each of the mixtures to cDNA purification columns. 6. Spin the columns at 10,000 x g for 1 minute, discard the flow through. 7. Wash the columns by adding 500uL of wash buffer and spin at 10,000 x g for 1 minute, discard the flow through. 8. Spin the columns empty at 10,000 x g for 1 minute then place then in collection tubes. Ill 9. Elute by adding 9uL of NF H20 preheated to 50-55°C, let the columns incubate at room temperature then spin at 10,000 x g for 1 minute. Repeat elution for a final volume of ~18uL of purified cDNA. In vitro transcription (IVT) reaction 10. Add 26 uL of IVT master mix to each of the column purified cDNAs, mix, pulse spin and place at 37°C for 16 hours. IVT master mix: -3uL amino allyl UTP -12uL ATP/CTP/GTP mix -3uLUTP -4uL 10 x in vitro transcription reaction buffer -4uL IVT in vitro transcription enzyme Antisense RNA (aRNA) purification 11. Upon completion, stop the IVT reaction by adding 60uL of NF H2O, then add 350uL of aRNA binding buffer, 250uL of 100% ethanol, mix and transfer to aRNA purification columns. 12. Spin the columns at 10,000 x g for 1 minute, discard the flow through. 13. Wash the columns by adding 650uL of wash buffer and spin at 10,000 x g for 1 minute, discard the flow through. 14. Spin the columns empty at 10,000 x g for 1 minute then place them in collection tubes. 15. Elute by adding 50uL of NF H2O preheated to 50-55°C, let the columns incubate at room temperature for 2 minutes then spin at 10,000 x g for 1 minute. Repeat elution for a final volume of lOOuL of purified aRNA. 16. Quantify the purified aRNA using a Nanodrop 1000 Spectrophotometer. Dye coupling 17. Transfer a total of 15(j,g of aRNA to new 1.5mL microfuge tubes and vacuum dry the samples. 18. Resuspend the dried aRNA in 9uL of dye coupling buffer then add 11 uL of NHS- Cy3 or Cy5 in DMSO, mix and incubate for 60 minutes at room temperature in the dark. 19. Quench the coupling reactions by adding 4.5uL of 4M hydroxylamine, mix and incubate for 15 minutes in the dark. 112 20. Add 5.5uL of NF H20 to each reaction to bring the volume up to 30uL. Dye labelled aRNA purification 21. Add 105uL of aRNA binding buffer and 75 uL of 100% ethanol, mix and then transfer to aRNA purification columns. 22. Spin the columns at 10,000 x g for 1 minute, discard the flow through. 23. Wash the columns by adding 500uL of wash buffer and spin at 10,000 x g for 1 minute, discard the flow through. 24. Spin the columns empty at 10,000 x g for 1 minute then place them in collection tubes. 25. Elute by adding lOuL of NF H2O preheated to 50-55°C, let the columns incubate at room temperature for 2 minutes then spin at 10,000 x g for 1 minute. Repeat elution step 2 more times for a final volume of 30uL of purified aRNA. 26. Quantify the purified dye labelled aRNA using a Nanodrop 1000 Spectrophotometer. Preparation for hybridization 27. The volume required to obtain 5ug of labelled aRNA is calculated based on the quantification results. This volume is transferred to a new tube. 28. Transferred volumes are adjusted to 9uL by adding NF H2O or vacuum drying the dye labelled aRNA. Fragmentation 29. Add luL of fragmentation solution to each dye labelled aRNA, mix and incubate at 70°C for 15 minutes. 30. Stop the fragmentation reactions by adding luL of fragmentation stop solution and place the reaction tubes on ice until the slides are ready. Slide preparation 31. The 9K bovine oligonucleotide microarray slides are incubated for 45 minutes at 42°C in prehybridization buffer. Prehybridization buffer -2.5g BSA -186.5 mLNFH20 -62.5 mL 20 x SSC -1.25 mL 10%SDS 113 32. Slides were then washed in milli-Q H2O for 5 minutes with gentle agitation (-llOrpm). 33. Dip slides in isopropanol and spin dry, wash lifter slips in isopropanol. Preparing targets for hybridization 34. Add 27uL of hybridization buffer to each dye labelled, fragmented aRNA to be applied to the slides. Hybridization buffer -22.5|j,L deionized formamide -18.75uL 20 x SSC (Ambion, USA) -0.75uL 10% SDS (Ambion, USA) -6.0|iL yeast plus tRNA 10 mg/mL -6.0uL salmon sperm DNA lOmg/mL 35. Mix the diluted sample with its designated comparative sample labelled with the opposite dye. 36. Pipette up the entire volume of the 2 mixed comparative samples and slowly dispense the mixture onto the microarray slide so it is wicked underneath the lifter slip and covers all of the microarray slide spots. 37. Place slides in a sealed hybridization chamber and incubate at 42°C for 18 hours. Slide washing 38. After the 18 hour incubation place slides in low stringency wash buffer to remove the lifter slip and test samples, and then wash 2 times for 5 minutes each in low stringency wash buffer with gentle agitation (-110 rpm). Low stringency wash buffer -2 x SSC -0.5% SDS 39. Wash slides 2 times in high stringency wash buffer for 5 minutes with gentle agitation (-110 rpm). High stringency wash buffer -0.5 x SSC -0.2% SDS 40. Transfer slides to a new slide holder and wash for 5 minutes with gentle agitation (-110 rpm) in final wash buffer. Final wash buffer -0.05 x SSC 114 41. Spin dry and scan to determine the dye intensities. 115 Appendix 3: Quantitative real time PCR (qRT-PCR) protocol cDNA synthesis DNase treatment 1. Add 2uL of DNase I, 2uL of 10 x buffer to 2ug of sample total RNA diluted in 16uL of nuclease free (NF) H20. 2. Mix the reactions, pulse spin, incubate at room temperature for 15 minutes. 3. Add luL of 15mM EDTA to the DNase I treated samples and incubate at 65°C for 10 minutes followed by a snap chill on ice. Reverse transcription 3. Add 2uL of 0.5|j.g/uL oligo dT primer and 4uL of lOmM dNTP mix, mix, pulse spin and incubate at 65°C for 5 minutes, and then snap chill on ice. 4. Add 8uL of 5 x first strand buffer, luL of RNaseOUT RNase inhibitor, 4uL of 0.1 mM DTT, mix, pulse spin and incubate at 42°C for 2 minutes. 5. While still incubating add 2uL of Super Script II reverse transcriptase, mix by pipetting and continue to incubate for an additional 50 minutes at 42°C followed by 15 minutes at 70°C. 6. If continuing, place the reaction tubes on ice until required, if not store the cDNA at - 20°C until needed. qRT-PCR set up Real time PCR master mix (25uL reaction volume) -12.5jj.L 2 x PCR Master Mix (Applied Biosystems, USA) -2uL Forward primer (lOOuM, Invitrogen, Canada) -2uL Reverse primer (lOOuM, Invitrogen, Canada) -0.63 uL Taqman MGB probe (lOOuM, Applied Biosystems, USA) -4.87uL NF H20 -3uL cDNA template Real time PCR cycle program Cycles Repeats Time Temperature 1 cycle 2 minutes 50°C 1 cycle 10 minutes 95°C 15 seconds 95°C 40 cycles 1 minute 60°C 116 Appendix 4: Formulas used for data analysis Intensity-dependent normalization formula used to normalize microarray intensities (Yang et al., 2002): log2 RIG -> log2 BJG-c(A) = log2 R/(K(A)G) A - overall spot intensity R - Cy5 (red) dye intensity G- Cy3 (green) dye intensity c - the lowess fit to the log-ratio intensity vs overall spot intensity K - constant linking red and green intensities Equation for real time PCR gene expression fold change calculations (Pfaffl, 2001): fC \ACt target (control-sample) / ^r: \ ACtref (control-sample) \p targetj ' \J^ refy E target - real time PCR efficiency of the target gene transcript E ref - real time PCR efficiency of the reference gene transcript ACt target - the cycle threshold deviation of the control - sample of the target gene transcript ACt ref - the cycle threshold deviation of the control - sample of the reference gene transcript Real time PCR efficiency equation (Pfaffl, 2001): £ = 1Q[-1/slope] E - real time PCR efficiency slope - derived from plotting Ct values from a 10 fold dilution series of template 117