Identification of central nervous system genes involved in the host response to the scrapie agent during preclinical and clinical infection

Stephanie Booth1, Christopher Bowman2, Richard Baumgartner2, Garrett Sorensen1, Catherine Robertson1, Michael Coulthart1, Clark Phillipson1 and Rajmund L. Somorjai2

1 Division of Host Genetics and Prion Diseases, National Microbiology Laboratory, Health Canada, Winnipeg, MB, Canada R3E 3R2
2 Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada R3B 1Y6

Correspondence
Stephanie Booth
Stephanie_Booth{at}hc-sc.gc.ca


   ABSTRACT
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Genes that are expressed differentially in the central nervous system of mice during infection with mouse-adapted scrapie agents were identified in this study. cDNA microarrays were used to examine gene-expression profiles at early, middle (preclinical) and late (clinical) time points after inoculation. A number of genes that showed significant changes in expression during the clinical stage of disease were identified. Of these, 138 were upregulated and 20 were downregulated. A smaller number of genes showed differential expression at the early and middle stages of the disease time course. These genes are interesting, as they may reflect biological processes that are involved in the molecular pathogenesis of the prion agent. At present, little is known about the early events in the disease process that trigger neurodegeneration. Perhaps most interestingly, one group of genes that exhibited decreased expression in all tested stages of the disease was identified in this study. This cluster included four transcripts representing haematopoietic system-related genes, which suggests that the haematopoietic system is involved in the disease process from an early stage.

Raw data and a hyperlinked version of Table 1 are available as supplementary material in JGV Online.


   INTRODUCTION
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Prion diseases, or transmissible spongiform encephalopathies (TSEs), include Creutzfeldt–Jakob disease (CJD) and Gerstmann–Straussler–Sheinker (GSS) disease in humans and bovine spongiform encephalopathy (BSE), scrapie and chronic wasting disease (CWD) in animals. All of these diseases are defined by the accumulation of an abnormally folded, protease-resistant isoform (PrPSc) of a normal host protein, the prion protein (PrPc), in the brains of infected individuals. This occurs progressively throughout the incubation period of disease and is accompanied by vacuolation, gliosis and neuronal cell death. Clinical stages always involve progressive, fatal neurodegeneration. It is generally assumed that the disease-specific conformational isoform acts as a template and induces the same structural changes within normally folded PrPc molecules on contact, thus accounting for the transmissibility of the diseases (Prusiner, 1998). However, the precise molecular and cellular mechanisms that underlie prion disease pathogenesis, and even the role of PrPc in host species, are unknown.

Molecular tests to detect the disease-specific isomer of the prion protein have been under development for many years. Progress is hampered by the similarity between healthy and disease isomers of the protein. Reagents such as antibodies, which rely on affinity binding, can show some degree of cross-reactivity. In addition, PrPc is expressed at high levels in many normal tissues, resulting in low sensitivity and high false-positive rates. Significant accumulation of PrPSc, even in the central nervous system (CNS), is also not observed in all TSEs (Manson et al., 1999). Identification of host factors that are expressed differentially during disease pathogenesis and can be used as secondary markers of infection is another avenue of investigation. Several such studies, aimed at the characterization of gene expression in prion-infected tissues, have been performed and a small number of genes have been identified that show differential expression as a result of prion disease. These include the genes encoding the following: cathepsin S; the C1q B chain of complement; apolipoprotein D (Dandoy-Dron et al., 1998); {beta}2-microglobulin, F4/80; metallothionein II (Duguid & Dinauer, 1990); and {alpha}-haemoglobin stabilizing factor or Edrf (Miele et al., 2001).

These genes constitute relatively few targets for further study as potential biomarkers of infection. The availability of tools to study differential gene expression across whole genomes provides the means to identify many more potential molecular biomarkers for prion disease. In addition, resolving the relationship between the phenotype of neurodegeneration and infection with a TSE agent would allow construction of a predictive model that may aid in diagnosis and treatment and, in addition, bring about a better understanding of the basic biological processes.

We used cDNA microarrays to identify genes that are expressed differentially in response to infection with prion agents in C57BL/6 mouse models. Two strains of mouse-adapted scrapie that result in different pathological signatures in the brains of C57BL/6 mice were used, to ensure that a generalized response to prion disease was identified. We identified over 150 genes that were the most significantly different between infected and uninfected mice at the clinical stages of infection with scrapie. In addition, genes that were significantly different between infected and uninfected mice at preclinical stages of infection were identified.


   METHODS
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Array construction.
Libraries and clones derived from brain regions of adult mouse (strain C57BL/6) were obtained from Research Genetics. The 11 136 clones represent expressed sequence tags (ESTs) from some 5471 unique known genes and 5665 as-yet-uncharacterized mRNAs and form part of a library that was created as part of the Brain Molecular Anatomy Project (BMAP). Each insert from this BMAP 3' EST library was amplified by PCR. cDNA was purified by using Millipore multiwell purification plates and lyophilized PCR products were resuspended in 1x Micro-spotting Solution Plus (TeleChem) at a concentration of 0·25–0·75 µg µl–1. DNA was spotted onto CMT–GAPS-coated glass slides (Corning) by using Stealth micro-spotting pins (TeleChem).

Biological material.
C57BL/6 mice were inoculated intracerebrally with 20 µl 1 % brain homogenate (obtained from the TSE Resource Centre, Institute for Animal Health, Compton, UK). Transmissions of mouse-adapted scrapie, ME7 and 79a, were set up in C57BL/6 mice. The mice were sacrificed when they showed classically defined clinical signs of scrapie (uncoordinated gait, flaccid paralysis of the hind limbs, rigidity, righting reflex abolished). This was between 148 and 153 days for 79a and between 153 and 160 days for ME7 scrapie. These incubation periods were concordant with results from previous studies (Fraser & Dickinson, 1973; Bruce et al., 1991). To confirm the diagnosis, a number of representative mice were examined by histology and immunohistochemistry and the presence of PrPSc and strain-specific neuropathology (lesion profiles) was confirmed (Fraser & Dickinson, 1973; Bruce et al., 1991). Brains were removed from the remainder of the infected mice and total RNA was extracted. Mock-infected control mice were inoculated intracerebrally with 20 µl PBS.

Although ten mice were inoculated for each experimental group, there were some early fatalities during the time course of the experiment. Therefore, between eight and ten mice per sample group were used for RNA extraction. Because of the long time course of infection in mice, we stored mouse brain tissue at –80 °C in RNAlater solution (Ambion) until all experimental time points were completed. All procedures were approved by the Canadian Science Centre for Human and Animal Health Animal Care Committee.

RNA purification and cDNA preparation.
Total RNA was isolated from stored tissue samples by homogenization in Trizol reagent (Invitrogen). RNA was further purified by using RNeasy columns (Qiagen) according to the manufacturer's instructions. Samples (10 µg) of total RNA were used as a template for oligo(dT)-primed reverse transcription and incorporation of aminoallyl-dUTP (Sigma) into the cDNA, essentially following the protocol published by the Institute for Genomic Research, Manassas, VA, USA (TIGR; available at http://pga.tigr.org/sop/M004_1a.pdf). The remaining RNA template was hydrolysed and the cDNA product purified by using the QIAquick PCR purification system (Qiagen). cDNA was dried down and then resuspended in 0·1 M Na2CO3 buffer, pH 9·0, and incubated for 1 h in either an Alexa Fluor555 (AF555) or Alexa Fluor647 (AF647) monofunctional reactive dye (Molecular Probes), made up in DMSO. Labelled cDNA was also purified by using QIAquick PCR purification columns.

Array hybridization and scanning.
Labelled cDNA was dried down and resuspended in 60 µl DIG Easy Hyb hybridization buffer (Roche) containing calf thymus DNA (Sigma) and yeast tRNA (Life Technologies). Labelled cDNAs were applied to arrays under M Series lifter slips (Erie Scientific) and the slides were incubated in a hybridization chamber at 42 °C for 16 h. Following hybridization, lifter slips were removed and the arrays washed three times for 5 min each in 1x SSC, 0·2 % SDS, and then three times for 5 min each in 0·1x SSC, 0·2 % SDS that had been pre-warmed to 42 °C. After a final wash in 0·1x SSC at room temperature, slides were centrifuged to dryness. Slides were scanned immediately by using a VersArray scanner (Bio-Rad) at appropriate laser power and photomultiplier tube settings, so that high-intensity spots were not saturated. Spots were quantified and background signals removed by using ArrayPro software (Media Cybernetics).

Data analysis.
Data were stored in and analysed with the GeneTraffic Microarray Database and Analysis System (Iobion Informatics) as well as the software packages Significance Analysis for Microarrays (SAM) (Tusher et al., 2001) and GeneMaths (Applied Maths). The raw data were filtered so that individual spots had to pass a number of quality criteria, including minimum intensity levels and minimum signal-to-background ratios. Genes that passed these criteria were used for further data analysis. Intensity values for each slide were normalized by using a linear regression smoothing algorithm (Loess best-fit) over individual array sub-grids. Log2 ratios for the resulting filtered and normalized intensity values were used for all further statistical analysis.

SAM analysis.
To identify the genes that were most significantly different in expression between clinically infected mice and age-matched, mock-infected mice, a one-class SAM analysis with a false discovery rate (FDR) of 10 % was used. To increase the confidence level, the only genes selected were those in which the log2 intensity ratios between mock-infected and infected mice were over a threshold level. The threshold used was a 1·3-fold change in the ratios between mock-infected and infected intensities for a given gene.

To identify genes that were most significantly different between time points after infection, a SAM multiclass analysis with an FDR of 10 % was used. A different class in the analysis was used to describe each of the time points at which mice were sacrificed. The results were visualized on a two-dimensional heat map after Euclidean distance hierarchical clustering of the SAM-selected genes.

Real-time PCR.
To remove any contaminating genomic DNA from the RNA template, approximately 2 µg total RNA was treated with DNase I by using the DNA-free system (Ambion). Purified total RNA was used as the template for oligo(dT)-primed reverse transcription, using Superscript II reverse transcriptase (Invitrogen). To remove any remaining complementary RNA, the cDNA product was treated with RNase H (Invitrogen). After RNase H treatment, the cDNA was purified by using the QIAquick PCR purification system. The purified cDNA product was adjusted to 50 ng µl–1 and used as the template for real-time PCR, using the LightCycler platform (Roche). All reagents were supplied in the LightCycler FastStart Master SYBR Green kit (Roche) and were prepared according to the manufacturer's instructions. Primers were designed and provided by TIB Molbiol LLC for {beta}2-microglobulin (B2m), clusterin (Clu) and apolipoprotein D (apoD): B2m fwd, 5'-CTGACCGGCCTGTATGCTA-3', and B2m rev, 5'-CGATCCCAGTAGACGGTCTT-3'; Clu fwd, 5'-TGAAGATTCTCCTGCTGTGC-3', and Clu rev, 5'-TGCCTTCAGCTTCATTTCAG-3'; apoD fwd, 5'-TGAAAACTATGCCCTCGTCTAC-3', and apoD rev, 5'-GCAGTTCGCTTGATCTGTT-3'. Primers for gapd and Egr1 were designed by using the software PrimerSelect (DNAStar): gapd fwd, 5'-CACGGCAAATTCAACGGCACAGT-3' and gapd rev, 5'-TGGGGGCATCGGCAGAAGG-3'; Egr1 fwd, 5'-CATAATTGCCTTGTTGTGAGACTG-3' and Egr1 rev, 5'-CGAACCGGGAACGAGGGAAGTC-3'. Reactions were set up in microcapillary tubes with the following final concentrations: 0·4 µM each of the appropriate forward and reverse primers; 3·0 mM MgCl2 for gapd, B2m and Clu or 4·0 mM MgCl2 for apoD and Egr1; 1x SYBR Green master mix; and 2 µl cDNA template. Cycling conditions were as follows: denaturation (95 °C for 10 min), amplification and quantification (95 °C for 5 s, 55 or 63 °C for 5 s and 72 °C for 10–13 s, with a single fluorescence measurement at the end of the 72 °C extension) repeated 40 times, a melting-curve program (50–95 °C with a heating rate of 0·2 °C s–1 and a continuous fluorescence measurement) and a cooling step to 40 °C. Data were analysed by using the LightCycler analysis and RelQuant software packages (Roche).


   RESULTS
Top
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Identification of mouse CNS genes that are expressed differentially during clinical stages of infection with mouse-adapted scrapie
C57BL/6 mice were inoculated by intracerebral injection of brain homogenate from C57BL/6 mice clinically infected with ME7 and 79a strains of scrapie, as described by Scott & Fraser (1984). These strains were chosen so that we could identify genes that are expressed differentially in a generalized response to prion infection and not those that are dependent on the different pathological characteristics of each strain. Pathological differences between the two strains in C57BL/6 mice were assessed by histology, after which slightly greater vacuolation in clinical ME7 disease was observed, with less vacuolation seen in the hypothalamus and superior colliculus of C57BL/6 mice that were clinically infected with scrapie strain 79a. By immunohistochemistry, much finer and less intense foci of prion protein accumulation were also observed in the 79a-infected mice.

RNA was isolated from the brain tissue of the mice when they showed classically defined clinical signs of scrapie. Mouse gene expression was analysed by two-colour microarray experiments, using labelled total RNA isolated from an individual infected mouse brain versus reference RNA. The reference RNA was pooled RNA collected from equivalent numbers of age-matched, mock-infected, control mice. We prepared our own 11 136-element cDNA microarrays, where each element on the array was a PCR-generated amplicon from a mouse CNS-derived EST library (http://genome.uiowa.edu/projects/BMAP/index.html). In this way, we were able to specifically target transcripts expressed in the CNS, the large number of uncharacterized ESTs allowing for gene discovery. Raw data are available as supplementary material in JGV Online (and have also been deposited in the public database http://www.ncbi.nlm.nih.gov/geo/). The data from microarray hybridizations were analysed and filtered by using strict quality criteria to select spots to be used for further analysis. The resulting dataset, which contained 8151 genes, was analysed by using the software package SAM to identify changes in gene transcription that were related to prion infection. Analysis of microarray data with the SAM program has previously been shown to be more accurate than calculation of fold change, as determined by Northern blot (Tusher et al., 2001). SAM calculates a modified t-statistic (SAM score), based on the change in gene expression and SD across a group of biological replicates. The percentage of genes that are selected by chance, the FDR, is then calculated, based on permutations of the measurements for each gene. The FDR is then used to set a threshold SAM score and genes with scores over and above this threshold are identified as having statistically significant changes in expression. The user can adjust the threshold to create smaller or larger sets of genes that are called as significant.

We performed a SAM one-class response analysis, which assumes that each array in the dataset is equivalent and then determines significant differences in regulation in comparison with a control (in this case, the log2 ratios for infected mice versus mock-infected, age-matched control mice). The resulting plot for a one-class SAM analysis is shown in Fig. 1(a). The plot shows a high correlation between the observed and calculated expected gene-regulation values. In this case, we used a {delta} value for SAM that resulted in an estimation of <10 % false discoveries in a set of 304 genes that were predicted to be significantly differentially represented in the infected mice versus mock-infected mice. The SAM methodology calculates a statistic on the basis that differences between the groups in the analysis are larger than the differences within the group. In practice, this means that genes with a very small fold change between the two groups can have high SAM scores and, hence, be predicted as significant. A number of genes that were predicted by SAM to be significantly differentially represented in clinically infected mice versus control had low fold changes between the two groups (between 0·8 and 1·2). Given that these small differences would be difficult to validate by using real-time PCR and Northern blot, we further filtered the gene list that was produced by SAM to select only those genes with mean log2 ratios greater than 1·3. Fig. 1(b) shows the mean log2 ratio of each gene on the array versus the mean log2 signal intensity for each gene. In total, 158 genes that were predicted as significant by SAM, and that also had a threshold fold change of 1·3-fold, are highlighted. Of these, 138 genes showed upregulation in the clinical stage of scrapie infection and 20 were downregulated. Table 1 provides a brief description of those genes that have some functional annotation associated with them; a fuller version with hyperlinks is available as supplementary data in JGV Online.



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Fig. 1. Data to show selection of genes expressed differentially in mouse CNS at the end-stage of clinical infection with scrapie. (a) SAM analysis data, which generated 304 genes that showed up- or downexpression by using an FDR of 10 % (median predicted number of false discoveries, 19·3; {delta}=0·57). (b) Plot of the mean log2 ratios for each gene in all mice at the clinical end-point of scrapie versus mean log2 signal intensity for each gene.

 

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Table 1. Identification by microarray analysis of mouse CNS genes that are represented differentially in scrapie-infected brain

The SAM score is a modified t-statistic based on the change in gene expression and SD across the whole group of infected mice.

 
Confirmation of microarray findings
We performed multiple SAM two-class analysis of random groups of four microarrays within each class of array analysed (i.e. within the same time point). In each case, we found fewer than five genes that were predicted to be significant between the two groups (results not shown). We are therefore confident that the technical variation among our arrays is small and the genes that are predicted to be significant by SAM have biological significance. For further confirmation of our findings, we performed real-time PCR quantification for a representative group of our SAM-selected genes. The results of these assays are shown in Table 2. The levels of expression were comparable with those observed by microarray analysis.


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Table 2. Quantitative RT-PCR of selected genes confirming differential expression of representative differentially expressed genes shown in Table 1

Mean fold changes are shown at three different time points throughout infection (n=6).

 
Functional categories of differentially expressed genes
Although at least 40 % of the probes on our microarray represented functionally uncharacterized genes, the remaining genes were used for functional data mining. In an initial step to collect information on the genes that we had selected previously, we attempted to create a functional profile to characterize broad features of the biological processes that underlie prion disease. An online software package, Onto-Express, was used to gain more information about the gene ontologies that best described our selected genes, as defined by the Gene Ontology Consortium (GO) (http://www.geneontology.org/; Draghici et al., 2003). The major biological processes in which the selected genes are involved are shown in Fig. 2.



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Fig. 2. Functional profiling of the genes that exhibited differential gene expression in clinically scrapie-infected mice to reveal significantly impacted biological processes. Analysis was performed by using Onto-express software for comparison of examination of gene ontologies and by reference to publications (Draghici et al., 2003). A summary of biological processes that were predicted to be most significant among our group of 158 genes is presented as a bar chart. Only groups containing three or more genes are shown. The Bonferroni-corrected P value provides a measure of statistical confidence, based on the proportions of each GO group scored as significant versus the genes represented on the microarray as a whole, and the probability that they could be selected by chance.

 
Of the 158 array probes that were selected as exhibiting differential expression in mice clinically infected with scrapie, 21 represented as yet completely uncharacterized genes.

Identification of host genes whose expression profiles change over the time course of infection with mouse-adapted scrapie
Many of the genes that were identified at clinical stages of prion disease were recognized as showing a generalized response to CNS disease. To examine the regulation of genes at early stages of infection, before substantial damage to the brain, we studied gene-expression profiles in preclinically infected hosts. Mice were inoculated with either the ME7 or the 79a scrapie strain contained in brain homogenate, as for the previous experiment. Groups of mice were sacrificed at 21 days post-infection (p.i.), 100 days p.i. and the clinical end-point of infection, between 148 (79a) and 160 (ME7) days p.i.

The 21-day time point was used to identify genes showing an initial acute response to the prion agent, whereas the 100-day time point was taken because it represents a stage of disease where pathological changes are evident in the CNS, but no clinical signs are evident. Immunohistochemistry on infected mouse brains that were analysed at 100 days p.i. showed a build-up of PrPSc and some vacuolation. PrPSc accumulation was most evident in the ME7-infected mice, with vacuolation being more pronounced at this stage in the brains of mice that were infected with the 79a strain of scrapie. Control groups were as for the previous experiment: groups of equivalent numbers of age-matched, mock-infected mice. After the mice were sacrificed, tissue was stored in RNAlater so that all microarrays were performed together on the same production batch for experimental consistency. Data were collected and pre-processed as described previously. Analysis of the data was performed to identify differences in gene expression at the three different stages of the disease for the two strains of mouse-adapted scrapie. One approach to finding differences in gene-expression profiles between different classes of samples is to show that there are statistically significant numbers of genes that separate the classes (i.e. more than would be expected by chance). To this end, we used the ‘multiclass' function of the SAM software to establish the major genes that are expressed differentially between mice infected with the prion agent at time points very early in infection (21 days p.i.), just after the mid-point (100 days p.i.) and at the clinical phase of infection (between 148 and 160 days p.i.). To this, we added the SAM ‘one-class' analysis list for each time point. After removing some of the many duplicated genes between the two lists, we were left with 217 genes. Fig. 3 shows a hierarchical cluster image of these 217 genes.



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Fig. 3. Genes that are expressed differentially in mice infected with scrapie versus mock-infected mice at different stages throughout the course of disease. Multiclass and one-class SAM analysis was used to identify the genes in mouse CNS that are expressed differentially at and between different time points throughout the course of infection. By using an FDR of 10 % in each case, 217 genes were selected. Arrays were standardized by subtraction of the mean log2 ratio of each array and division by the root mean square of each array. Genes were clustered by Euclidean distance coefficient and visualized as a heat map. Colour-intensity levels of the squares correlate with the degree of gene expression in the mouse brain samples. Panels (a–d) highlight interesting clusters of genes that are expressed differentially at early stages of infection, 21 days and/or 100 days p.i. Each panel represents the mean log2 ratios of all the genes in the cluster plotted for each mouse. Arrows denote mice that were sacrificed at either 21 or 100 days p.i. or at the end-stage of clinical disease. Coloured bars adjacent to the tree represent genes that are expressed differentially during the end-stage of clinical disease: red represents upregulation and green represents downregulation.

 
Although the major clusters of genes showing differential expression during prion infection were evident only late in the disease process at the clinical stages of infection, we did identify a number of interesting clusters of genes that are apparently expressed differentially at earlier stages of the disease process. Gene clusters that showed significant changes at preclinical stages (early and middle) of infection with scrapie are represented in Fig. 3(a–d). Adjacent plots show the mean log2 ratios for genes within the cluster for each individual infected mouse. GenBank accession numbers for each gene in each of these four clusters are provided in Table 3. These four clusters represent genes that are: (a) genes upregulated at 21 days; (b) genes downregulated throughout the course of disease; (c) genes downregulated at 21 days; and (d) genes upregulated at the mid-point (100 days p.i.) in the disease process.


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Table 3. Mouse CNS genes identified by microarray analysis as differentially expressed in response to scrapie at the preclinical stages of infection

Genes are arranged in cluster groups corresponding to those highlighted in Fig. 3. These four clusters represent genes that are: (A) upregulated at 21 days; (B) downregulated throughout the course of disease; (C) downregulated at 21 days; and (D) upregulated at the mid-point (100 days p.i.) of the disease process.

 
A number of genes showed significant changes in expression very early after inoculation (21 days). As the control mice for these experiments were inoculated with PBS, it was possible that the early expression changes could have been due to the presence of foreign brain homogenate in the inocula, rather than being specific to the disease process. A further group of ten control mice was therefore inoculated with uninfected C57BL/6 brain homogenate, also obtained from the TSE Resource Centre, UK, and sacrificed at 21 days. Arrays were performed using RNA from the homogenate-inoculated mice versus the 21-day pooled control that was used previously. The resulting log2 ratios were analysed using a SAM one-class analysis. No significant changes in gene expression were predicted in the analysis and so we concluded that the early changes found were indeed a result of a response to prion infection.


   DISCUSSION
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
This study represents a global analysis of the overall transcriptional response in CNS tissue of mice in response to prion infection. It is evident from the results presented that the major response found was a detectable increase in gene expression of over 130 genes in the clinical stages of the disease. Some of these changes were evident at 100 days p.i. A smaller number of genes exhibited a concordant decrease in expression. Stringent filtering and use of the SAM program permitted the detection of altered expression of 138 upregulated and 20 downregulated genes in the CNS of mice that were clinically infected with mouse-adapted scrapie (Table 1).

Many of the individual genes described have been identified previously as having an association with the neurodegenerative process, both in prion disease and in numerous other degenerative diseases affecting the nervous system, such as Alzheimer's disease. The identification of significant numbers of genes that have been described previously as having an association with neurodegenerative disorders is compelling evidence that we were able to pick out, with considerable accuracy, significantly differentially expressed genes from our microarray data. This was despite the use of total brain tissue for the analysis, in which differential gene expression occurring only in some specific cell types may be diluted by expression throughout the whole organ. The majority of gene-expression ratios observed were <1·75-fold. Examination of the gene ontology assignments of these genes begins to define biological processes that may be involved in the pathogenesis of prion-induced neurodegeneration. Functional groups of genes identified in this study included several major groups: secreted extracellular proteins, lysosomal proteases, defence and immune response-related proteins, signal transduction genes and cell growth- and biogenesis-related genes (Baker & Manuelidis, 2003; Dandoy-Dron et al., 1998; Riemer et al., 2000).

Many members of the protease gene family have previously been described as showing upregulation in scrapie-infected CNS tissue and cells. Cysteine proteases have been implicated in the process of conversion of PrPc to PrPSc and thus in the pathogenesis of prion disease (Zhang et al., 2003). Increased expression of cysteine proteases has, however, been described in other neurodegenerative diseases and probably represents a generalized compensatory response against abnormal protein accumulation, and so this effect is perhaps not specific to prion pathogenesis (Myerowitz et al., 2002; Nixon et al., 2001). We also identified changes in expression of several immune-response genes that have also been implicated in prion pathogenesis, including B2m, Ly86, Ly6c and Fcer1g (Baker & Manuelidis, 2003). These immune response-related genes have also been described as being upregulated in other diseases, including Alzheimer's disease, Huntington's disease and also in acute viral infections. Interestingly, the Egr1 gene, encoding the transcription factor early growth response 1 (which is involved in the regulation of growth, differentiation and senescence), was strongly downregulated at the clinical stage of infection (Krones-Herzig et al., 2003; Liu et al., 1996). Quantitative RT-PCR results also identified detectable downregulation at 100 days p.i. (Table 2). Decreased expression of Egr1 is frequently observed in breast tumours and glioblastomas and its re-expression has been found to have a growth suppression effect. In contrast, Egr1 expression has been shown to be upregulated in prostate tumours and in response to infection with some neurotropic viruses (Saha & Rangarajan, 2003) and during Alzheimer's disease (MacGibbon et al., 1997). Our results suggest a possible role for the negative regulation of Egr1 in prion pathogenesis.

Much of the differential expression observed during the clinical stage of disease reflects gross changes that are probably indicative of a generalized response to damage within the brain and the poor clinical condition of the mice, rather than having a direct role in prion pathogenesis. In any case, it would be difficult to distinguish between generalized disease and prion-specific responses at this stage.

We were, therefore, very interested to identify changes in gene expression that occur early in the infection cycle, as these responses may provide a clearer indication as to the molecular mechanisms of pathogenesis, as well as being candidate preclinical biomarkers. We were able to identify a small number of genes that appear to be regulated differentially at 21 and 100 days p.i. There were 21 genes that were upregulated at 21 days p.i. and five genes that were downregulated, none of which showed differential expression at the clinical end-point of infection. The upregulated genes include Nf2, a putative tumour-suppressor gene involved in proliferation, and polb, a gene implicated as a cell-death mediator in neurodegenerative conditions (Ikeda et al., 1999). DNA polymerase {beta} plays an essential role in neurogenesis; mice devoid of this gene die as a result of impaired neurogenesis and apoptosis in the CNS (Sugo et al., 2000). Induction of polb has been shown to occur in neurons by treatment with a {beta}-amyloid peptide and as a response to hypoxia (Copani et al., 2002; Mishra et al., 2003).

Downregulated genes included the transcription factor Cebp, another gene involved in cell differentiation and proliferation, which is induced by hormonal stimuli. Transthyretin was also downregulated 21 days p.i.; this protein is responsible for transport of thyroid hormones to the developing brain.

Taken together with the previously described downregulation of Egr1, the biological functions of the observed differentially expressed genes suggest that the regulation of development and differentiation within the brain may be disrupted early in the prion disease process. This may be an important factor in the molecular pathogenesis of prion-induced neurodegeneration. One possibility is that neurones are driven out of senescence into a growth cycle leading to apoptosis. This would concur with recent results indicating that the loss of dendrites and synapses is an early event in the pathogenesis of prion-induced neurodegeneration, coinciding with the deposition of PrPSc and preceding neuronal cell-loss or signs of apoptosis (Cunningham et al., 2003; Jeffrey et al., 2000).

Perhaps the most interesting group of genes we identified comprised the six genes that uniquely showed downregulation throughout the entire time course of infection. These genes may have potential as preclinical biomarkers for prion infection. Three of these genes, Hbb-y, Hba-a1 and Nckap1, represent haematopoietic system gene transcripts. The Nckap1 gene product has been suggested to play a role in haematopoiesis and was shown to be strongly downregulated in sporadic Alzheimer's disease (Baumgartner et al., 1995; Yamamoto et al., 2001). A recent study showed that {alpha}-haemoglobin stabilizing factor is downregulated during prion disease in animals, also suggesting that the haematopoietic system is involved in prion disease from an early stage (Miele et al., 2001; Turner, 2003).

Another gene that is downregulated throughout the time course of infection is Nfkbia, which encodes the inhibitor of NF-{kappa}B; this protein functions by sequestering NF-{kappa}B to prevent the transcription of a number of anti-apoptotic genes that protect cells from a variety of extracellular stress stimuli (Beg & Baltimore, 1996; Liu et al., 1996; Wang et al., 1996). This downregulation of Nfkbia may result in the previously reported increase in activation of NF-{kappa}B in scrapie-infected mice and thereby play a role in the response of CNS cells to prion infection (Kim et al., 1999).

We are presently targeting some of these genes for validation at the RNA and protein levels in the CNS and other tissues to determine whether their differential expression can be used as surrogate markers of infection. In addition, we are further investigating the contribution of deregulation of genes that are involved in haematopoiesis and neurogenesis to prion pathogenesis. Another target for further investigation is how the route of infection affects the differential gene-expression changes that we observed early in infection. Although using mice as a model system means that no route of infection is truly ‘natural’, it may be that early events in pathogenesis could be better understood by using an intraperitoneal or oral inoculation methodology. These routes of infection are less reproducible in terms of the length of incubation period following inoculation and this raises reproducibility issues for microarray experiments, which work optimally with stringent biological replication. We are currently addressing these issues in our laboratory. Future studies will require techniques such as laser-capture microdissection to investigate the response of individual cell populations to prion disease, in order to continue to unravel the process of pathogenesis.


   ACKNOWLEDGEMENTS
 
We acknowledge grant support from the Canadian Institute of Health Research (S. B., R. L. S.), Canadian Biotechnology Strategy Fund: Genomics Initiative for Government Laboratories (S. B.), Manitoba Medical Services Foundation (R. B.), NIH contract N01-NS-0-2327 (M. C.) and continuing support from Health Canada's Blood Safety Program. We would like to thank Nicole Beausoleil and the staff of the NML animal house for the maintenance of the mice; A. Ahamed for technical assistance; DNA core staff at the NML for DNA sequencing and synthesis of oligonucleotides; and especially S. Tyson for assistance with amplification of the BMAP library and C. Ouellette for assistance with microarray preparation.


   REFERENCES
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
 
Baker, C. A. & Manuelidis, L. (2003). Unique inflammatory RNA profiles of microglia in Creutzfeldt–Jakob disease. Proc Natl Acad Sci U S A 100, 675–679.[Abstract/Free Full Text]

Baumgartner, S., Martin, D., Chiquet-Ehrismann, R., Sutton, J., Desai, A., Huang, I., Kato, K. & Hromas, R. (1995). The HEM proteins: a novel family of tissue-specific transmembrane proteins expressed from invertebrates through mammals with an essential function in oogenesis. J Mol Biol 251, 41–49.[CrossRef][Medline]

Beg, A. A. & Baltimore, D. (1996). An essential role for NF-{kappa}B in preventing TNF-{alpha}-induced cell death. Science 274, 782–784.[Abstract/Free Full Text]

Bruce, M. E., McConnell, I., Fraser, H. & Dickinson, A. G. (1991). The disease characteristics of different strains of scrapie in Sinc congenic mouse lines: implications for the nature of the agent and host control of pathogenesis. J Gen Virol 72, 595–603.[Abstract]

Copani, A., Sortino, M. A., Caricasole, A., Chiechio, S., Chisari, M., Battaglia, G., Giuffrida-Stella, A. M., Vancheri, C. & Nicoletti, F. (2002). Erratic expression of DNA polymerases by {beta}-amyloid causes neuronal death. FASEB J 16, 2006–2008.[Free Full Text]

Cunningham, C., Deacon, R., Wells, H., Boche, D., Waters, S., Picanco Diniz, C., Scott, H., Rawlins, J. N. P. & Perry, V. H. (2003). Synaptic changes characterize early behavioural signs in the ME7 model of murine prion disease. Eur J Neurosci 17, 2147–2155.[CrossRef][Medline]

Dandoy-Dron, F., Guillo, F., Benboudjema, L., Deslys, J. P., Lasmézas, C., Dormont, D., Tovey, M. G. & Dron, M. (1998). Gene expression in scrapie. Cloning of a new scrapie-responsive gene and the identification of increased levels of seven other mRNA transcripts. J Biol Chem 273, 7691–7697.[Abstract/Free Full Text]

Draghici, S., Khatri, P., Bhavsar, P., Shah, A., Krawetz, S. A. & Tainsky, M. A. (2003). Onto-Tools, the toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Nucleic Acids Res 31, 3775–3781.[Abstract/Free Full Text]

Duguid, J. R. & Dinauer, M. C. (1990). Library subtraction of in vitro cDNA libraries to identify differentially expressed genes in scrapie infection. Nucleic Acids Res 18, 2789–2792.[Abstract]

Fraser, H. & Dickinson, A. G. (1973). Scrapie in mice. Agent-strain differences in the distribution and intensity of grey matter vacuolation. J Comp Pathol 83, 29–40.[Medline]

Ikeda, K., Saeki, Y., Gonzalez-Agosti, C., Ramesh, V. & Chiocca, E. A. (1999). Inhibition of NF2-negative and NF2-positive primary human meningioma cell proliferation by overexpression of merlin due to vector-mediated gene transfer. J Neurosurg 91, 85–92.[Medline]

Jeffrey, M., Halliday, W. G., Bell, J., Johnston, A. R., MacLeod, N. K., Ingham, C., Sayers, A. R., Brown, D. A. & Fraser, J. R. (2000). Synapse loss associated with abnormal PrP precedes neuronal degeneration in the scrapie-infected murine hippocampus. Neuropathol Appl Neurobiol 26, 41–54.[CrossRef][Medline]

Kim, J.-I., Ju, W.-K., Choi, J.-H., Kim, J., Choi, E.-K., Carp, R. I., Wisniewski, H. M. & Kim, Y.-S. (1999). Expression of cytokine genes and increased nuclear factor-kappa B activity in the brains of scrapie-infected mice. Brain Res Mol Brain Res 73, 17–27.[CrossRef][Medline]

Krones-Herzig, A., Adamson, E. & Mercola, D. (2003). Early growth response 1 protein, an upstream gatekeeper of the p53 tumor suppressor, controls replicative senescence. Proc Natl Acad Sci U S A 100, 3233–3238.[Abstract/Free Full Text]

Liu, Z.-G., Hsu, H., Goeddel, D. V. & Karin, M. (1996). Dissection of TNF receptor 1 effector functions: JNK activation is not linked to apoptosis while NF-{kappa}B activation prevents cell death. Cell 87, 565–576.[Medline]

MacGibbon, G. A., Lawlor, P. A., Walton, M., Sirimanne, E., Faull, R. L. M., Synek, B., Mee, E., Connor, B. & Dragunow, M. (1997). Expression of Fos, Jun, and Krox family proteins in Alzheimer's disease. Exp Neurol 147, 316–332.[CrossRef][Medline]

Manson, J. C., Jamieson, E., Baybutt, H. & 10 other authors (1999). A single amino acid alteration (101L) introduced into murine PrP dramatically alters incubation time of transmissible spongiform encephalopathy. EMBO J 18, 6855–6864.[Abstract/Free Full Text]

Miele, G., Manson, J. & Clinton, M. (2001). A novel erythroid-specific marker of transmissible spongiform encephalopathies. Nat Med 7, 361–364.[CrossRef][Medline]

Mishra, O. P., Akhter, W., Ashraf, Q. M. & Delivoria-Papadopoulos, M. (2003). Hypoxia-induced modification of poly (ADP-ribose) polymerase and DNA polymerase {beta} activity in cerebral cortical nuclei of newborn piglets: role of nitric oxide. Neuroscience 119, 1023–1032.[CrossRef][Medline]

Myerowitz, R., Lawson, D., Mizukami, H., Mi, Y., Tifft, C. J. & Proia, R. L. (2002). Molecular pathophysiology in Tay–Sachs and Sandhoff diseases as revealed by gene expression profiling. Hum Mol Genet 11, 1343–1351.[Abstract/Free Full Text]

Nixon, R. A., Mathews, P. M. & Cataldo, A. M. (2001). The neuronal endosomal-lysosomal system in Alzheimer's disease. J Alzheimers Dis 3, 97–107.[Medline]

Prusiner, S. B. (1998). Prions. Proc Natl Acad Sci U S A 95, 13363–13383.[Abstract/Free Full Text]

Riemer, C., Queck, I., Simon, D., Kurth, R. & Baier, M. (2000). Identification of upregulated genes in scrapie-infected brain tissue. J Virol 74, 10245–10248.[Abstract/Free Full Text]

Saha, S. & Rangarajan, P. N. (2003). Common host genes are activated in mouse brain by Japanese encephalitis and rabies viruses. J Gen Virol 84, 1729–1735.[Abstract/Free Full Text]

Scott, J. R. & Fraser, H. (1984). Degenerative hippocampal pathology in mice infected with scrapie. Acta Neuropathol 65, 62–68.[Medline]

Sugo, N., Aratani, Y., Nagashima, Y., Kubota, Y. & Koyama, H. (2000). Neonatal lethality with abnormal neurogenesis in mice deficient in DNA polymerase {beta}. EMBO J 19, 1397–1404.[Abstract/Free Full Text]

Turner, M. L. (2003). vCJD screening and its implications for transfusion – strategies for the future? Blood Coagul Fibrinolysis 14 (Suppl. 1), S65–S68.[CrossRef][Medline]

Tusher, V. G., Tibshirani, R. & Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98, 5116–5121.[Abstract/Free Full Text]

Wang, C.-Y., Mayo, M. W. & Baldwin, A. S., Jr (1996). TNF- and cancer therapy-induced apoptosis: potentiation by inhibition of NF-{kappa}B. Science 274, 784–787.[Abstract/Free Full Text]

Yamamoto, A., Suzuki, T. & Sakaki, Y. (2001). Isolation of hNap1BP which interacts with human Nap1 (NCKAP1) whose expression is down-regulated in Alzheimer's disease. Gene 271, 159–169.[CrossRef][Medline]

Zhang, Y., Spiess, E., Groschup, M. H. & Bürkle, A. (2003). Up-regulation of cathepsin B and cathepsin L activities in scrapie-infected mouse Neuro2a cells. J Gen Virol 84, 2279–2283.[Abstract/Free Full Text]

Received 13 March 2004; accepted 9 July 2004.



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