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
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ABSTRACT |
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Raw data and a hyperlinked version of Table 1 are available as supplementary material in JGV Online.
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INTRODUCTION |
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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
);
2-microglobulin, F4/80; metallothionein II (Duguid & Dinauer, 1990
); and
-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.
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METHODS |
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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 µl1 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 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 1013 s, with a single fluorescence measurement at the end of the 72 °C extension) repeated 40 times, a melting-curve program (5095 °C with a heating rate of 0·2 °C s1 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).
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RESULTS |
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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
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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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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DISCUSSION |
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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
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
-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
-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-B; this protein functions by sequestering NF-
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-
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.
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ACKNOWLEDGEMENTS |
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Received 13 March 2004;
accepted 9 July 2004.
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