A systems approach to prion disease
2009; Springer Nature; Volume: 5; Issue: 1 Linguagem: Inglês
10.1038/msb.2009.10
ISSN1744-4292
AutoresDaehee Hwang, Inyoul Y. Lee, Hyuntae Yoo, Nils Gehlenborg, Ji‐Hoon Cho, Brianne Petritis, David Baxter, Rose Pitstick, Rebecca Young, Doug Spicer, Nathan D. Price, John G. Hohmann, Stephen J. DeArmond, George A. Carlson, Leroy Hood,
Tópico(s)Neurological diseases and metabolism
ResumoArticle24 March 2009Open Access A systems approach to prion disease Daehee Hwang Daehee Hwang Institute for Systems Biology, Seattle, WA, USA I-Bio Program & Department of Chemical Engineering, POSTECH, Pohang, Republic of Korea Search for more papers by this author Inyoul Y Lee Inyoul Y Lee Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Hyuntae Yoo Hyuntae Yoo Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Nils Gehlenborg Nils Gehlenborg Institute for Systems Biology, Seattle, WA, USA Microarray Team, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK Search for more papers by this author Ji-Hoon Cho Ji-Hoon Cho I-Bio Program & Department of Chemical Engineering, POSTECH, Pohang, Republic of Korea Search for more papers by this author Brianne Petritis Brianne Petritis Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author David Baxter David Baxter Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Rose Pitstick Rose Pitstick McLaughlin Research Institute, Great Falls, MT, USA Search for more papers by this author Rebecca Young Rebecca Young McLaughlin Research Institute, Great Falls, MT, USA Search for more papers by this author Doug Spicer Doug Spicer McLaughlin Research Institute, Great Falls, MT, USA Search for more papers by this author Nathan D Price Nathan D Price Department of Chemical and Biomolecular Engineering & Institute for Genomic Biology, University of Illinois, Urbana, IL, USA Search for more papers by this author John G Hohmann John G Hohmann Allen Brain Institute, Seattle, WA, USA Search for more papers by this author Stephen J DeArmond Stephen J DeArmond Department of Pathology, University of California, San Francisco, CA, USA Search for more papers by this author George A Carlson Corresponding Author George A Carlson McLaughlin Research Institute, Great Falls, MT, USA Search for more papers by this author Leroy E Hood Corresponding Author Leroy E Hood Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Daehee Hwang Daehee Hwang Institute for Systems Biology, Seattle, WA, USA I-Bio Program & Department of Chemical Engineering, POSTECH, Pohang, Republic of Korea Search for more papers by this author Inyoul Y Lee Inyoul Y Lee Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Hyuntae Yoo Hyuntae Yoo Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Nils Gehlenborg Nils Gehlenborg Institute for Systems Biology, Seattle, WA, USA Microarray Team, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK Search for more papers by this author Ji-Hoon Cho Ji-Hoon Cho I-Bio Program & Department of Chemical Engineering, POSTECH, Pohang, Republic of Korea Search for more papers by this author Brianne Petritis Brianne Petritis Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author David Baxter David Baxter Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Rose Pitstick Rose Pitstick McLaughlin Research Institute, Great Falls, MT, USA Search for more papers by this author Rebecca Young Rebecca Young McLaughlin Research Institute, Great Falls, MT, USA Search for more papers by this author Doug Spicer Doug Spicer McLaughlin Research Institute, Great Falls, MT, USA Search for more papers by this author Nathan D Price Nathan D Price Department of Chemical and Biomolecular Engineering & Institute for Genomic Biology, University of Illinois, Urbana, IL, USA Search for more papers by this author John G Hohmann John G Hohmann Allen Brain Institute, Seattle, WA, USA Search for more papers by this author Stephen J DeArmond Stephen J DeArmond Department of Pathology, University of California, San Francisco, CA, USA Search for more papers by this author George A Carlson Corresponding Author George A Carlson McLaughlin Research Institute, Great Falls, MT, USA Search for more papers by this author Leroy E Hood Corresponding Author Leroy E Hood Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Author Information Daehee Hwang1,2,‡, Inyoul Y Lee1,‡, Hyuntae Yoo1,‡, Nils Gehlenborg1,3, Ji-Hoon Cho2, Brianne Petritis1, David Baxter1, Rose Pitstick4, Rebecca Young4, Doug Spicer4, Nathan D Price7, John G Hohmann5, Stephen J DeArmond6, George A Carlson 4 and Leroy E Hood 1 1Institute for Systems Biology, Seattle, WA, USA 2I-Bio Program & Department of Chemical Engineering, POSTECH, Pohang, Republic of Korea 3Microarray Team, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK 4McLaughlin Research Institute, Great Falls, MT, USA 5Allen Brain Institute, Seattle, WA, USA 6Department of Pathology, University of California, San Francisco, CA, USA 7Department of Chemical and Biomolecular Engineering & Institute for Genomic Biology, University of Illinois, Urbana, IL, USA ‡These authors contributed equally to this work *Corresponding authors. McLaughlin Research Institute, 1520 23rd Street South, Great Falls, MT 59405, USA. Tel.: +1 406 454 6044; Fax: +1 406 454 6019; E-mail: [email protected] for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. Tel.: +1 206 732 1201; Fax: +1 206 732 1254; E-mail: [email protected] Molecular Systems Biology (2009)5:252https://doi.org/10.1038/msb.2009.10 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Prions cause transmissible neurodegenerative diseases and replicate by conformational conversion of normal benign forms of prion protein (PrPC) to disease-causing PrPSc isoforms. A systems approach to disease postulates that disease arises from perturbation of biological networks in the relevant organ. We tracked global gene expression in the brains of eight distinct mouse strain–prion strain combinations throughout the progression of the disease to capture the effects of prion strain, host genetics, and PrP concentration on disease incubation time. Subtractive analyses exploiting various aspects of prion biology and infection identified a core of 333 differentially expressed genes (DEGs) that appeared central to prion disease. DEGs were mapped into functional pathways and networks reflecting defined neuropathological events and PrPSc replication and accumulation, enabling the identification of novel modules and modules that may be involved in genetic effects on incubation time and in prion strain specificity. Our systems analysis provides a comprehensive basis for developing models for prion replication and disease, and suggests some possible therapeutic approaches. Synopsis A systems approach to disease postulates that disease arises from the pathological perturbation (genetic and/or environmental) of one or more biological networks in the relevant organ and hence to understand a disease one must study the dynamical changes in relevant biological networks during disease progression. We applied the systems approach analyzing brain transcriptomes to the experimentally tractable neurodegenerative diseases caused by prion infection of mice. Prions are unique among transmissible, disease-causing agents in that they replicate by conformational conversion of normal benign forms of prion protein (PrPC) to disease-specific PrPSc isoforms. Neuropathological features common to all prion diseases in mammals, which include bovine spongiform encephalopathy (BSE) in cows, Creutzfeldt–Jakob disease (CJD) in humans, and scrapie in sheep, can be conveniently subdivided into four well-defined pathological processes: prion replication and PrPSc accumulation (Prusiner, 2003), synaptic degeneration (Ishikura et al, 2005), microglia and astrocyte activation (Rezaie and Lantos, 2001; Perry et al, 2002), and neuronal cell death (Liberski et al, 2004). Data on pathological changes in prion disease have been derived in multiple laboratories that have viewed prion-induced neurodegeneration from different perspectives and with different preconceptions. Our comprehensive and independent systems analysis of the brain transcriptomes in normal and prion-infected mice provides gene expression correlates with pathological information and will aid in organizing the current abundance of data fragments into a coherent pathogenic model of prion disease. We tracked global gene expression in the brains of eight distinct mouse strain–prion strain combinations at 8–10 time points throughout the incubation periods (60–350 days) to capture the effects of prion strain, host genetics, and PrP concentration on disease incubation time (Figure 1). Approximately 7400 genes were differentially expressed genes (DEGs) in one or more of the combinations. Subtractive analyses using three inbred mouse strains and two prion strains reduced the data dimensionality from 7400 to a core of 333 DEGs that reflected effects of prion strain and Prnp genotype that appeared central to prion disease. Of these, 178 had not previously been reported to change in prion-infected mice. Gene expression results were combined with temporal patterns of PrPSc accumulation, pathology, gene ontology, protein interactions, and cell-specific gene expression data to generate hypothetical dynamic protein networks that could be associated with known pathological events in disease progression; 231 DEGs were mapped into these networks. Figure 4 is a snapshot of one of these networks (PrPSc accumulation) in a single mouse strain–prion strain combination at 14 weeks after inoculation, before any clinical signs are apparent. This figure includes a histoblot to track regional deposition of proteinase K-resistant PrPSc in the brain; histoblots were collected at each time point for each prion strain–mouse strain combination. The previously unidentified DEGs and those that could not be readily assigned to networks likely encode previously unidentified aspects of prion disease and subsets of these may reveal involvement of modules reflecting androgenic steroid, iron, or arachidonate/prostaglandin metabolism. All data and tools used in these studies are available online in a prion disease database (http://prion.systemsbiology.net) Grouping mice in the five core prion strain–mouse strain combinations according to differences in incubation time revealed 55 DEGs, the expression of which was significantly enriched only in groups with short incubation times (B6-RML, B6.I-301V, and FVB-RML). Similarly, grouping according to prion strain (RML or 301V) identified 39 DEGs enriched in RML prion-inoculated mice. Interestingly, the emphasis on pathways such as cholesterol metabolism or glycosaminoglycan biosynthesis as central to prion disease may reflect the widespread use of RML and related prion isolates in short incubation time mice and in cell culture. The five core mouse strain–prion strain combinations emphasize incubation time differences reflecting interactions of PrPSc with PrPC encoded by alternative alleles of Prnp. PrPC concentration can also affect incubation time, and differential gene expression was explored in FVB.129-Prnptm1Zrch/wt (0/+) mice that express half the normal amount of PrP and have a very long RML incubation time and in FVB-Tg(PrP-A)4053 (Tg4053) mice that overexpress PrP and have a very short incubation time. Among the 333 shared DEGs gleaned from five prion–wild-type mouse strain combinations, 311 DEGs also were changed in Prnp (0/+) mice (summarized in Figure 1). In contrast, Tg4053 mice PrP showed significant changes in only 125 of the 333 DEGs in the shared set. Prominent shared DEGs in most of the key shared modules exhibited patterns in Tg4053 mice that were similar to the core groups, though generally with differentials of smaller magnitude and closer in time to clinical illness than all other combinations of prions and mice. Perplexingly, prion-infected Tg4053 also had a unique set of highly significant DEGs that were not seen in any other mouse–prion combination. We have demonstrated here the power of comprehensive, global systems approaches to diseases as complex as prion infection, even when the data sets are restricted to gene expression profiles, and involve whole brain. The efficacy of using several strain combinations, prion and genetic backgrounds as biological filters to identify the network signals that are important for various disease-related processes is a striking lesson from our study. The new modules that have been connected to the disease, the strong alignment of the specific pathogenic processes with network changes, and the range of novel and sharpened hypotheses illustrate the power of this approach. We have confidence that with the addition of other data types, the attribution of network processes to brain regions, and the specific testing of hypotheses suggested here, that the systems medicine of prion disease (and other neurodegenerative diseases) will advance rapidly. This study also provided new insights into the power of systems approaches to formulate new strategies for blood diagnosis and treatment. Introduction Systems approaches to disease arise from a simple hypothesis—disease emerges from the functioning of one or more disease-perturbed networks (genetic and/or environmental perturbations) that alter the levels of proteins and other informational molecules controlled by these networks. The dynamically changing levels of disease-perturbed proteins (networks) across disease progression presumably explain the mechanisms of the disease. Systems approaches to biology or medicine have two cardinal features: (1) global analyses to generate comprehensive data sets in the disease-relevant organ or cells across the dynamically changing disease process (e.g. all mRNA, miRNA, or protein levels) and (2) the integration of different levels of biological information (DNA, mRNA, miRNA, protein, interactions, metabolites, networks, tissues, organs, and phenotypes) to generate hypotheses about the fundamental principles of the disease (Hood et al, 2004). In this study, we present a systems biology approach that effectively uses these two features, uses multiple inbred mouse strains, uses a deep understanding of prion biology and applies statistical data integration methods to deal with two striking challenges: (1) sorting out the signal-to-noise issues arising from the global disease-associated changes as both measurement noise and biological noise and (2) reducing enormous data dimensionality so that the processes can be identified and visualized for study. The keys to reducing this noise are to apply a deep understanding of biology to carry out subtractive data analyses that focus on particular biological issues—as well as to use integrative statistical methods. We applied the systems approach to experimentally tractable neurodegenerative diseases caused by prion infection of mice. Analysis of a large data set (∼20 million data points) revealed slightly more than 300 differentially expressed genes (DEGs) that may encode fundamental processes in prion disease. Prions are unique among transmissible, disease-causing agents in that they replicate by conformational conversion of normal benign forms of prion protein (PrPC) to disease-specific PrPSc isoforms. Neuropathological features common to all prion diseases in mammals, which include bovine spongiform encephalopathy (BSE) in cows, Creutzfeldt–Jakob disease (CJD) in humans, and scrapie in sheep, can be conveniently subdivided into four processes: prion (PrPSc) replication and accumulation (Prusiner, 2003), synaptic degeneration (Ishikura et al, 2005), microglia and astrocyte activation (Rezaie and Lantos, 2001; Perry et al, 2002), and neuronal cell death (Liberski et al, 2004). There exist distinct strains of infectious prions that have different properties (e.g. duration of incubation time, sites of infection in the brain, and so on) that presumably arise from distinct structural forms of misfolded prion proteins (e.g. distinct three-dimensional structures and/or chemical modifications). Changes in gene expression induced by prion infection were followed at multiple time points in diverse mouse–prion combinations over the entire course of the incubation times with end points ranging from 56 to 392 days (Figure 1 and Table I: incubation time is defined as the time period from prion infection to an end point, where all mice in each combination showed end-stage clinical signs of prion disease (except for Prnp-null (0/0) mice)). Three factors responsible for this wide range in incubation times are represented in our mouse strain–prion strain combinations: (1) distinct prion strains can produce different incubation times in genetically identical hosts, (2) host genotype determines incubation times for a prion strain with the allelic forms of the PrP gene (Prnp) having the strongest effect, and (3) concentrations of PrP affect the incubation times. Infected animals exhibit no signs of illness over most of their incubation periods. The dynamically changing DEGs were mapped onto a series of known protein interaction maps and integrated with histopathological data on PrPSc accumulation and distribution in the brain at multiple time points during disease incubation. This integration was used to develop hypothetical molecular subnetworks presumably encoding various cellular processes that are perturbed by prion infection and these hypotheses were validated by statistical and biological assessments of the DEGs central to prion disease. The networks identify perturbations of cellular processes that appear essential for prion replication and also the interactions of such processes with subnetworks involved in pathological changes. With the exception of the prion replication and accumulation network, similar changing network processes are seen in other neurodegenerative disorders, including Alzheimer's disease (Bossy-Wetzel et al, 2004; Spires and Hannan, 2007). Figure 1.Strategies for identification of 333 core differentially expressed genes (DEGs) and their functional analysis in mouse prion diseases. Two prion strains (RML and 301V) were used for inoculating mice from six different genetic backgrounds (B6, B6.I, FVB, Tg4053, 0/+, and 0/0) to generate eight prion–mouse combinations. From the list of 7400 DEGs identified from at least one of the five combinations with normal levels of prion protein (1X), 333 DEGs shared by all five were selected through novel statistical methods to represent perturbed networks essential to prion pathophysiology. Venn diagram shows the overlap of the 333 DEGs with DEGs from Tg4053-RML (mice expressing eight times of normal prion protein levels) and from 0/+-RML (mice expressing one-half of normal prion protein levels). Among 333 DEGs, 161 genes were mapped to networks through protein–protein interaction network or metabolic pathways. Also, by comparison of 333 DEGs with previous prion microarray studies, we identified 178 DEGs that have not been reported in connection with prion disease. Download figure Download PowerPoint Table 1. Mouse strain–prion strain combinations Mouse strain Prnp genotype Prion strain End pointa (days/weeks) Harvest intervalb (weeks) C57BL/6J (B6) a/a RML 161/23 2 C57BL/6J (B6) a/a 301V 287/41 4 C57BL/6.I-1 (B6.I) b/b RML 336/48 4 C57BL/6.I-1 (B6.I) b/b 301V 126/18 2 FVB/NCr (FVB) a/a RML 154/22 2 FVB-Tg(PrP-A)4053 (Tg4053) >30a RML 56/8 1 (FVB x FVB.129-Prnptm1Zrch)F1 (0/+) a/0 RML 392/56 4 FVB.129-Prnptm1Zrch (0/0) 0/0 RML 196/28c 4 a End point is equivalent to the incubation time in this study, which is defined as the interval between prion inoculation to end point. With the exception of 0/0 mice, all animals were at the terminal stages of disease when killed for brain harvest. For each combination, a set of mice was inoculated with brain homogenate from normal mice and brains were harvested at the same time points as prion-infected mice. b Brains from three mice were taken at each time point. In a few instances, the interval deviated from that shown. For example, the final interval for B6-RML was 1 week due to severity of illness in these mice. The actual intervals are indicated in figures that follow. c Additional three RML-inoculated and three normal brain homogenate-inoculated 0/0 mice were aged 357 days (51 weeks). It is important to stress that dynamic biological networks may change in two ways—the transcripts of the nodal points in the network may change in concentration or chemical structure; the architecture of the network may change in the nature of its connections or interactions. One of the grand challenges of systems biology is to develop methods that may easily measure the dynamically changing architecture of networks—and currently we are unfortunately forced to map or correlate the dynamical changes in transcripts, histopathology and clinical signs onto networks, the dynamics of which are revealed only by changes in the concentrations of mRNAs. Even with this coarse granularity of understanding, powerful insights into the nature of prion disease emerge from the correlation of histopathology, clinical signs and dynamical networks—explaining much of what we already know about this disease mechanistically in terms of biological network behavior as well as beginning to providing insights into new pathogenic mechanisms and potential strategies for therapeutic intervention. Results Microarray analysis of gene expression To facilitate the identification of differential gene expression relevant to essential processes in prion replication and disease, we selected the mouse strain–prion strain combinations shown in Table I. The combinations reflect three different factors that can dramatically impact the incubation time: prion strain, host genotype, and PrP concentration. Two prion strains, Rocky Mountain Lab (RML) mouse-adapted scrapie prions and 301V mouse-adapted BSE prions, produce dramatically different incubation times in a single inbred strain of mice. For example, C57BL/6J (B6) mice inoculated with RML (B6-RML) have short incubation times compared with B6 mice inoculated with 301V prions (B6-301V) (see Table I). Host genotype also affects incubation time (Westaway et al, 1987). Indeed, alternative alleles of the PrP gene, Prnp, encode distinct proteins that dramatically alter incubation time. B6 and B6.I-Prnpb (B6.I) mice are congenic for the interval on Chr 2 that contains Prnp (Carlson et al, 1993, 1994). B6 mice inoculated with RML prions (B6-RML) have short incubation times, whereas B6.I (Prnpb) mice inoculated with the same prion strain (B6.I-RML) have long RML incubation times owing to their different PrP sequences (Westaway et al, 1987). The converse is true for 301V prions (B6-301V and B6.I-301V) with B6.I mice having shorter incubation times than B6 mice (Bruce et al, 1994; Carlson et al, 1994). Thus, it is important to stress that long incubation time is not an inherent property of either Prnp genotype or prion strain, but reflects interactions between the host and the agent. PrP concentration also affects incubation time. The FVB/NCr (FVB) genetic background was used for testing effects of the level of PrP expression on gene expression in prion disease. Mice that are heterozygous for a null allele of Prnp, (FVB.129-Prnptm1Zrch × FVB)F1 (0/+), express half the amount of PrP as FVB, and have very long incubation times in spite of accumulating high levels of PrPSc in a long preclinical stage (Bueler et al, 1994; Manson et al, 1994). On the other hand, FVB-Tg(PrP-A)4053 mice (Tg4053) that overexpress PrP transgenes have very short incubation times (Carlson et al, 1994). FVB, 0/+, and Tg4053 mice were inoculated with RML prions (FVB-RML, 0/+-RML, and Tg4053-RML). FVB.129-Prnptm1Zrch mice (0/0) that lack PrP entirely, cannot be infected with prions (Bueler et al, 1993) and do not develop disease, were also inoculated with RML prions to eliminate DEGs induced by prions that are not relevant to prion disease. Only one statistically significant DEG (Itgam) was found in 0/0 mice—reassuring us that the statistics and subtractive analyses of dynamically changing transcriptomes guided by prion biology worked effectively. Thus, the eight mouse strain–prion strain combinations can be assigned to different groups to emphasize different aspects of disease and the DEGs generated by these groups can be used in subtractive comparisons to analyze discrete aspects of prion disease. Grouping B6-RML, B6-301V, B6.I-RML, B6.I-301V, and FVB-RML will emphasize DEGs induced by both prion strains and across incubation times; such shared DEGs are likely to reflect functionally important processes. The combinations also can be grouped according to prion strain for the identification of DEGs enriched in response to one strain versus the other (comparing B6-RML, B6.I-RML, and FVB-RML with B6-301V and B6.I-301V). Short incubation time combinations reflecting prion strain–PrP genotype interactions (B6-RML, B6.I-301V, and FVB-RML) also can be compared with long incubation time combinations (B6-301V and B6.I-RML). The genetically modified combinations (0/+-RML and Tg4053-RML) were used as biological filters whose incubation time differences reflected PrP concentration rather than PrP genotype. Comparison of B6 and FVB mice inoculated with RML (B6-RML and FVB-RML) that do not have a large difference in prion incubation times served to filter out DEGs that presumably reflect mouse strain genetic polymorphisms (see Supplementary Figure S1 showing Ccl12, Usp18, and Bst2 as examples of genes, the expression of which increased only in B6 mice). Brains from three mice per group were harvested every 1, 2, or 4 weeks depending on the length of the incubation times as indicated in Table I (the idea was to have 8–10 sample comparisons across the incubation period). Comprehensive time course transcriptomic data sets were generated from each of the eight mouse strain–prion strain combinations using Affymetrix mouse array 430 2.0 chips (450 arrays, see Materials and methods and Supplementary information). The noise in this enormous amount of data includes both (1) biological noise due to environment-induced or stochastic variation among replicate, genetically identical individual mice that could obscure meaningful, but small, differences coming from different prion strains, alternative alleles of Prnp, differences in PrP concentration, and the resulting differences in incubation time and dynamics of pathological changes and (2) technological noise from variation in prion inoculation precision into the thalamus, sample preparation for array analysis, the array measurements themselves, and other factors. The majority of transcripts do not change their expression patterns as a result of prion disease, though some could be associated with prion disease despite no expression change—Prnp is the best-known example. The transcripts that are differentially expressed can also show different temporal patterns dependent on the mouse strain–prion strain combinations. The challenge here is to reliably detect initially small changes superimposed on a large more constant background. We developed an effective data analysis framework that can extract core prion-related signatures from such noise-corrupted data by resolving the noise-related problems. To cancel out responses caused by intracerebral inoculation and aging of the mice, we performed microarray analysis of age- and genotype-matched control mice inoculated with brain homogenates from normal mice at each time point; differential mRNA expression at each time point in each mouse–prion combination reflected subtraction of expression from the corresponding control mice. This minimized both biological and technological noises. We developed a statistical method that effectively identified a core gene set, the expression levels of which were changed similarly across multiple mouse–prion combinations. The identification of this core gene set reduced the whole genome-scale data down to a smaller data set with potential prion disease association. Our method to identify shared expression patterns by integrating multiple mouse strain–prion strain data allowed the detection of differential signals that were marginal in some individual combinations but significant when considering all combinations together. Although analysis of DEGs using mRNA prepared from whole brain dilutes expression signals from individual brain regions, this integration-based gene selection compensates by its biological focus on changes that are relevant to the progression of pathology and prion replication. That is, this method can reliably select as DEGs the genes with marginal P-values (close to the cutoff) due to small expression changes (e.g. fold change=1.5) over time in the individual mouse–prion combinations by producing significant overall P-values (<10−3) when the individual marginal P-values in multiple mouse–prion combinations were combined (details of the method described below). Using this method, we found that the overall P-values for such transcripts tend to be smaller than those of the transcripts that showed inconsistent temporal expression patterns (e.g. changed in some combinations and not changed in the others or changed in opposite directions) across the multiple mouse–prion combinations. Shared DEGs in prion disease To identify genes involved in essential processes related to prion pathogenesis, we first focused on the f
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