A systems approach to prion disease

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Daehee Hwang^1,2,a, Inyoul Y Lee^1,a, Hyuntae Yoo^1,a, Nils Gehlenborg^1,3, Ji-Hoon Cho², Brianne Petritis¹, David Baxter¹, Rose Pitstick⁴, Rebecca Young⁴, Doug Spicer⁴, Nathan D Price⁷, John G Hohmann⁵, Stephen J DeArmond⁶, George A Carlson⁴ & Leroy E Hood¹

^aThese authors contributed equally to this work

Article highlights

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 (PrP^C) to disease-specific PrP^Sc 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 PrP^Sc 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 PrP^Sc 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 (PrP^Sc 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 PrP^Sc 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)

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.

Full figure and legend (175K)Figures & Tables index

Figure 4

Dynamic PrP replication and accumulation network. Hypothetical networks of proteins and metabolites that are potentially involved in PrP replication and accumulation were constructed starting from the list of 333 shared DEGs and protein–protein interaction/metabolic pathway information from public databases. Relative changes of the transcripts for the corresponding proteins are represented in color changes: red—upregulation, green—downregulation, yellow—no change. Data for the transcriptional changes are from BL6 mice infected with RML prions, at 6 weeks (A), 10 weeks (B), 14 weeks (C), 18 weeks (D), 20 weeks (E), and 22 weeks (F) after inoculation. See main text for detailed description of modules 1 through 6. Ch: cholesterol; ECM: extracellular matrix; PM: plasma membrane. Large nodes indicate DEGs whose expression change patterns are shared by five prion–mouse combinations (B6-RML, B6-301V, B6.I-RML, B6.I-301V, and FVB-RML); asterisks indicate DEGs whose prion-related changes are unique in this study; genes in blue are also DEGs in RML-infected 0/+ mice; genes in bold are also DEGs in RML-infected Tg4053 mice; genes with solid underline were changed only in mice with short incubation times; genes with dotted underline were changed only in RML-infected mice. Source data is available for this figure at www.nature.com/msb

Full figure and legend (6,061K)Source data for figure 4 (4K)Figures & Tables index

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 PrP^Sc with PrP^C encoded by alternative alleles of Prnp. PrP^C concentration can also affect incubation time, and differential gene expression was explored in FVB.129-Prnp^tm1Zrch/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.

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