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. 2012 Oct 15:6:132.
doi: 10.1186/1752-0509-6-132.

Gene regulatory network analysis supports inflammation as a key neurodegeneration process in prion disease

Isaac Crespo  1 , Kirsten Roomp, Wiktor Jurkowski, Hiroaki Kitano, Antonio del Sol
Affiliations

Gene regulatory network analysis supports inflammation as a key neurodegeneration process in prion disease

Isaac Crespo et al. BMC Syst Biol. .

Abstract

Background: The activation of immune cells in the brain is believed to be one of the earliest events in prion disease development, where misfolded PrionSc protein deposits are thought to act as irritants leading to a series of events that culminate in neuronal cell dysfunction and death. The role of these events in prion disease though is still a matter of debate. To elucidate the mechanisms leading from abnormal protein deposition to neuronal injury, we have performed a detailed network analysis of genes differentially expressed in several mouse prion models.

Results: We found a master regulatory core of genes related to immune response controlling other genes involved in prion protein replication and accumulation, and neuronal cell death. This regulatory core determines the existence of two stable states that are consistent with the transcriptome analysis comparing prion infected versus uninfected mouse brain. An in silico perturbation analysis demonstrates that core genes are individually capable of triggering the transition and that the network remains locked once the diseased state is reached.

Conclusions: We hypothesize that this locking may be the cause of the sustained immune response observed in prion disease. Our analysis supports the hypothesis that sustained brain inflammation is the main pathogenic process leading to neuronal dysfunction and loss, which, in turn, leads to clinical symptoms in prion disease.

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Figures

Figure 1
Figure 1
Fragmentation analysis of the global network. The original global unfragmented network (a), the impact on the network connectivity due to the removal of the sixteen genes belonging to the SCC (b), and an example of the removal of sixteen genes randomly selected (c). In (b) most of the genes become disconnected and the size of the giant component or the biggest connected graph is only 38 genes. In (c) when removing 16 randomly selected genes, the mean of the giant component was 81.02 nodes (standard deviation of 8.29) for 1000 removals . This figure illustrates the relevant role of the SCC as a connectivity element of the network.
Figure 2
Figure 2
Perturbation analysis of a gene in the SCC Perturbation of the TLR2 gene (black diamond), and its effect on the other genes of the SCC. Y-axis: 0 indicates the “off” state, 1 indicates the “on” state. TLR2 is capable of triggering the transition from the “off” (healthy) to the “on” (disease) stable state for all genes in the SCC. The simulations were performed assuming a continuous dynamical system where the initial states are the attractors previously computed in a discrete model (Boolean). The Y-axis represents the “level of activity” in a range between 0 and 1, and X-axis represents “time” in arbitrary units.
Figure 3
Figure 3
Functional analysis of core network with pathological features Genes associated with PrPScreplication and accumulation are in green, with nerve cell death in blue, with immune response (including, microglia/astrocyte activation, leukocyte extravasation, general immune response) in pink. Other genes are indicated in grey. SCC genes are indicated as octagons.
See this image and copyright information in PMC

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