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Background and challenges

In the prion paradigm framework, host-encoded monomeric proteins are converted into misfolded aggregated assemblies, which serve as a template for further autocatalytic recruitment and conversion in the brain. Since the late 2000s, the prion paradigm has been extended to other neurodegenerative diseases due to protein misfolding such as Alzheimer's and Parkinson's disease.

In mammalian prion diseases, also known as Transmissible Spongiform Encephalopathies (TSE), prion assemblies (PrP^Sc), formed from the cellular prion protein (PrP^C), contain all the structural information necessary to their replication and their specific stereotyped disease phenotype in the infected host. In TSE, multiple PrP^Sc conformational variants exist. They define the prion strains and dictate specific physiopathological patterns such as region-specific PrP^Sc deposits in the same host species. Although self-replicative processes provide a mechanistic framework for the prion paradigm, to date there is no mechanistic link between prion replication, the neuroinvasion process and the strain-specific neuropathological pattern

Goals

The PrionDif project seeks to develop a multi-scale mechanistic model accounting for the spatiotemporal dynamic of prion spreading within the brain by integrating experimental observations with an effective model of prion replication which takes into account the dynamicity of PrP^Sc assemblies. By integrating the spatio-temporal mapping of the spread of prion replicative centres with the prion replication/dissemination model, we aim to build a synthetic multi-scale model of prion structural diversification and lesional propagation. This open-access model will allow us to investigate which parameters of the prion replication process specific to each strain dictate the progression of the disease and the apparition of strain specific PrP^Sc deposition patterns.

Ultimately, this synthetic approach will allow the identification of key processes to enable therapeutic advances and promote early diagnosis

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Partnerships

INRAE participants

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