Spread of α-synuclein pathology through the brain connectome is modulated by selective vulnerability and predicted by network analysis
Studies of patients afflicted by neurodegenerative diseases suggest that misfolded proteins spread through the brain along anatomically connected networks, prompting progressive decline. Recently, mouse models have recapitulated the cell-to-cell transmission of pathogenic proteins and neuron death o...
Gespeichert in:
Veröffentlicht in: | Nature neuroscience 2019-08, Vol.22 (8), p.1248-1257 |
---|---|
Hauptverfasser: | , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Studies of patients afflicted by neurodegenerative diseases suggest that misfolded proteins spread through the brain along anatomically connected networks, prompting progressive decline. Recently, mouse models have recapitulated the cell-to-cell transmission of pathogenic proteins and neuron death observed in patients. However, the factors regulating the spread of pathogenic proteins remain a matter of debate due to an incomplete understanding of how vulnerability functions in the context of spread. Here we use quantitative pathology mapping in the mouse brain, combined with network modeling to understand the spatiotemporal pattern of spread. Patterns of α-synuclein pathology are well described by a network model that is based on two factors: anatomical connectivity and endogenous α-synuclein expression. The map and model allow the assessment of selective vulnerability to α-synuclein pathology development and neuron death. Finally, we use quantitative pathology to understand how the G2019S
LRRK2
genetic risk factor affects the spread and toxicity of α-synuclein pathology.
Henderson et al. use quantitative pathology mapping and network modeling to show that α-synuclein pathology spreads through a neuroanatomically connected network, guided by selective vulnerability and genetic risk factors. |
---|---|
ISSN: | 1097-6256 1546-1726 |
DOI: | 10.1038/s41593-019-0457-5 |