Key Connectomes and Synaptic‐Compartment‐Specific Risk Genes Drive Pathological α‐Synuclein Spreading
Abstract Previous studies have suggested that pathological α‐synuclein (α‐Syn) mainly transmits along the neuronal network, but several key questions remain unanswered: 1) How many and which connections in the connectome are necessary for predicting the progression of pathological α‐Syn? 2) How to i...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-07-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202413052 |
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| Summary: | Abstract Previous studies have suggested that pathological α‐synuclein (α‐Syn) mainly transmits along the neuronal network, but several key questions remain unanswered: 1) How many and which connections in the connectome are necessary for predicting the progression of pathological α‐Syn? 2) How to identify risk genes that affect pathology spreading functioning at presynaptic or postsynaptic regions, and are these genes enriched in different cell types? Here, these questions are addressed with novel mathematical models. Strikingly, the spreading of pathological α‐Syn is predominantly determined by the key subnetworks composed of only 2% of the strongest connections in the connectome. Genes associated with the selective vulnerability of brain regions to pathological α‐Syn transmission are further analyzed to distinguish those functioning at presynaptic versus postsynaptic regions. Those risk genes are significantly enriched in microglial cells of presynaptic regions and neurons of postsynaptic regions. Gene regulatory network analyses are then conducted to identify “key drivers” of genes responsible for selective vulnerability and overlapping with Parkinson's disease risk genes. By identifying and discriminating between key gene mediators of transmission operating at presynaptic and postsynaptic regions, this study has demonstrated for the first time that these are functionally distinct processes. |
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| ISSN: | 2198-3844 |