Genetically encoded self-assembling synuclein for tunable Parkinsonian pathologyin vitroandin vivo

Abstract

Many neurodegenerative diseases feature abnormal protein aggregation in neurons. A notable example is α-synuclein (αSyn) aggregates in Parkinson’s disease (PD). The mechanisms underlying aggregates formation and their contribution to the pathogenesis remain unclear. Here we tackle this question from a build-to-understand perspective by recreating αSyn aggregates that recapitulate PD pathologies. We developed a novel system, named self-assembling α-synuclein (SAS), which enables αSyn to form different types of aggregates intracellularly. By screening various SAS constructs, we identified those that induce key PD pathologies, including Ser129 phosphorylation of αSyn (pS129-αSyn) and neurite retraction. As a genetically-encoded system, SAS can be seamlessly applied to a wide range of in vitro and in vivo models. In vitro , SAS led to strong pS129-αSyn, secondary aggregation of endogenous αSyn, mitochondrial deficits and neuronal degeneration in primary neurons, hESC-derived neurons and organoids. To further validate if SAS-induced aggregates recapitulate PD pathologies in vivo , we systemically delivered SAS into mouse brain using the adeno-associated virus PHP.eB. SAS-injected mice exhibited impaired motor function, dopaminergic neuron loss and reduced striatal projections. Furthermore, dopaminergic neuron-targeted SAS recapitulated motor deficits and revealed that induced αSyn aggregates can propagate from substantia nigra to other brain regions. This study establishes SAS as a robust and widely applicable system for temporally-controllable, tunable, and cell-type-specific PD pathology. By closely mirroring human PD pathology in terms of histology hallmarks, transcriptional profiles, and behavioral outcomes, SAS provides a powerful tool to investigate disease mechanisms and therapeutic interventions, with potential applicability to other neurodegenerative diseases.

Authors

Yujie Fan, Jonathan Hoang, Chelsie Steele, Yochana Benchetrit, Sayan Dutta, Gerard Coughlin, Nathan Appling, Xiaozhe Ding, Changfan Lin, Zhe Qu, Viviana Gradinaru

Caltech