In vivo evidence is provided supporting a model in which DJ-1 deficiency can promote key PD-like phenotypes in tree shrews, potentially involving PSer129αSyn accumulation, nigral dopaminergic neuron loss, and motor dysfunction.
Abstract
DJ-1 is a genetic factor associated with Parkinson’s disease (PD), and altered DJ-1 function has been implicated in PD pathogenesis. However, DJ-1 knockout mice fail to reproduce robust PD-like phenotypes or neuropathology, leaving the contribution of DJ-1 deficiency to PD-related disease processes unresolved. To investigate this question in a species more closely related to primates than rodents, we used the tree shrew and performed bilateral, neuron-targeted DJ-1 knockout in the substantia nigra using an AAV-mediated in situ gene editing system. Four out of five DJ-1 knockout tree shrews developed key PD features, including motor abnormalities, substantial loss of nigral dopaminergic neurons, and aggregation of phosphorylated α-synuclein at serine 129 (PSer129αSyn). Notably, one DJ-1 knockout animal did not develop these phenotypes, despite comparable AAV transduction efficiency and DJ-1 knockout levels in the substantia nigra, suggesting that DJ-1 loss may not be solely sufficient in all cases and that additional modifying or compensatory factors may influence disease manifestation. Further pathological analysis showed that PSer129αSyn aggregation was associated with the development of PD-like phenotypes in this model. These findings support a model in which DJ-1 deficiency can promote key PD-like phenotypes in tree shrews, potentially involving PSer129αSyn accumulation, nigral dopaminergic neuron loss, and motor dysfunction. Given that multiple molecular pathways have been proposed to link DJ-1 dysfunction to PD, our results provide in vivo evidence supporting PSer129αSyn aggregation as an important pathological process associated with DJ-1 deficiency. This tree shrew model may therefore provide a useful experimental platform for studying DJ-1-related PD pathogenesis and evaluating potential therapeutic strategies.
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Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.