Back

Lack of functional STING modulates immunity but does not protect dopaminergic neurons in the alpha-synuclein preformed fibrils Parkinson's Disease mouse model

Klaestrup, I. H.; Reinert, L. S.; Ferreira, S. A.; Lauritsen, J.; Toft, G. U.; Gram, H.; Jensen, P. H.; Paludan, S. R.; Romero-Ramos, M.

2025-06-02 neuroscience
10.1101/2025.05.30.656977 bioRxiv
Show abstract

Microglia response is proposed to be relevant in the neurogenerative process associated with alpha-synuclein (-syn) pathology in Parkinsons disease (PD). STING is a protein related to the immune sensing of DNA and autophagy, and it has been proposed to be involved in PD neurodegeneration. To investigate this, we injected 10 {micro}g of murine pre-formed fibrils (PFFs) of -syn (or monomeric and PBS as controls) into the striatum of wild-type (WT) and STINGgt/gt mice, which lack functional STING. We examined motor behavior and brain pathology at 1- and 6-months post-injection. STINGgt/gt mice showed more motor changes associated with PFF injection than WT mice. STINGgt/gt mice had a differential immune response to PFF with early and sustained increased microglia numbers and higher macrophagic CD68 response, but milder changes in the expression of immune-relevant markers such as TLR2, TLR4, IL1b, and TREM2. However, the lack of STING did not induce changes in the extent of -syn pathology nor the p62 accumulation seen in the model. Altogether, this resulted in a faster but similar degree of nigrostriatal dopaminergic degeneration after 6 months. Therefore, the data do not support a necessary role for STING in the -syn induced nigral neuronal loss in the PFF-PD mouse model used here. However, the results suggest a functional relevance for STING in the brain response to the excess and aggregation of amylogenic proteins such as -syn that can contribute to symptomatic changes.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.