Back

In vivo 18F-DOPA PET imaging identifies a dopaminergic deficit in a rat model with a G51D α-synuclein mutation

Morley, V.; Singh-Dolt, K.; Alcaide-Corral, C. J.; Walton, T.; Lucatelli, C.; Mashimo, T.; Tavares, A.; Kunath, T.

2022-11-05 neuroscience
10.1101/2022.11.05.515268 bioRxiv
Show abstract

Parkinsons disease (PD) is a neurodegenerative condition with several major hallmarks, including loss of substantia nigra neurons, reduction in striatal dopaminergic function, and formation of -synuclein-rich Lewy bodies. Mutations in SNCA, encoding for -synuclein, are a known cause of familial PD, and the G51D mutation causes a particularly aggressive form of the condition. CRISPR/Cas9 technology was used to introduce the G51D mutation into the endogenous rat SNCA gene. SNCAG51D/+ and SNCAG51D/G51D rats were born in Mendelian ratios and did not exhibit any severe behavourial defects. L-3,4-dihydroxy-6-18F-fluorophenylalanine (18F-DOPA) positron emission tomography (PET) imaging was used to investigate this novel rat model. Wild-type (WT), SNCAG51D/+ and SNCAG51D/G51D rats were characterised over the course of ageing (5, 11, and 16 months old) using 18F-DOPA PET imaging and kinetic modelling. We measured the influx rate constant (Ki) and effective distribution volume ratio (EDVR) of 18F-DOPA in the striatum relative to the cerebellum in WT, SNCAG51D/+ and SNCAG51D/G51D rats. A significant reduction in EDVR was observed in SNCAG51D/G51D rats at 16 months of age indicative of increased dopamine turnover. Furthermore, we observed a significant asymmetry in EDVR between the left and right striatum in aged SNCAG51D/G51D rats. The increased and asymmetric dopamine turnover observed in the striatum of aged SNCAG51D/G51D rats is similar to prodromal PD, which suggests the presence of compensatory mechanisms. SNCAG51D rats represent a novel genetic model of PD, and kinetic modelling of 18F-DOPA PET data has identified a highly relevant early disease phenotype.

Matching journals

The top 6 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.