Back

Differential susceptibility to reduced dopamine in SNc versus VTA in a mouse Tyrosine hydroxylase KO model of Parkinson’s Disease

Koltun, M.; Cichewicz, K.; Gibbs, J. T.; Darvas, M.; Hirsh, J.

2019-07-01 neuroscience
10.1101/468330 bioRxiv
Show abstract

Parkinsons Disease (PD), is a neurodegenerative disorder affecting both cognitive and motor functions. It is characterized by decreased brain dopamine (DA) and a selective and progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc), whereas dopaminergic neurons in the ventral tegmental area (VTA) show reduced vulnerability. The majority of animal models of PD are genetic lesion or neurotoxin exposure models that lead to death of dopaminergic neurons. Here we use a DAT:TH KO mouse model that by inactivation of the tyrosine hydroxylase (Th) gene in dopamine transporter-expressing neurons, causes selective depletion of striatal dopamine without affecting DA neuron survival. We analyzed transcriptome responses to decreased DA in both pre- and post-synaptic dopaminergic brain regions of DAT:TH KO animals. We detected only few differentially expressed genes in the post-synaptic regions as a function of DA deficiency. This suggests that the broad striatal transcriptional changes in neurodegeneration-based PD models are not direct effects of DA depletion, but are rather a result of DA neuronal death. However, we find a number of dopaminergic genes differentially expressed in SNc, and to a lesser extent in VTA, as a function of DA deficiency, providing evidence for a DA-dependent feedback loop. Of particular interest, expression of Nr4a2, a crucial transcription factor maintaining DA neuron identity, is significantly decreased in SNc, but not VTA, of DAT:TH KO mice, implying a potential protective role for DA in the SNc.

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.