Degeneration of nigrostriatal dopaminergic system is the principal lesion in Parkinson’s disease. Because glial cell line-derived neurotrophic factor (GDNF) promotes survival of dopamine neurons in vitro and in vivo, intracranial delivery of GDNF has been attempted for Parkinson’s disease treatment but with variable success. For improving GDNF-based therapies, knowledge on physiological role of endogenous GDNF at the sites of its expression is important. However, due to limitations of existing genetic model systems, such knowledge is scarce. Here, we report that prevention of transcription of Gdnf 3’UTR in Gdnf endogenous locus yields GDNF hypermorphic mice with increased, but spatially unchanged GDNF expression, enabling analysis of postnatal GDNF function. We found that increased level of GDNF in the central nervous system increases the number of adult dopamine neurons in the substantia nigra pars compacta and the number of dopaminergic terminals in the dorsal striatum. At the functional level, GDNF levels increased striatal tissue dopamine levels and augmented striatal dopamine release and re-uptake. In a proteasome inhibitor lactacystin-induced model of Parkinson’s disease GDNF hypermorphic mice were protected from the reduction in striatal dopamine and failure of dopaminergic system function. Importantly, adverse phenotypic effects associated with spatially unregulated GDNF applications were not observed. Enhanced GDNF levels up-regulated striatal dopamine transporter activity by at least five fold resulting in enhanced susceptibility to 6-OHDA, a toxin transported into dopamine neurons by DAT. Further, we report how GDNF levels regulate kidney development and identify microRNAs miR-9, miR-96, miR-133, and miR-146a as negative regulators of GDNF expression via interaction with Gdnf 3’UTR in vitro. Our results reveal the role of GDNF in nigrostriatal dopamine system postnatal development and adult function, and highlight the importance of correct spatial expression of GDNF. Furthermore, our results suggest that 3’UTR targeting may constitute a useful tool in analyzing gene function.
Synapse degeneration is an early and invariant feature of neurodegenerative diseases. Indeed, synapse loss occurs prior to neuronal degeneration and correlates with the symptom severity of these diseases. However, the molecular mechanisms that trigger synaptic loss remain poorly understood. Here we demonstrate that deficient Wnt signalling elicits synaptic degeneration in the adult striatum. Inducible expression of the secreted Wnt antagonist Dickkopf1 (Dkk1) in adult mice (iDkk1) decreases the number of cortico-striatal glutamatergic synapses and of D1 and D2 dopamine receptor clusters. Synapse loss occurs in the absence of axon retraction or cell death. The remaining excitatory terminals contain fewer synaptic vesicles and have a reduced probability of evoked transmitter release. IDkk1 mice show impaired motor coordination and are irresponsive to amphetamine. These studies identify Wnts as key endogenous regulators of synaptic maintenance and suggest that dysfunction in Wnt signalling contributes to synaptic degeneration at early stages in neurodegenerative diseases.
Glial cell line-derived neurotrophic factor (GDNF) has been tested in clinical trials to treat Parkinson’s disease with promising but variable results. Improvement of therapeutic effectiveness requires solid understanding of the physiological role of GDNF in the maintenance of the adult brain catecholamine system. However, existing data on this issue is contradictory. Here we show with three complementary approaches that, independent of the time of reduction, Gdnf is not required for maintenance of catecholaminergic neurons in adult mice.
Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and time-consuming. The development of neural networks and deep learning has opened a new way to teach computers to count neurons. Implementation of a programming paradigm enables a computer to learn from the data and development of an automated cell counting method. The advantages of computerized counting are reproducibility, elimination of human error and fast high-capacity analysis. We implemented whole-slide digital imaging and deep convolutional neural networks (CNN) to count substantia nigra dopamine neurons. We compared the results of the developed method against independent manual counting by human observers and validated the CNN algorithm against previously published data in rats and mice, where tyrosine hydroxylase (TH)-immunoreactive neurons were counted using unbiased stereology. The developed CNN algorithm and fully cloud-embedded Aiforia™ platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.
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