A Glio-Protective Role of mir-263a by Tuning Sensitivity to Glutamate

Glutamate is a ubiquitous neurotransmitter, mediating information flow between neurons. Defects in the regulation of glutamatergic transmission can result in glutamate toxicity, which is associated with neurodegeneration. Interestingly, glutamate receptors are expressed in glia, but little is known about their function, and the effects of their misregulation, in these non-neuronal cells. Here, we report a glio-protective role for Drosophila mir-263a mediated by its regulation of glutamate receptor levels in glia. mir-263a mutants exhibit a pronounced movement defect due to aberrant overexpression of CG5621/Grik, Nmdar1, and Nmdar2. mir-263a mutants exhibit excitotoxic death of a subset of astrocyte-like and ensheathing glia in the CNS. Glial-specific normalization of glutamate receptor levels restores cell numbers and suppresses the movement defect. Therefore, microRNA-mediated regulation of glutamate receptor levels protects glia from excitotoxicity, ensuring CNS health. Chronic low-level glutamate receptor overexpression due to mutations affecting microRNA (miRNA) regulation might contribute to glial dysfunction and CNS impairment

Fully automated leg tracking of Drosophila neurodegeneration models reveals distinct conserved movement signatures.

Some neurodegenerative diseases, like Parkinsons Disease (PD) and Spinocerebellar ataxia 3 (SCA3), are associated with distinct, altered gait and tremor movements that are reflective of the underlying disease etiology. Drosophila melanogaster models of neurodegeneration have illuminated our understanding of the molecular mechanisms of disease. However, it is unknown whether specific gait and tremor dysfunctions also occur in fly disease mutants. To answer this question, we developed a machine-learning image-analysis program, Feature Learning-based LImb segmentation and Tracking (FLLIT), that automatically tracks leg claw positions of freely moving flies recorded on high-speed video, producing a series of gait measurements. Notably, unlike other machine-learning methods, FLLIT generates its own training sets and does not require user-annotated images for learning. Using FLLIT, we carried out high-throughput and high-resolution analysis of gait and tremor features in Drosophila neurodegeneration mutants for the first time. We found that fly models of PD and SCA3 exhibited markedly different walking gait and tremor signatures, which recapitulated characteristics of the respective human diseases. Selective expression of mutant SCA3 in dopaminergic neurons led to a gait signature that more closely resembled those of PD flies. This suggests that the behavioral phenotype depends on the neurons affected rather than the specific nature of the mutation. Different mutations produced tremors in distinct leg pairs, indicating that different motor circuits were affected. Using this approach, fly models can be used to dissect the neurogenetic mechanisms that underlie movement disorders