α-Synuclein misfolding and aggregation plays a major role in the pathogenesis of Parkinson’s disease. Although loss of function mutations in the ubiquitin ligase, parkin, cause autosomal recessive Parkinson’s disease, there is evidence that parkin is inactivated in sporadic Parkinson’s disease. Whether parkin inactivation is a driver of neurodegeneration in sporadic Parkinson’s disease or a mere spectator is unknown. Here we show that parkin in inactivated through c-Abelson kinase phosphorylation of parkin in three α-synuclein-induced models of neurodegeneration. This results in the accumulation of parkin interacting substrate protein (zinc finger protein 746) and aminoacyl tRNA synthetase complex interacting multifunctional protein 2 with increased parkin interacting substrate protein levels playing a critical role in α-synuclein-induced neurodegeneration, since knockout of parkin interacting substrate protein attenuates the degenerative process. Thus, accumulation of parkin interacting substrate protein links parkin inactivation and α-synuclein in a common pathogenic neurodegenerative pathway relevant to both sporadic and familial forms Parkinson’s disease. Thus, suppression of parkin interacting substrate protein could be a potential therapeutic strategy to halt the progression of Parkinson’s disease and related α-synucleinopathies.
Maintenance of healthy mitochondria is crucial in cells, such as neurons, with high metabolic demands, and dysfunctional mitochondria are thought to be selectively degraded. Studies of chemically uncoupled cells have implicated PINK1 mitochondrial kinase, and Parkin E3 ubiquitin ligase in targeting depolarized mitochondria for degradation. However, the role of the PINK1/Parkin pathway in mitochondrial turnover is unclear in the nervous system under normal physiological conditions, and we understand little about the changes that occur in the mitochondrial life cycle when turnover is disrupted. Here, we evaluated the nature, location, and regulation of quality control in vivo using quantitative measurements of mitochondria in Drosophila nervous system, with deletion and overexpression of genes in the PINK1/Parkin pathway. We tested the hypotheses that impairment of mitochondrial quality control via suppression of PINK1 function should produce failures of turnover, accumulation of senescent mitochondria in the axon, defects in mitochondrial traffic, and a significant shift in the mitochondrial fission-fusion steady state. Although mitochondrial membrane potential was diminished by PINK1 deletion, we did not observe the predicted increases in mitochondrial density or length in axons. Loss of PINK1 also produced specific, directionally balanced defects in mitochondrial transport, without altering the balance between stationary and moving mitochondria. Somatic mitochondrial morphology was also compromised. These results strongly circumscribe the possible mechanisms of PINK1 action in the mitochondrial life cycle and also raise the possibility that mitochondrial turnover events that occur in cultured embryonic axons might be restricted to the cell body in vivo, in the intact nervous system.
Network Function Virtualization (NFV) is an emerging paradigm that turns hardware-dependent implementation of network functions (i.e., middleboxes) into software modules running on virtualized platforms, for significant cost reduction and ease of management. Such virtual network functions (VNFs) commonly constitute service chains, to provide network services that traffic flows need to go through. Efficient deployment of VNFs for network service provisioning is key to realize the NFV goals. Existing efforts on VNF placement mostly deal with offline or one-time placement, ignoring the fundamental, dynamic deployment and scaling need of VNFs to handle practical timevarying traffic volumes. This work investigates dynamic placement of VNF service chains across geo-distributed datacenters to serve flows between dispersed source and destination pairs, for operational cost minimization of the service chain provider over the entire system span. An efficient online algorithm is proposed, which consists of two main components: (1) A regularizationbased approach from online learning literature to convert the offline optimal deployment problem into a sequence of one-shot regularized problems, each to be efficiently solved in one time slot; (2) An online dependent rounding scheme to derive feasible integer solutions from the optimal fractional solutions of the one-shot problems, and to guarantee a good competitive ratio of the online algorithm over the entire time span. We verify our online algorithm with solid theoretical analysis and trace-driven simulations under realistic settings.
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