Cells maintain healthy mitochondria by degrading damaged mitochondria through mitophagy; defective mitophagy is linked to Parkinson's disease. Here we report that USP30, a deubiquitinase localized to mitochondria, antagonizes mitophagy driven by the ubiquitin ligase parkin (also known as PARK2) and protein kinase PINK1, which are encoded by two genes associated with Parkinson's disease. Parkin ubiquitinates and tags damaged mitochondria for clearance. Overexpression of USP30 removes ubiquitin attached by parkin onto damaged mitochondria and blocks parkin's ability to drive mitophagy, whereas reducing USP30 activity enhances mitochondrial degradation in neurons. Global ubiquitination site profiling identified multiple mitochondrial substrates oppositely regulated by parkin and USP30. Knockdown of USP30 rescues the defective mitophagy caused by pathogenic mutations in parkin and improves mitochondrial integrity in parkin- or PINK1-deficient flies. Knockdown of USP30 in dopaminergic neurons protects flies against paraquat toxicity in vivo, ameliorating defects in dopamine levels, motor function and organismal survival. Thus USP30 inhibition is potentially beneficial for Parkinson's disease by promoting mitochondrial clearance and quality control.

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the most common cause of familial Parkinson's disease (PD). Although biochemical studies have shown that certain PD mutations confer elevated kinase activity in vitro on LRRK2, there are no methods available to directly monitor LRRK2 kinase activity in vivo. We demonstrate that LRRK2 autophosphorylation on Ser(1292) occurs in vivo and is enhanced by several familial PD mutations including N1437H, R1441G/C, G2019S, and I2020T. Combining two PD mutations together further increases Ser(1292) autophosphorylation. Mutation of Ser(1292) to alanine (S1292A) ameliorates the effects of LRRK2 PD mutations on neurite outgrowth in cultured rat embryonic primary neurons. Using cell-based and pharmacodynamic assays with phosphorylated Ser(1292) as the readout, we developed a brain-penetrating LRRK2 kinase inhibitor that blocks Ser(1292) autophosphorylation in vivo and attenuates the cellular consequences of LRRK2 PD mutations in vitro. These data suggest that Ser(1292) autophosphorylation may be a useful indicator of LRRK2 kinase activity in vivo and may contribute to the cellular effects of certain PD mutations.

Geometric-phase metasurfaces, recently utilized for controlling wavefronts of circular polarized (CP) electromagnetic waves, are drastically limited to the cross-polarization modality. Combining geometric with propagation phase allows to further control the co-polarized output channel, nevertheless addressing only similar functionality on both co-polarized outputs for the two different CP incident beams. Here we introduce the concept of chirality-assisted phase as a degree of freedom, which could decouple the two co-polarized outputs, and thus be an alternative solution for designing arbitrary modulated-phase metasurfaces with distinct wavefront manipulation in all four CP output channels. Two metasurfaces are demonstrated with four arbitrary refraction wavefronts, and orbital angular momentum modes with four independent topological charge, showcasing complete and independent manipulation of all possible CP channels in transmission. This additional phase addressing mechanism will lead to new components, ranging from broadband achromatic devices to the multiplexing of wavefronts for application in reconfigurable-beam antenna and wireless communication systems.

A classification and regression tool, J. H. Friedman's Stochastic Gradient Boosting (SGB), is applied to predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Stochastic Gradient Boosting is a procedure for building a sequence of models, for instance regression trees (as in this paper), whose outputs are combined to form a predicted quantity, either an estimate of the biological activity, or a class label to which a molecule belongs. In particular, the SGB procedure builds a model in a stage-wise manner by fitting each tree to the gradient of a loss function: e.g., squared error for regression and binomial log-likelihood for classification. The values of the gradient are computed for each sample in the training set, but only a random sample of these gradients is used at each stage. (Friedman showed that the well-known boosting algorithm, AdaBoost of Freund and Schapire, could be considered as a particular case of SGB.) The SGB method is used to analyze 10 cheminformatics data sets, most of which are publicly available. The results show that SGB's performance is comparable to that of Random Forest, another ensemble learning method, and are generally competitive with or superior to those of other QSAR methods. The use of SGB's variable importance with partial dependence plots for model interpretation is also illustrated.