Neurotransmission relies on the tight spatial and temporal regulation of the synaptic vesicle (SV) cycle. Nerve terminals contain hundreds of SVs that form tight clusters. These clusters represent a distinct liquid phase in which one component of the phase are SVs and the other synapsin 1, a highly abundant synaptic protein. Another major family of disordered proteins at the presynapse includes synucleins, most notably α-synuclein. The precise physiological role of α-synuclein in synaptic physiology remains elusive, albeit its role has been implicated in nearly all steps of the SV cycle. To determine the effect of α-synuclein on the synapsin phase, we employ the reconstitution approach using natively purified SVs from rat brains and the heterologous cell system to generate synapsin condensates. We demonstrate that synapsin condensates recruit α-synuclein, and while enriched into these synapsin condensates, α-synuclein still maintains its high mobility. The presence of SVs enhances the rate of synapsin/α-synuclein condensation, suggesting that SVs act as catalyzers for the formation of synapsin condensates. Notably, at physiological salt and protein concentrations, α-synuclein alone is not able to cluster isolated SVs. Excess of α-synuclein disrupts the kinetics of synapsin/SV condensate formation, indicating that the molar ratio between synapsin and α-synuclein is important in assembling the functional condensates of SVs. Understanding the molecular mechanism of α-synuclein interactions at the nerve terminals is crucial for clarifying the pathogenesis of synucleinopathies, where α-synuclein, synaptic proteins and lipid organelles all accumulate as insoluble intracellular inclusions.

Neurotransmission relies on the tight spatial and temporal regulation of the synaptic vesicle (SV) cycle. Nerve terminals contain hundreds of SVs that form tight clusters. We have shown that SVs form a distinct liquid phase in which one component of the phase are SVs and the other synapsin 1, a highly abundant synaptic protein. Another major family of disordered proteins at the presynapse includes synucleins, most notably alpha-synuclein. The precise physiological role of alpha-synuclein in synaptic physiology remains elusive, albeit its role was implicated in nearly all steps of the SV cycle. A recent analysis of neurons in culture showed that overexpression of alpha-synuclein attenuates neurotransmitter release but only in the presence of synapsin, suggesting a functional interaction between the two proteins. To explore the effect of alpha-synuclein/synapsin interaction, we employ the reconstitution approach using natively purified SVs from rat brains and the heterologous cell system to generate synapsin condensates. We demonstrate that synapsin condensates recruit alpha-synuclein. While enriched into synapsin condensates, alpha-synuclein still maintains its high mobility. Notably, at physiological concentrations and salt conditions, alpha-synuclein alone is not able to cluster isolated SVs. The presence of SVs enhances the rate of synapsin/alpha-synuclein condensation, suggesting that SVs act as catalyzers for the formation of synapsin condensates. interestingly, excess of alpha-synuclein disrupts the kinetics of synapsin/SV condensate formation, indicating that the molar ratio between synapsin and alpha-synuclein is important in assembling the functional condensates of SVs. Understanding the molecular mechanism of alpha-synuclein interactions at the nerve terminals is crucial for tackling synucleinopathies, where alpha-synuclein, synaptic proteins and lipid organelles accumulate as insoluble intracellular inclusions.

Identifying interesting relationships between pairs of genes, presented over some of experimental conditions in gene expression data set, is useful for discovering novel functional gene interactions. In this paper, we introduce a new method for id entifying L ocal C o-regulation R elationships (IdLCR). These local relationships describe the behaviors of pairwise genes, which are either up- or down-regulated throughout the identified condition subset. IdLCR firstly detects the pairwise gene-gene relationships taking functional forms and the condition subsets by using a regression spline model. Then it measures the relationships using a penalized Pearson correlation and ranks the responding gene pairs by their scores. By this way, those relationships without clearly biological interpretations can be filtered out and the local co-regulation relationships can be obtained. In the simulation data sets, ten different functional relationships are embedded. Applying IdLCR to these data sets, the results show its ability to identify functional relationships and the condition subsets. For micro-array and RNA-seq gene expression data, IdLCR can identify novel biological relationships which are different from those uncovered by IFGR and MINE.