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Mutschall, S.

Publications and source records attributed to Mutschall, S..

2 recordsLinked to original sources

Structure and function of otoferlin, a synaptic protein of sensory hair cells essential for hearing

Our sense of hearing relies upon speedy synaptic transmission of sound information from cochlear inner hair cells (IHCs) to spiral ganglion neurons (SGNs). To accomplish this, IHCs employ a sophisticated presynaptic machinery including the multi-C2-domain protein otoferlin which is affected by human deafness mutations. Otoferlin is essential for IHC-exocytosis but how it binds Ca2+ and the target membrane to serve synaptic vesicle (SV) tethering, docking and fusion remained unclear. Here, we obtained cryo-electron-microscopy structures of Ca2+-bound otoferlin and employed molecular dynamics simulations of membrane binding. We show that membrane binding involves C2B-C2G-domains and repositions C2F- and C2G-domains. Progressive disruption of Ca2+-binding by the C2D-domain in mice increasingly altered synaptic sound encoding and eliminated the Ca2+-cooperativity of SV-exocytosis, indicating that this Ca2+-cooperativity reflects binding of several Ca2+-ions to otoferlin. Together, our findings elucidate molecular mechanisms underlying otoferlin-mediated SV-docking and support a role of otoferlin as Ca2+-sensor of SV-fusion in IHCs.

neuroscience↗

SynapseNet: Deep Learning for Automatic Synapse Reconstruction

Electron microscopy is an important technique for the study of synaptic morphology and its relation to synaptic function. The data analysis for this task requires the segmentation of the relevant synaptic structures, such as synaptic vesicles, active zones, mitochondria, presynaptic densities, synaptic ribbons, and synaptic compartments. Previous studies were predominantly based on manual segmentation, which is very time-consuming and prevented the systematic analysis of large datasets. Here, we introduce SynapseNet, a tool for the automatic segmentation and analysis of synapses in electron micrographs. It can reliably segment synaptic vesicles and other synaptic structures in a wide range of electron microscopy approaches, thanks to a large annotated dataset, which we assembled, and domain adaptation functionality we developed. We demonstrated its capability for (semi-)automatic biological analysis in two applications and made it available as an easy-to-use tool to enable novel data-driven insights into synapse organization and function.

bioinformatics↗