TY - JOUR A1 - Kaltdorf, Kristin Verena A1 - Theiss, Maria A1 - Markert, Sebastian Matthias A1 - Zhen, Mei A1 - Dandekar, Thomas A1 - Stigloher, Christian A1 - Kollmannsberger, Philipp T1 - Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning JF - PLoS ONE N2 - Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical “clear core” vesicles (CCV) and the typically larger “dense core” vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3]. To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms. KW - synaptic vesicles KW - Caenorhabditis elegans KW - machine learning Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-176831 VL - 13 IS - 10 ER - TY - JOUR A1 - Jarick, Marcel A1 - Bertsche, Ute A1 - Stahl, Mark A1 - Schultz, Daniel A1 - Methling, Karen A1 - Lalk, Michael A1 - Stigloher, Christian A1 - Steger, Mirco A1 - Schlosser, Andreas A1 - Ohlsen, Knut T1 - The serine/threonine kinase Stk and the phosphatase Stp regulate cell wall synthesis in Staphylococcus aureus JF - Scientific Reports N2 - The cell wall synthesis pathway producing peptidoglycan is a highly coordinated and tightly regulated process. Although the major components of bacterial cell walls have been known for decades, the complex regulatory network controlling peptidoglycan synthesis and many details of the cell division machinery are not well understood. The eukaryotic-like serine/threonine kinase Stk and the cognate phosphatase Stp play an important role in cell wall biosynthesis and drug resistance in S. aureus. We show that stp deletion has a pronounced impact on cell wall synthesis. Deletion of stp leads to a thicker cell wall and decreases susceptibility to lysostaphin. Stationary phase Δstp cells accumulate peptidoglycan precursors and incorporate higher amounts of incomplete muropeptides with non-glycine, monoglycine and monoalanine interpeptide bridges into the cell wall. In line with this cell wall phenotype, we demonstrate that the lipid II:glycine glycyltransferase FemX can be phosphorylated by the Ser/Thr kinase Stk in vitro. Mass spectrometric analyses identify Thr32, Thr36 and Ser415 as phosphoacceptors. The cognate phosphatase Stp dephosphorylates these phosphorylation sites. Moreover, Stk interacts with FemA and FemB, but is unable to phosphorylate them. Our data indicate that Stk and Stp modulate cell wall synthesis and cell division at several levels. KW - bacterial transcription KW - pathogens KW - cell wall synthesis Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-177333 VL - 8 IS - 13693 ER -