@article{JarickBertscheStahletal.2018,
  author    = {Jarick, Marcel and Bertsche, Ute and Stahl, Mark and Schultz, Daniel and Methling, Karen and Lalk, Michael and Stigloher, Christian and Steger, Mirco and Schlosser, Andreas and Ohlsen, Knut},
  title     = {The serine/threonine kinase Stk and the phosphatase Stp regulate cell wall synthesis in Staphylococcus aureus},
  series = {Scientific Reports},
  volume    = {8},
  journal   = {Scientific Reports},
  number    = {13693},
  doi       = {10.1038/s41598-018-32109-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-177333},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{KaltdorfTheissMarkertetal.2018,
  author    = {Kaltdorf, Kristin Verena and Theiss, Maria and Markert, Sebastian Matthias and Zhen, Mei and Dandekar, Thomas and Stigloher, Christian and Kollmannsberger, Philipp},
  title     = {Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning},
  series = {PLoS ONE},
  volume    = {13},
  journal   = {PLoS ONE},
  number    = {10},
  doi       = {10.1371/journal.pone.0205348},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176831},
  pages     = {e0205348},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}