@misc{SchornSalmanCarvalhoLittmannetal.2019,
  author    = {Schorn, Sina and Salman-Carvalho, Verena and Littmann, Sten and Ionescu, Danny and Grossart, Hans-Peter and Cypionka, Heribert},
  title     = {Cell architecture of the giant sulfur bacterium achromatium oxaliferum},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {2},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-54993},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-549935},
  pages     = {10},
  year      = {2019},
  abstract  = {Achromatium oxaliferum is a large sulfur bacterium easily recognized by large intracellular calcium carbonate bodies. Although these bodies often fill major parts of the cells' volume, their role and specific intracellular location are unclear. In this study, we used various microscopy and staining techniques to identify the cell compartment harboring the calcium carbonate bodies. We observed that Achromatium cells often lost their calcium carbonate bodies, either naturally or induced by treatments with diluted acids, ethanol, sodium bicarbonate and UV radiation which did not visibly affect the overall shape and motility of the cells (except for UV radiation). The water-soluble fluorescent dye fluorescein easily diffused into empty cavities remaining after calcium carbonate loss. Membranes (stained with Nile Red) formed a network stretching throughout the cell and surrounding empty or filled calcium carbonate cavities. The cytoplasm (stained with FITC and SYBR Green for nucleic acids) appeared highly condensed and showed spots of dissolved Ca2+ (stained with Fura-2). From our observations, we conclude that the calcium carbonate bodies are located in the periplasm, in extra-cytoplasmic pockets of the cytoplasmic membrane and are thus kept separate from the cell's cytoplasm. This periplasmic localization of the carbonate bodies might explain their dynamic formation and release upon environmental changes.},
  language  = {en}
}
@misc{CuadratIonescuDavilaetal.2018,
  author    = {Cuadrat, Rafael R. C. and Ionescu, Danny and D{\´a}vila, Alberto M. R. and Grossart, Hans-Peter},
  title     = {Recovering genomics clusters of secondary metabolites from lakes using genome-resolved metagenomics},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {924},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-44565},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-445656},
  pages     = {15},
  year      = {2018},
  abstract  = {Metagenomic approaches became increasingly popular in the past decades due to decreasing costs of DNA sequencing and bioinformatics development. So far, however, the recovery of long genes coding for secondary metabolites still represents a big challenge. Often, the quality of metagenome assemblies is poor, especially in environments with a high microbial diversity where sequence coverage is low and complexity of natural communities high. Recently, new and improved algorithms for binning environmental reads and contigs have been developed to overcome such limitations. Some of these algorithms use a similarity detection approach to classify the obtained reads into taxonomical units and to assemble draft genomes. This approach, however, is quite limited since it can classify exclusively sequences similar to those available (and well classified) in the databases. In this work, we used draft genomes from Lake Stechlin, north-eastern Germany, recovered by MetaBat, an efficient binning tool that integrates empirical probabilistic distances of genome abundance, and tetranucleotide frequency for accurate metagenome binning. These genomes were screened for secondary metabolism genes, such as polyketide synthases (PKS) and non-ribosomal peptide synthases (NRPS), using the Anti-SMASH and NAPDOS workflows. With this approach we were able to identify 243 secondary metabolite clusters from 121 genomes recovered from our lake samples. A total of 18 NRPS, 19 PKS, and 3 hybrid PKS/NRPS clusters were found. In addition, it was possible to predict the partial structure of several secondary metabolite clusters allowing for taxonomical classifications and phylogenetic inferences. Our approach revealed a high potential to recover and study secondary metabolites genes from any aquatic ecosystem.},
  language  = {en}
}