@article{WagnerSlaghuisGoebeletal.2021,
  author    = {Wagner, Martin and Slaghuis, J{\"o}rg and G{\"o}bel, Werner and V{\´a}zquez-Boland, Jos{\´e} Antonio and Rychli, Kathrin and Schmitz-Esser, Stephan},
  title     = {Virulence pattern analysis of three Listeria monocytogenes lineage I epidemic strains with distinct outbreak histories},
  series = {Microorganisms},
  volume    = {9},
  journal   = {Microorganisms},
  number    = {8},
  issn      = {2076-2607},
  doi       = {10.3390/microorganisms9081745},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245093},
  year      = {2021},
  abstract  = {Strains of the food-borne pathogen Listeria (L.) monocytogenes have diverse virulence potential. This study focused on the virulence of three outbreak strains: the CC1 strain PF49 (serovar 4b) from a cheese-associated outbreak in Switzerland, the clinical CC2 strain F80594 (serovar 4b), and strain G6006 (CC3, serovar 1/2a), responsible for a large gastroenteritis outbreak in the USA due to chocolate milk. We analysed the genomes and characterized the virulence in vitro and in vivo. Whole-genome sequencing revealed a high conservation of the major virulence genes. Minor deviations of the gene contents were found in the autolysins Ami, Auto, and IspC. Moreover, different ActA variants were present. Strain PF49 and F80594 showed prolonged survival in the liver of infected mice. Invasion and intracellular proliferation were similar for all strains, but the CC1 and CC2 strains showed increased spreading in intestinal epithelial Caco2 cells compared to strain G6006. Overall, this study revealed long-term survival of serovar 4b strains F80594 and PF49 in the liver of mice. Future work will be needed to determine the genes and molecular mechanism behind the long-term survival of L. monocytogenes strains in organs.},
  language  = {en}
}
@article{RemmeleLutherBalkenholetal.2015,
  author    = {Remmele, Christian W. and Luther, Christian H. and Balkenhol, Johannes and Dandekar, Thomas and M{\"u}ller, Tobias and Dittrich, Marcus T.},
  title     = {Integrated inference and evaluation of host-fungi interaction networks},
  series = {Frontiers in Microbiology},
  volume    = {6},
  journal   = {Frontiers in Microbiology},
  number    = {764},
  doi       = {10.3389/fmicb.2015.00764},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148278},
  year      = {2015},
  abstract  = {Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi human and fungi mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host fungi transcriptome and proteome data.},
  language  = {en}
}