@article{GoethelListekMesserschmidtetal.2021,
  author    = {G{\"o}thel, Markus and Listek, Martin and Messerschmidt, Katrin and Schl{\"o}r, Anja and H{\"o}now, Anja and Hanack, Katja},
  title     = {A New Workflow to Generate Monoclonal Antibodies against Microorganisms},
  series = {Applied Sciences},
  volume    = {11},
  journal   = {Applied Sciences},
  number    = {20},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1454-5101},
  doi       = {10.3390/app11209359},
  pages     = {15},
  year      = {2021},
  abstract  = {Monoclonal antibodies are used worldwide as highly potent and efficient detection reagents for research and diagnostic applications. Nevertheless, the specific targeting of complex antigens such as whole microorganisms remains a challenge. To provide a comprehensive workflow, we combined bioinformatic analyses with novel immunization and selection tools to design monoclonal antibodies for the detection of whole microorganisms. In our initial study, we used the human pathogenic strain E. coli O157:H7 as a model target and identified 53 potential protein candidates by using reverse vaccinology methodology. Five different peptide epitopes were selected for immunization using epitope-engineered viral proteins. The identification of antibody-producing hybridomas was performed by using a novel screening technology based on transgenic fusion cell lines. Using an artificial cell surface receptor expressed by all hybridomas, the desired antigen-specific cells can be sorted fast and efficiently out of the fusion cell pool. Selected antibody candidates were characterized and showed strong binding to the target strain E. coli O157:H7 with minor or no cross-reactivity to other relevant microorganisms such as Legionella pneumophila and Bacillus ssp. This approach could be useful as a highly efficient workflow for the generation of antibodies against microorganisms.},
  language  = {en}
}
@misc{GoethelListekMesserschmidtetal.2021,
  author    = {G{\"o}thel, Markus and Listek, Martin and Messerschmidt, Katrin and Schl{\"o}r, Anja and H{\"o}now, Anja and Hanack, Katja},
  title     = {A New Workflow to Generate Monoclonal Antibodies against Microorganisms},
  series = {Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Mathematisch-Naturwissenschaftliche Reihe},
  number    = {20},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-52334},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-523341},
  pages     = {17},
  year      = {2021},
  abstract  = {Monoclonal antibodies are used worldwide as highly potent and efficient detection reagents for research and diagnostic applications. Nevertheless, the specific targeting of complex antigens such as whole microorganisms remains a challenge. To provide a comprehensive workflow, we combined bioinformatic analyses with novel immunization and selection tools to design monoclonal antibodies for the detection of whole microorganisms. In our initial study, we used the human pathogenic strain E. coli O157:H7 as a model target and identified 53 potential protein candidates by using reverse vaccinology methodology. Five different peptide epitopes were selected for immunization using epitope-engineered viral proteins. The identification of antibody-producing hybridomas was performed by using a novel screening technology based on transgenic fusion cell lines. Using an artificial cell surface receptor expressed by all hybridomas, the desired antigen-specific cells can be sorted fast and efficiently out of the fusion cell pool. Selected antibody candidates were characterized and showed strong binding to the target strain E. coli O157:H7 with minor or no cross-reactivity to other relevant microorganisms such as Legionella pneumophila and Bacillus ssp. This approach could be useful as a highly efficient workflow for the generation of antibodies against microorganisms.},
  language  = {en}
}