TY - GEN A1 - Göthel, Markus A1 - Listek, Martin A1 - Messerschmidt, Katrin A1 - Schlör, Anja A1 - Hönow, Anja A1 - Hanack, Katja T1 - A New Workflow to Generate Monoclonal Antibodies against Microorganisms T2 - Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1174 KW - monoclonal antibody KW - antibody producing cell selection KW - hybridoma KW - epitope prediction Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-523341 SN - 1866-8372 IS - 20 ER - TY - JOUR A1 - Göthel, Markus A1 - Listek, Martin A1 - Messerschmidt, Katrin A1 - Schlör, Anja A1 - Hönow, Anja A1 - Hanack, Katja T1 - A New Workflow to Generate Monoclonal Antibodies against Microorganisms JF - Applied Sciences N2 - 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. KW - monoclonal antibody KW - antibody producing cell selection KW - hybridoma KW - epitope prediction Y1 - 2021 U6 - https://doi.org/10.3390/app11209359 SN - 1454-5101 VL - 11 IS - 20 PB - MDPI CY - Basel ER - TY - THES A1 - Göthel, Markus T1 - Entwicklung eines Verfahrens zur Generierung von spezifischen monoklonalen Antikörpern gegen Mikroorganismen basierend auf in silico Epitopanalysen T1 - A new workflow to generate monoclonal antibodies against microorganisms based on in silico epitope predictions N2 - Monoklonale Antikörper (mAK) sind eines der wichtigsten Biomoleküle für die Umweltanalytik und die medizinische Diagnostik. Für die Detektion von Mikroorganismen bilden sie die Grundlage für ein schnelles und präzises Testverfahren. Bis heute gibt es, aufgrund des hohen zeitlichen und materiellen Aufwandes und der unspezifischen Immunisierungsstrategien, nur wenige mAK, die spezifisch Mikroorganismen erkennen. Zu diesem Zweck sollte ein anwendbares Verfahren für die Generierung von mAK gegen Mikroorganismen entwickelt werden, welches anhand von Escherichia coli O157:H7 und Legionella pneumophila validiert wurde. In dieser Dissertation konnten neue Oberflächenstrukturen auf den Mikroorganismen mittels vergleichender Genomanalysen und in silico Epitopanalysen identifiziert werden. Diese wurden in das Virushüllprotein VP1 integriert und für eine gezielte Immunisierungsstrategie verwendet. Für die Bestimmung antigenspezifischer antikörperproduzierender Hybridome wurde ein Immunfärbeprotokoll entwickelt und etabliert, um die Hybridome im Durchflusszytometer zu sortieren. In der vorliegenden Studie konnten für E. coli O157:H7 insgesamt 53 potenzielle Proteinkandidaten und für L. pneumophila 38 Proteine mithilfe der bioinformatischen Analyse identifiziert werden. Fünf verschiedene potenzielle Epitope wurden für E. coli O157:H7 und drei verschiedenen für L. pneumophila ausgewählt und für die Immunisierung mit chimären VP1 verwendet. Alle Immunseren zeigten eine antigenspezifische Immunantwort. Aus den nachfolgend generierten Hybridomzellen konnten mehrere Antikörperkandidaten gewonnen werden, welche in Charakterisierungsstudien eine starke Bindung zu E. coli O157:H7 bzw. L. pneumophila vorwiesen. Kreuzreaktivitäten zu anderen relevanten Mikroorganismen konnten keine bzw. nur in geringem Maße festgestellt werden. Folglich konnte der hier beschriebene interdisziplinäre Ansatz zur Generierung spezifischer mAK gegen Mikroorganismen nachweislich spezifische mAK hervorbringen und ist als hocheffizienter Arbeitsablauf für die Herstellung von Antikörpern gegen Mikroorganismen einsetzbar. N2 - Monoclonal antibodies (mAbs) are one of the most important biomolecules for environmental analysis and medical diagnostics. For the detection of microorganisms, they form the basis for a rapid and precise test procedure. Until today, due to the substantial time and material effort and the non-specific immunization strategies, there are only a few mAbs that specifically detect microorganisms. Therefore, an easy-to-use methodology for the generation of mAbs against microorganisms was developed and validated using Legionella pneumophila and Escherichia coli O157:H7. In this dissertation, several new surface structures on the microorganisms were identified using comparative genomic analyses and in silico epitope modeling. These were integrated into the VP1 viral envelope protein and used for a specific immunization strategy. For the identification of antigen-specific antibody-producing hybridomas, an immunostaining protocol was developed and established to sort the hybridomas. In the present study, 53 potential protein candidates were identified for E. coli O157:H7 and 38 proteins for L. pneumophila using bioinformatic analysis methods. Five different peptide epitopes were selected for E. coli O157:H7 and three different peptide epitopes for L. pneumophila for immunization using chimeric VP1. All immune sera showed an antigen-specific immune response. Several antibody candidates were obtained from the generated hybridoma cells, which showed strong binding to E. coli O157:H7 and L. pneumophila. Cross-reactivity to other relevant microorganisms could not be detected or could only be observed to a minor extent. Consequently, the interdisciplinary approach to generate specific mAbs against microorganisms has been shown to generate specific mAbs and is applicable as a highly efficient workflow for the generation of antibodies against microorganisms. KW - Antikörper KW - antibody KW - Epitopvorhersage KW - epitop prediction KW - Escherichia coli KW - legionella pneumophila Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-588017 ER -