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Selection, identification, and characterization of SARS-CoV-2 monoclonal antibody resistant mutants.

Journal of virological methods (2021-01-30)
Fatai S Oladunni, Jun-Gyu Park, Kevin Chiem, Chengjin Ye, Michael Pipenbrink, Mark R Walter, James Kobie, Luis Martinez-Sobrido
RESUMEN

The use of monoclonal neutralizing antibodies (mNAbs) is being actively pursued as a viable intervention for the treatment of Severe Acute Respiratory Syndrome CoV-2 (SARS-CoV-2) infection and associated coronavirus disease 2019 (COVID-19). While highly potent mNAbs have great therapeutic potential, the ability of the virus to mutate and escape recognition and neutralization of mNAbs represents a potential problem in their use for the therapeutic management of SARS-CoV-2. Studies investigating natural or mNAb-induced antigenic variability in the receptor binding domain (RBD) of SARS-CoV-2 Spike (S) glycoprotein, and their effects on viral fitness are still rudimentary. In this manuscript we described experimental approaches for the selection, identification, and characterization of SARS-CoV-2 monoclonal antibody resistant mutants (MARMs) in cultured cells. The ability to study SARS-CoV-2 antigenic drift under selective immune pressure by mNAbs is important for the optimal implementation of mNAbs for the therapeutic management of COVID-19. This will help to identify essential amino acid residues in the viral S glycoprotein required for mNAb-mediated inhibition of viral infection, to predict potential natural drift variants that could emerge upon implementation of therapeutic mNAbs, as well as vaccine prophylactic treatments for SARS-CoV-2 infection. Additionally, it will also enable the assessment of MARM viral fitness and its potential to induce severe infection and associated COVID-19 disease.

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Cellulose, ~50 μm particle size
Sigma-Aldrich
Seroalbúmina bovina solution, 35% in 0.85% sodium chloride, aseptically filled
Sigma-Aldrich
Anti-SARS-CoV-1/2 NP Antibody, clone 1C7C7 ZooMAb® Mouse Monoclonal, recombinant, expressed in HEK 293 cells