Multivalent Affinity Profiling: Direct Visualization of the Superselective Binding of Influenza Viruses

Nico J. Overeem, P. H. Erik Hamming, Malte Tieke, Erhard Van Der Vries, Jurriaan Huskens*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
107 Downloads (Pure)

Abstract

The influenza A virus (IAV) interacts with the glycocalyx of host cells through its surface proteins hemagglutinin (HA) and neuraminidase (NA). Quantitative biophysical measurements of these interactions may help to understand these interactions at the molecular level with the long-Term aim to predict influenza infectivity and answer other biological questions. We developed a method, called multivalent affinity profiling (MAP), to measure virus binding profiles on receptor density gradients to determine the threshold receptor density, which is a quantitative measure of virus avidity toward a receptor. Here, we show that imaging of IAVs on receptor density gradients allows the direct visualization and efficient assessment of their superselective binding. We show how the multivalent binding of IAVs can be quantitatively assessed using MAP if the receptor density gradients are prepared around the threshold receptor density without crowding at the higher densities. The threshold receptor density increases strongly with increasing flow rate, showing that the superselective binding of IAV is influenced by shear force. This method of visualization and quantitative assessment of superselective binding allows not only comparative studies of IAV-receptor interactions, but also more fundamental studies of how superselectivity arises and is influenced by experimental conditions.

Original languageEnglish
Pages (from-to)8525-8536
Number of pages12
JournalACS nano
Volume15
Issue number5
DOIs
Publication statusPublished - 25 May 2021

Keywords

  • Glycans
  • Influenza viruses
  • Lipid membranes
  • Microfluidics
  • Multivalency
  • Superselectivity
  • Supramolecular chemistry
  • UT-Hybrid-D

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