First contact: an interdisciplinary guide into decoding H5N1 influenza virus interactions with glycosaminoglycans in 3D respiratory cell models - Hassan, Mariam; Kaifer, Bianca; Christian, Tyra; Quaas, Xenia Tamara; Mueller, Johannes; Boehm, Heike (Frontiers in Cellular and Infection Microbiology, 2025)
The human respiratory system is vulnerable to viral infections. The influenza virus family alone accounts for one billion reported cases annually, some of which are severe and can be fatal. Among these, Influenza A viruses (IAVs) cause the most severe symptoms and course of disease. IAV has been a major health concern, especially since the emergence of the potentially pandemic avian H5N1 strain. However, despite the knowledge that IAVs recognize terminally attached sialic acids on the host cell surface for cell entry, the involvement of other glycans during early infection remains to be elucidated. In particular, the involvement of the alveolar epithelial glycocalyx as a last line of defense is often overlooked. Studying early infection of any virus in real time remains a challenge due to the currently available model systems and imaging techniques. Therefore, we extensively compare the use of different 3D cell systems and provide an overview of currently available scaffold-based and scaffold-free air-liquid interface (ALI) models. In addition, we discuss in detail the preferred use of a recently developed 3D organ tissue equivalent (OTE) model incorporating solubilized extracellular matrix components (sECM) to study viral interaction with glycosaminoglycans (GAGs) during the early stages of IAV infection. We further discuss and recommend the use of various synthetic virus models over IAV virions to reduce complexity by focusing only on surface protein interactions while simultaneously lowering the required biosafety levels, including, but not limited to virus-like particles (VLPs) or DNA origami. Finally, we delve into potential labeling strategies for IAV or IAV-like particles by reviewing internal and external labeling strategies with quantum dots (QDs) and potential GAG labeling, combined with a recommendation to combine high spatial resolution imaging techniques with high temporal resolution tracking, such as single virus tracking.