A robust cell-based infection model for Rhinovirus C research and antiviral drug discovery

Rhinoviruses (RV) comprise three species, RV-A, RV-B, and RV-C, with approximately 170 types. RV-C is associated with severe respiratory illness, particularly in children and individuals with asthma or chronic obstructive pulmonary disease, underscoring the need for effective antiviral strategies. Progress in RV-C research and drug discovery has been limited by the lack of robust, scalable cell-based infection models that recapitulate the complete RV-C replication cycle. Here, we describe a high-content imaging (HCI)-based high-throughput infection system for RV-C. Rather than relying solely on receptor overexpression, we used a genetically stable fluorescent reporter virus (RV-C15a-mGL) to screen ~300 monoclonal cell lines expressing the RV-C receptor variant CDHR3-Tyr529. This approach identified a clone that efficiently supports RV-C replication and revealed that productive infection depends on determinants beyond receptor abundance alone. Using this clone, we established and validated a robust, scalable screening platform with Z' > 0.75 in both 96- and 384-well formats. The system was readily adapted to additional RV-C types (C11 and C41), as well as RV-A and RV-B. A pilot screen of approximately 10,000 small molecules identified both known and novel RV-C inhibitors, supporting the utility of this platform for antiviral discovery and for advancing the study of RV-C biology.

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Publication Details

Journal
npj Viruses
Published
2026-05-11
DOI
https://doi.org/10.1038/s44298-026-00194-5
Primary Topic
Respiratory viral infections research
Type
article
Field-Weighted Citation Impact
0.00

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article

A robust cell-based infection model for Rhinovirus C research and antiviral drug discovery

Pieter Leyssen, Jasmine Paulissen, Madina Rasulova, Hendrik Jan Thibaut et al.
npj Viruses
Respiratory viral infections research
article

A robust cell-based infection model for Rhinovirus C research and antiviral drug discovery

Pieter Leyssen, Jasmine Paulissen, Madina Rasulova, Hendrik Jan Thibaut, C. Chang Yun, Soo-Bong Han, Nam‐Chul Cho, Céline Sablon, Nathalie Thys, Kai Dallmeier, Johan Neyts, Toon Röpke, Yeranddy A. Alpizar, Heyrhyoung Lyoo
article en

Abstract

Rhinoviruses (RV) comprise three species, RV-A, RV-B, and RV-C, with approximately 170 types. RV-C is associated with severe respiratory illness, particularly in children and individuals with asthma or chronic obstructive pulmonary disease, underscoring the need for effective antiviral strategies. Progress in RV-C research and drug discovery has been limited by the lack of robust, scalable cell-based infection models that recapitulate the complete RV-C replication cycle. Here, we describe a high-content imaging (HCI)-based high-throughput infection system for RV-C. Rather than relying solely on receptor overexpression, we used a genetically stable fluorescent reporter virus (RV-C15a-mGL) to screen ~300 monoclonal cell lines expressing the RV-C receptor variant CDHR3-Tyr529. This approach identified a clone that efficiently supports RV-C replication and revealed that productive infection depends on determinants beyond receptor abundance alone. Using this clone, we established and validated a robust, scalable screening platform with Z' > 0.75 in both 96- and 384-well formats. The system was readily adapted to additional RV-C types (C11 and C41), as well as RV-A and RV-B. A pilot screen of approximately 10,000 small molecules identified both known and novel RV-C inhibitors, supporting the utility of this platform for antiviral discovery and for advancing the study of RV-C biology.

npj Viruses
Institute of Chemical Technology (IN), Rega Institute for Medical Research (BE), Institut de Recherche Vaccinale (FR), Korea Research Institute of Chemical Technology (KR), Sungkyunkwan University (KR), Korea Institute of Science & Technology Information (KR), Korea University of Science and Technology (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Good health and well-being
Openalex Percentile: Top 7%
Respiratory viral infections research
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