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Precision arbovirus serology with a pan-arbovirus peptidome

Arthropod-borne viruses represent a crucial public health threat. Current arboviral serology assays are either labor intensive or incapable of distinguishing closely related viruses, and many zoonotic arboviruses that may transition to humans lack any serologic assays. In this study, we present a pr...

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Bibliographic Details
Published in:Nature communications 2024-07, Vol.15 (1), p.5833-12, Article 5833
Main Authors: Morgenlander, William R., Chia, Wan Ni, Parra, Beatriz, Monaco, Daniel R., Ragan, Izabela, Pardo, Carlos A., Bowen, Richard, Zhong, Diana, Norris, Douglas E., Ruczinski, Ingo, Durbin, Anna, Wang, Lin-Fa, Larman, H. Benjamin, Robinson, Matthew L.
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Language:English
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Summary:Arthropod-borne viruses represent a crucial public health threat. Current arboviral serology assays are either labor intensive or incapable of distinguishing closely related viruses, and many zoonotic arboviruses that may transition to humans lack any serologic assays. In this study, we present a programmable phage display platform, ArboScan, that evaluates antibody binding to overlapping peptides that represent the proteomes of 691 human and zoonotic arboviruses. We confirm that ArboScan provides detailed antibody binding information from animal sera, human sera, and an arthropod blood meal. ArboScan identifies distinguishing features of antibody responses based on exposure history in a Colombian cohort of Zika patients. Finally, ArboScan details epitope level information that rapidly identifies candidate epitopes with potential protective significance. ArboScan thus represents a resource for characterizing human and animal arbovirus antibody responses at cohort scale. Arboviruses are transmitted by arthropods and include a number of critical human and zoonotic pathogens. Here the authors report a phage display library, ArboScan, for evaluating antibody binding to the peptidome of 691 human and zoonotic arthropod borne viruses, and use it to profile anti-arbovirus antibodies from diverse samples.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-49461-0