bioRxivImmunology
Evolution of inflammation and immunity in a dengue virus 1 human infection model
A. T. Waickman, J. Q. Lu, H. Fang, M. Waldran, C. Gebo, J. R. Currier, L. Van Wesenbeeck, N. Verpoorten, O. Lenz, L. Tambuyzer, G. Herrera-Taracena, M. Van Loock, T. P. Endy, S. J. Thomas
Abstract
Dengue virus (DENV) infections are significant sources of morbidity and mortality throughout the tropics and subtropics. Over 400 million infections are estimated to occur every year, resulting in nearly 100 million symptomatic infections and over 20,000 deaths. Early immune response kinetics to infection remain unclear, in large part due to the variable incubation period exhibited by the DENVs after introduction into a susceptible host. To fill this knowledge gap, we performed a comprehensive virologic and immunologic analysis of individuals experimentally infected with the under-attenuated DENV-1 strain 45AZ5. This analysis captured both the kinetics and composition of the innate, humoral, and cellular immune responses elicited by experimental DENV-1 infection, as well as virologic and clinical features. Revealed in this analysis was a robust DENV-specific IgA antibody response that manifested between the appearance of DENV-specific IgM and IgG in all challenged individuals, as well as the presence of a non-neutralizing/NS1-specific antibody response that was delayed relative to the appearance of DENV-virion specific humoral immunity. RNAseq analysis also revealed several distinct and temporally-restricted gene modules that allowed for the identification and differentiation of the innate and adaptive immune response to DENV-infection. Our analysis provides a detailed description, in time and space, of the evolving matrix of DENV-elicited human inflammation and immunity and reveals several previously unappreciated immunological aspects of primary DENV-1 infection that can inform countermeasure development and evaluation.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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