ABSTRACT
In this study, we examined the relationships between anti-influenza virus serum antibody titers, clinical disease, and peripheral blood leukocyte (PBL) global gene expression during presymptomatic, acute, and convalescent illness in 83 participants infected with 2009 pandemic H1N1 virus in a human influenza challenge model. Using traditional statistical and logistic regression modeling approaches, profiles of differentially expressed genes that correlated with active viral shedding, predicted length of viral shedding, and predicted illness severity were identified. These analyses further demonstrated that challenge participants fell into three peripheral blood leukocyte gene expression phenotypes that significantly correlated with different clinical outcomes and prechallenge serum titers of antibodies specific for the viral neuraminidase, hemagglutinin head, and hemagglutinin stalk. Higher prechallenge serum antibody titers were inversely correlated with leukocyte responsiveness in participants with active disease and could mask expression of peripheral blood markers of clinical disease in some participants, including viral shedding and symptom severity. Consequently, preexisting anti-influenza antibodies may modulate PBL gene expression, and this must be taken into consideration in the development and interpretation of peripheral blood diagnostic and prognostic assays of influenza infection.
IMPORTANCE Influenza A viruses are significant human pathogens that caused 83,000 deaths in the United States during 2017 to 2018, and there is need to understand the molecular correlates of illness and to identify prognostic markers of viral infection, symptom severity, and disease course. Preexisting antibodies against viral neuraminidase (NA) and hemagglutinin (HA) proteins play a critical role in lessening disease severity. We performed global gene expression profiling of peripheral blood leukocytes collected during acute and convalescent phases from a large cohort of people infected with A/H1N1pdm virus. Using statistical and machine-learning approaches, populations of genes were identified early in infection that correlated with active viral shedding, predicted length of shedding, or disease severity. Finally, these gene expression responses were differentially affected by increased levels of preexisting influenza antibodies, which could mask detection of these markers of contagiousness and disease severity in people with active clinical disease.
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