Longitudinal proteomic investigation of COVID-19 vaccination

ABSTRACT

Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac(R)) at a 28-day interval. Using TMT-based proteomics, we identified 1715 serum and 7342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMCs potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning. HighlightsO_LILongitudinal proteomics of PBMC and serum from individuals vaccinated with CoronaVac(R). C_LIO_LIMachine learning models predict neutralizing antibody generation and decline after COVID-19 vaccination. C_LIO_LIThe adaptive and the innate immune responses are stronger in the seropositive groups (especially in the early seropositive group). C_LIO_LIVaccine-induced immunity involves in lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. C_LI