Phenotypic Timeline Kinetics, Integrative Networks, and Performance of T- and B-Cell Subsets Associated with Distinct Clinical Outcome of Severe COVID-19 Patients

表型时间线动力学、整合网络以及与重症 COVID-19 患者不同临床结局相关的 T 细胞和 B 细胞亚群的性能

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Abstract

The present study aimed to evaluate the kinetics of the phenotypic profile and integrative networks of T/B-cells in severe COVID-19 patients, categorized according to disease outcome, during the circulation of the B.1.1.28 and B.1.1.33 SARS-CoV-2 strains in Brazil. Peripheral blood obtained at distinct time points (baseline/D0; D7; D14-28) was used for ex vivo flow cytometry immunophenotyping. The data demonstrated a decrease at D0 in the frequency of CD3(+) T-cells and CD4(+) T-cells and an increase in B-cells with mixed activation/exhaustion profiles. Higher changes in B-cell and CD4(+) T-cells at D7 were associated with discharge/death outcomes, respectively. Regardless of the lower T/B-cell connectivity at D0, distinct profiles from D7/D14-28 revealed that, while discharge was associated with increasing connectivity for B-cells, CD4(+) and CD8(+) T-cells death was related to increased connectivity involving B-cells, but with lower connections mediated by CD4(+) T-cells. The CD4(+)CD38(+) and CD8(+)CD69(+) subsets accurately classified COVID-19 vs. healthy controls throughout the kinetic analysis. Binary logistic regression identified CD4(+)CD107a(+), CD4(+)T-bet(+), CD8(+)CD69(+), and CD8(+)T-bet(+) at D0 and CD4(+)CD45RO(+)CD27(+) at D7 as subsets associated with disease outcomes. Results showed that distinct phenotypic timeline kinetics and integrative networks of T/B-cells are associated with COVID-19 outcomes that may subsidize the establishment of applicable biomarkers for clinical/therapeutic monitoring.

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