In International journal of biological sciences
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic respiratory infectious disease COVID-19. However, clinical manifestations and outcomes differ significantly among COVID-19 patients, ranging from asymptomatic to extremely severe, and it remains unclear what drives these disparities. Here, we studied 159 sequentially enrolled hospitalized patients with COVID-19-associated pneumonia from Brescia, Italy using the VirScan phage-display method to characterize circulating antibodies binding to 96,179 viral peptides encoded by 1,276 strains of human viruses. SARS-CoV-2 infection was associated with a marked increase in immune antibody repertoires against many known pathogenic and non-pathogenic human viruses. This antiviral antibody response was linked to longitudinal trajectories of disease severity and was further confirmed in additional 125 COVID-19 patients from the same geographical region in Northern Italy. By applying a machine-learning-based strategy, a viral exposure signature predictive of COVID-19-related disease severity linked to patient survival was developed and validated. These results provide a basis for understanding the role of memory B-cell repertoire to viral epitopes in COVID-19-related symptoms and suggest that a unique anti-viral antibody repertoire signature may be useful to define COVID-19 clinical severity.
Wang Limin, Candia Julián, Ma Lichun, Zhao Yongmei, Imberti Luisa, Sottini Alessandra, Quiros-Roldan Eugenia, Dobbs Kerry, Burbelo Peter D, Cohen Jeffrey I, Delmonte Ottavia M, Forgues Marshonna, Liu Hui, Matthews Helen F, Shaw Elana, Stack Michael A, Weber Sarah E, Zhang Yu, Lisco Andrea, Sereti Irini, Su Helen C, Notarangelo Luigi D, Wang Xin Wei