ABSTRACT
BackgroundPrognostic markers for COVID-19 disease outcome are currently lacking. Plasma gelsolin (pGSN) is an actin-binding protein and an innate immune marker involved in disease pathogenesis and viral infections. Here, we demonstrate the utility of pGSN as a prognostic marker for COVID-19 disease outcome; a test performance that is significantly improved when combined with cytokines and antibodies compared to other conventional markers such as CRP and ferritin. MethodsBlood samples were longitudinally collected from hospitalized COVID-19 patients as well as COVID-19 negative controls and the levels of pGSN in g/mL, cytokines and anti-SARS-CoV-2 spike protein antibodies assayed. Mean{+/-}SEM values were correlated with clinical parameters to develop a prognostic platform. ResultspGSN levels were significantly reduced in COVID-19 patients compared to healthy individuals. Additionally, pGSN levels combined with plasma IL-6, IP-10 and M-CSF significantly distinguished COVID-19 patients from healthy individuals. While pGSN and anti-spike IgG titers together strongly predict COVID-19 severity and death, the combination of pGSN and IL-6 was a significant predictor of milder disease and favorable outcomes. ConclusionTaken together, these findings suggest that multi-parameter analysis of pGSN, cytokines and antibodies could predict COVID-19 hospitalization outcomes with greater certainty compared with conventional clinical laboratory markers such as CRP and ferritin. This research will inform and improve clinical management and health system interventions in response to SARS-CoV-2 infection. Trial RegistrationN/A FundingThe Ottawa Hospital Department of Medicine - Special Pandemic Agile Research Competition
ABSTRACT
BackgroundAntibodies raised against human seasonal coronaviruses (sCoVs), which are responsible for the common cold, are known to cross-react with SARS-CoV-2 antigens. This prompts questions about their protective role against SARS-CoV-2 infections and COVID-19 severity. However, the relationship between sCoV exposure and SARS-CoV-2 correlates of protection are not clearly identified. MethodsWe performed a cross-sectional analysis of cross-reactivity and cross-neutralization to SARS-CoV-2 antigens (S-RBD, S-trimer, N) using pre-pandemic serum from four different groups: pediatrics and adolescents, persons 21 to 70 years of age, older than 70 years of age, and persons living with HCV or HIV. FindingsAntibody cross-reactivity to SARS-CoV-2 antigens varied between 1.6% and 15.3% depending on the cohort and the isotype-antigen pair analyzed. We also show a range of neutralizing activity (0-45%) in serum that interferes with SARS-CoV-2 spike attachment to ACE2. While the abundance of sCoV antibodies did not directly correlate with neutralization, we show that neutralizing activity is rather dependent on relative ratios of IgGs in sera directed to all four sCoV spike proteins. More specifically, we identified antibodies to NL63 and OC43 as being the most important predictors of neutralization. InterpretationOur data support that exposure to sCoVs triggers antibody responses that influence the efficiency of SARS-CoV-2 spike binding to ACE2, and may also impact COVID-19 disease severity through other latent variables. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSThere is a growing body of evidence showing that within the population there are varying levels of pre-existing immunity to SARS-CoV-2 infection and possibly COVID-19 disease severity. This immunity is believed to be attributable to prior infection by four prevalent seasonal coronaviruses (sCoVs) responsible for the common cold. Pre-existing immunity can be assessed in part by antibodies directed to sCoVs that also cross-react to SARS-CoV-2 antigens. The SARS-CoV-2 spike and, more specifically, the receptor binding domain are the primary targets for neutralizing antibodies. It is unclear if cross-reactive antibodies to SARS-CoV-2 are neutralizing and are also responsible for the broad spectrum of COVID-19 disease severity, from asymptomatic to critical, observed in the infected population. Added-value of this studyHere we carried out a detailed analysis of sCoV prevalence in samples acquired before the pandemic from individuals of various age groups and in people living with HIV and HCV. We then analyzed the frequency of all the different types of antibodies that cross-react to three SARS-CoV-2 antigens. We found a high level of people with cross-reactive antibodies, surprisingly we also detected that some people have antibodies that block the SARS-CoV-2 spike from binding to its human receptor, ACE2. By using machine learning, we were able to accurate predict which individuals can neutralize SARS-CoV-2 spike-ACE2 interactions based on their relative ratios of antibodies against the four sCoVs. Implications of all the available evidenceWe demonstrate that it not absolute levels of sCoVs antibodies that are predictive of neutralization but the relative ratios to all four sCoVs, with NL63 being the most weighted for this prediction. Machine learning also highlighted the existence of latent variables that contribute to the neutralization and that may be related to the type of cellular immune response triggered by the infection to certain sCoVs. This study is one of the first to identify a functional relationship between prior-exposure to sCoV and the establishment of a certain degree of immunity to SARS-CoV-2 by way of a cross-reactive antibody response. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/21260079v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@e74392org.highwire.dtl.DTLVardef@1052bd5org.highwire.dtl.DTLVardef@80d88eorg.highwire.dtl.DTLVardef@10976cb_HPS_FORMAT_FIGEXP M_FIG C_FIG
