Multi-variate statistical and machine learning reveals the interplay between sex and age in antibody responses to de novo SARS-CoV-2 infection and vaccination (preprint)

Prevention of negative COVID19 infection outcomes and infection/vaccine-acquired immunity is associated with the quality of antibody responses, whose variance by age and sex are poorly understood. Integrated, network approaches, identified sex and age effects in antibody responses and neutralization potential of de novo infection and vaccination throughout the Covid-19 pandemic. Cluster analysis found neutralization values followed SARS-CoV-2 specific receptor binding RIgG, spike SIgG and S and RIgA levels based on COVID19 status. Stochastic behavior tests and other analytical methods revealed sex differences only in persons <40y.o. Serum IgA antibody titers correlated with neutralization only in females 40-60y.o. Network analysis found males could improve IgA responses after vaccination dose 2, unlike >60 y.o. females. Complex correlation analyses found vaccination induced less antibody isotype switching and neutralization in older persons, especially in females. Sex dependent antibody & neutralization behavior decayed fastest in older males and with vaccination. Such sex and age characterization by machine learning can direct studies integrating cell mediated responses to define yet elusive correlates of protection and inform age and sex precision-focused vaccine design.

CLINICAL AND SEROLOGICAL PREDICTORS OF POST COVID-19 CONDITION: FINDINGS FROM A CANADIAN PROSPECTIVE COHORT STUDY (preprint)

Introduction: More than three years into the pandemic, there is persisting uncertainty as to the etiology, biomarkers, and risk factors of Post COVID-19 Condition (PCC). Serological research data remain a largely untapped resource. Few studies have investigated the potential relationships between post-acute serology and PCC, while accounting for clinical covariates. Methods: We compared clinical and serological predictors among COVID-19 survivors with (n=102 cases) and without (n=122 controls) persistent symptoms [≥] 12 weeks post-infection. We selected four primary serological predictors (anti-nucleocapsid (N), anti-Spike, and anti-receptor binding domain (RBD) IgG titres, and neutralization efficiency), and specified clinical covariates a priori. Results: Similar proportions of PCC-cases (66.7%, n=68) and infected-controls (71.3%, n=87) tested positive for anti-N IgG. More cases tested positive for anti-Spike (94.1%, n=96) and anti-RBD (95.1%, n=97) IgG, as compared with controls (anti-Spike: 89.3%, n=109; anti-RBD: 84.4%, n=103). Similar trends were observed among unvaccinated participants. Effects of IgG titres on PCC status were non-significant in univariate and multivariate analyses. Adjusting for age and sex, PCC-cases were more likely to be efficient neutralizers (OR 2.2, 95% CI 1.11 - 4.49), and odds was further increased among cases to report deterioration in quality of life (OR 3.4, 95% CI 1.64 - 7.31). Clinical covariates found to be significantly related to PCC included obesity (OR 2.3, p=0.02), number of months post COVID-19 (OR 1.1, p<0.01), allergies (OR 1.8, p=0.04), and need for medical support (OR 4.1, p<0.01). Conclusion: Despite past COVID-19 infection, approximately one third of PCC-cases and infected-controls were seronegative for anti-N IgG. Findings suggest higher neutralization efficiency among cases as compared with controls, and that this relationship is stronger among cases with more severe PCC. Cases also required more medical support for COVID-19 symptoms, and described complex, ongoing health sequelae. More data from larger cohorts are needed to substantiate results, permit subgroup analyses of IgG titres, and explore for differences between clusters of PCC symptoms. Future assessment of IgG subtypes may also elucidate new findings.

Relative Ratios of Human Seasonal Coronavirus Antibodies Predict the Efficiency of Cross-Neutralization of SARS-CoV-2 Spike Binding to ACE2 (preprint)

Antibodies raised against highly prevalent human seasonal coronaviruses (sCoVs), which are responsible for the common cold, are known to cross-react with SARS-CoV-2 antigens. This cross-reactivity prompts questions about their protective role against SARS-CoV-2 infections and COVID-19 disease severity. However, the relationship between sCoV exposure and SARS-CoV-2 correlates of protection have not been clearly identified. Here we performed a cross-sectional analysis of cross-reactivity and cross-neutralization to three SARS-CoV-2 antigens using pre-pandemic serum from four different groups: pediatrics and adolescents (<21 yrs of age), persons 21 to 70 yrs of age, persons older than 70 yrs of age, and persons living with HCV or HIV. We find that antibody 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 demonstrate a broad range of neutralizing activity (0-45%) in pre-pandemic serum that interferes with SARS-CoV-2 spike attachment to ACE2. While the abundance of sCoV antibodies did not directly correlate with neutralization efficiency, by using machine learning methodologies, we show that neutralizing activity is rather dependent on the latent variables related to the pattern ratios of sCoVs antibodies presented by each person. These were independent of age or sex, and could be accurately predicted by comparing the relative ratios of IgGs in sera directed to NL63, 229E, HKU-1, and OC43 spike proteins. More specifically, we identified antibodies to NL63 and OC43 as being the two most important predictors of latent variables responsible for protection, and 229E as being the least weighted. Our data support that exposure to sCoVs triggers various cellular and immune responses that influence the efficiency of SARS-CoV-2 spike binding to ACE2, and may impact COVID-19 disease severity through various other latent variables.