Patterns of Volatility Across the Spike Protein Accurately Predict the Emergence of Mutations within SARS-CoV-2 Lineages (preprint)

New lineages of SARS-CoV-2 are constantly emerging. They contain mutations in the spike glycoprotein that can affect virus infectivity, transmissibility, or sensitivity to vaccine-elicited antibodies. Here we show that the emergence of new spike variants is accurately predicted by patterns of amino acid variability (volatility) in small virus clusters that phylogenetically-precede or chronologically-predate such events. For each spike position, volatility within the virus clusters, volatility at adjacent positions on the three-dimensional structure of the protein, and volatility across the network of co-volatile sites describe its likelihood for mutations. By combining these variables, early-pandemic sequences accurately forecasted mutations in lineages that appeared 6-13 months later. The patterns of mutations in variants Alpha and Delta, as well as the recently-appearing variant Omicron were also predicted remarkably well. Importantly, probabilities assigned to spike positions for within-lineage mutations were lineage-specific, and accurately forecasted the observed changes. Sufficient antecedent warning of the imminent changes in SARS-CoV-2 lineages will allow design of immunogens that address their specific antigenic properties.

Antibodies targeting conserved non-canonical antigens and endemic coronaviruses associate with favorable outcomes in severe COVID-19 (preprint)

While there have been extensive analyses characterizing cellular and humoral responses across the severity spectrum in COVID-19, predictors of outcomes within severe COVID-19 remain to be comprehensively elucidated. Recently, we identified divergent monocyte states as predictors of outcomes within severe COVID-19, but corresponding humoral profiles of risk have not been delineated. Furthermore, the nature of antibodies (Abs) directed against viral antigens beyond the spike protein or endemic coronavirus antigens and their associations with disease severity and outcomes remain poorly defined. We performed deep molecular profiling of Abs directed against a wide range of antigenic specificities in severe COVID-19 patients admitted to the ICU. The profiles consisted of canonical (S, RBD, N) and non-canonical (orf3a, orf8, nsp3, nps13 and M) antigenic specificities. Notably, multivariate machine learning (ML) models, generated using profiles of Abs directed against canonical or non-canonical antigens, were equally discriminative of recovery and mortality COVID-19 outcomes. In both ML models, survivors were associated with increased virus-specific IgA and IgG3 antibodies and with higher antigen-specific antibody galactosylation. Intriguingly, pre-pandemic healthy controls had cross-reactive Abs directed against nsp13 which is a conserved protein in other alpha and beta coronaviruses. Notably, higher levels of nsp13-specific IgA antibodies were associated with recovery in severe COVID-19. In keeping with these findings, a model built on Ab profiles for endemic coronavirus antigens was also predictive of COVID-19 outcome bifurcation, with higher levels of IgA and IgG3 antibodies against OC43 S and NL63 S being associated with survival. Our results suggest the importance of Abs targeting non-canonical SARS-CoV-2 antigens as well as those directed against endemic coronaviruses in favorable outcomes of severe COVID-19.