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The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.

The B7 family represents one of the best-studied subgroups within the Ig superfamily, yet new interactions continue to be discovered. However, this binding promiscuity represents a major challenge for defining the biological contribution of each specific interaction. We developed a strategy for addressing these challenges by combining cell microarray and high-throughput FACS methods to screen for promiscuous binding events, map binding interfaces, and generate functionally selective reagents. Applying this approach to the interactions of mPD-L1 with its receptor mPD-1 and its ligand mB7-1, we identified the binding interface of mB7-1 on mPD-L1 and as a result generated mPD-L1 mutants with binding selectivity for mB7-1 or mPD-1. Next, using a panel of mB7-1 mutants, we mapped the binding sites of mCTLA-4, mCD28 and mPD-L1. Surprisingly, the mPD-L1 binding site mapped to the dimer interface surface of mB7-1, placing it distal from the CTLA-4/CD28 recognition surface. Using two independent approaches, we demonstrated that mPD-L1 and mB7-1 bind in cis, consistent with recent reports from Chaudhri A et al. and Sugiura D et al. We further provide evidence that while CTLA-4 and CD28 do not directly compete with PD-L1 for binding to B7-1, they can disrupt the cis PD-L1:B7-1 complex by reorganizing B7-1 on the cell surface. These observations offer new functional insights into the regulatory mechanisms associated with this group of B7 family proteins and provide new tools to elucidate their function in vitro and in vivo.