Functional proteomic profiling links deficient DNA clearance to mortality in patients with severe COVID-19 pneumonia (preprint)

Hyperinflammation, coagulopathy and immune dysfunction are prominent in patients with severe infections. Extracellular chromatin clearance by plasma DNases suppresses such pathologies in mice but whether severe infection interferes with these pathways is unclear. Here, we show that patients with severe SARS-CoV-2 infection or microbial sepsis exhibit low extracellular DNA clearance capacity associated with the release of the DNase inhibitor actin. Unlike naked DNA degradation (DNase), neutrophil extracellular trap degradation (NETase) was insensitive to G-actin, indicating distinct underlying mechanisms. Functional proteomic profiling of severely ill SARS-CoV-2 patient plasma revealed that patients with high NETase and DNase activities exhibited 18-fold higher survival compared to patients with low activity proteomic profiles. Remarkably, low DNA clearance capacity was also prominent in healthy individuals with chronic inflammation, suggesting that pre-existing inflammatory conditions may increase the risk for mortality upon infection. Hence, functional proteomic profiling illustrates that non-redundant DNA clearance activities protect critically ill patients from mortality, uncovering protein combinations that can accurately predict mortality in critically ill patients.

A proteomic survival predictor for COVID-19 patients in intensive care (preprint)

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.

A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome (preprint)

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.