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.

Clinical and Virological Characteristics of Hospitalized COVID-19 Patients in a German Tertiary Care Center during the First Wave of the SARS-CoV-2 Pandemic (preprint)

BackgroundAdequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. MethodsA cohort of 168 hospitalized adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care center was analyzed. ResultsForty-four percent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95%CI 1.10-1.37, p<0.01), age 60-69 as compared to 18-59 years (aOR 4.33, 95%CI 1.07-20.10, p=0.04), and history of hypertension (aOR 5.55, 95%CI 2.00-16.82, p<0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p<0.01). Median duration of hospitalization was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV-patients. ConclusionOur results indicate a short duration of symptoms before admission as a risk factor for severe disease and different viral load kinetics in severely affected patients.

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.

Suppressive myeloid cells are a hallmark of severe COVID-19 (preprint)

Severe Acute Respiratory Syndrome - Coronavirus-2 (SARS-CoV-2) infection causes Coronavirus Disease 2019 (COVID-19), a mild to moderate respiratory tract infection in the majority of patients. A subset of patients, however, progresses to severe disease and respiratory failure with acute respiratory distress syndrome (ARDS). Severe COVID-19 has been associated with increased neutrophil counts and dysregulated immune responses. The mechanisms of protective immunity in mild forms and the pathogenesis of dysregulated inflammation in severe courses of COVID-19 remain largely unclear. Here, we combined two single-cell RNA-sequencing technologies and single-cell proteomics in whole blood and peripheral blood mononuclear cells (PBMC) to determine changes in immune cell composition and activation in two independent dual-center patient cohorts (n=46+n=54 COVID-19 samples), each with mild and severe cases of COVID-19. We observed a specific increase of HLA-DRhiCD11chi inflammatory monocytes that displayed a strong interferon (IFN)-stimulated gene signature in patients with mild COVID-19, which was absent in severe disease. Instead, we found evidence of emergency myelopoiesis, marked by the occurrence of immunosuppressive pre-neutrophils and immature neutrophils and populations of dysfunctional and suppressive mature neutrophils, as well as suppressive HLA-DRto monocytes in severe COVID-19. Our study provides detailed insights into systemic immune response to SARS-CoV-2 infection and it reveals profound alterations in the peripheral myeloid cell compartment associated with severe courses of COVID-19.

Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics (preprint)

The COVID-19 pandemic is an unprecedented global challenge. Highly variable in its presentation, spread and clinical outcome, novel point-of-care diagnostic classifiers are urgently required. Here, we describe a set of COVID-19 clinical classifiers discovered using a newly designed low-cost high-throughput mass spectrometry-based platform. Introducing a new sample preparation pipeline coupled with short-gradient high-flow liquid chromatography and mass spectrometry, our methodology facilitates clinical implementation and increases sample throughput and quantification precision. Providing a rapid assessment of serum or plasma samples at scale, we report 27 biomarkers that distinguish mild and severe forms of COVID-19, of which some may have potential as therapeutic targets. These proteins highlight the role of complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling upstream and downstream of Interleukin 6. Application of novel methodologies hence transforms proteomics from a research tool into a rapid-response, clinically actionable technology adaptable to infectious outbreaks. Highlights- A completely redesigned clinical proteomics platform increases throughput and precision while reducing costs. - 27 biomarkers are differentially expressed between WHO severity grades for COVID-19. - The study highlights potential therapeutic targets that include complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling both upstream and downstream of interleukin 6.