A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

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

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. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human 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 showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 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 plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care

Increased risk of severe clinical course of COVID-19 in carriers of HLA-C*04:01

BACKGROUND: Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing urgency to identify pathophysiological characteristics leading to severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors that affect individual immune response to SARS-CoV-2. We sought to evaluate this hypothesis by conducting a multicenter study using HLA sequencing. METHODS: We analyzed the association between COVID-19 severity and HLAs in 435 individuals from Germany ((n) = 135), Spain ((n) = 133), Switzerland ((n) = 20) and the United States ((n) = 147), who had been enrolled from March 2020 to August 2020. This study included patients older than 18 years, diagnosed with COVID-19 and representing the full spectrum of the disease. Finally, we tested our results by meta-analysing data from prior genome-wide association studies (GWAS). FINDINGS: We describe a potential association of HLA-C*04:01 with severe clinical course of COVID-19. Carriers of HLA-C*04:01 had twice the risk of intubation when infected with SARS-CoV-2 (risk ratio 1.5 [95% CI 1.1-2.1], odds ratio 3.5 [95% CI 1.9-6.6], adjusted (p)-value = 0.0074). These findings are based on data from four countries and corroborated by independent results from GWAS. Our findings are biologically plausible, as HLA-C*04:01 has fewer predicted bindings sites for relevant SARS-CoV-2 peptides compared to other HLA alleles. INTERPRETATION: HLA-C*04:01 carrier state is associated with severe clinical course in SARS-CoV-2. Our findings suggest that HLA class I alleles have a relevant role in immune defense against SARS-CoV-2

Soluble ACE2 correlates with severe COVID-19 and can impair antibody responses

Identifying immune modulators that impact neutralizing antibody responses against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is of great relevance. We postulated that high serum concentrations of soluble angiotensin-converting enzyme 2 (sACE2) might mask the spike and interfere with antibody maturation toward the SARS-CoV-2-receptor-binding motif (RBM). We tested 717 longitudinal samples from 295 COVID-19 patients and showed a 2- to 10-fold increase of enzymatically active sACE2 (a-sACE2), with up to 1 μg/mL total sACE2 in moderate and severe patients. Fifty percent of COVID-19 sera inhibited ACE2 activity, in contrast to 1.3% of healthy donors and 4% of non-COVID-19 pneumonia patients. A mild inverse correlation of a-sACE2 with RBM-directed serum antibodies was observed. In silico, we show that sACE2 concentrations measured in COVID-19 sera can disrupt germinal center formation and inhibit timely production of high-affinity antibodies. We suggest that sACE2 is a biomarker for COVID-19 and that soluble receptors may contribute to immune suppression informing vaccine design

The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation.

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches

Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19

Longitudinal analyses of the innate immune system, including the earliest time points, are essential to understand the immunopathogenesis and clinical course of coronavirus disease (COVID-19). Here, we performed a detailed characterization of natural killer (NK) cells in 205 patients (403 samples; days 2 to 41 after symptom onset) from four independent cohorts using single-cell transcriptomics and proteomics together with functional studies. We found elevated interferon (IFN)-α plasma levels in early severe COVD-19 alongside increased NK cell expression of IFN-stimulated genes (ISGs) and genes involved in IFN-α signaling, while upregulation of tumor necrosis factor (TNF)-induced genes was observed in moderate diseases. NK cells exert anti-SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) activity but are functionally impaired in severe COVID-19. Further, NK cell dysfunction may be relevant for the development of fibrotic lung disease in severe COVID-19, as NK cells exhibited impaired anti-fibrotic activity. Our study indicates preferential IFN-α and TNF responses in severe and moderate COVID-19, respectively, and associates a prolonged IFN-α-induced NK cell response with poorer disease outcome

Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19.

Longitudinal analyses of the innate immune system, including the earliest time points, are essential to understand the immunopathogenesis and clinical course of coronavirus disease (COVID-19). Here, we performed a detailed characterization of natural killer (NK) cells in 205 patients (403 samples; days 2 to 41 after symptom onset) from four independent cohorts using single-cell transcriptomics and proteomics together with functional studies. We found elevated interferon (IFN)-α plasma levels in early severe COVD-19 alongside increased NK cell expression of IFN-stimulated genes (ISGs) and genes involved in IFN-α signaling, while upregulation of tumor necrosis factor (TNF)-induced genes was observed in moderate diseases. NK cells exert anti-SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) activity but are functionally impaired in severe COVID-19. Further, NK cell dysfunction may be relevant for the development of fibrotic lung disease in severe COVID-19, as NK cells exhibited impaired anti-fibrotic activity. Our study indicates preferential IFN-α and TNF responses in severe and moderate COVID-19, respectively, and associates a prolonged IFN-α-induced NK cell response with poorer disease outcome

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease