Coronavirus disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progress to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19 associated with increased neutrophil counts and dysregulated immune responses remain unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole-blood and peripheral-blood mononuclear cells to determine changes in immune cell composition and activation in mild versus severe COVID-19 (242 samples from 109 individuals) over time. HLA-DR hi CD11c hi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DR lo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in the myeloid cell compartment associated with severe 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.
Background In face of the Coronavirus Disease (COVID)-19 pandemic, best practice for mechanical ventilation in COVID-19 associated Acute Respiratory Distress Syndrome (ARDS) is intensely debated. Specifically, the rationale for high positive end-expiratory pressure (PEEP) and prone positioning in early COVID-19 ARDS has been questioned. Methods The first 23 consecutive patients with COVID-19 associated respiratory failure transferred to a single ICU were assessed. Eight were excluded: five were not invasively ventilated and three received veno-venous ECMO support. The remaining 15 were assessed over the first 15 days of mechanical ventilation. Best PEEP was defined by maximal oxygenation and was determined by structured decremental PEEP trials comprising the monitoring of oxygenation, airway pressures and trans-pulmonary pressures. In nine patients the impact of prone positioning on oxygenation was investigated. Additionally, the effects of high PEEP and prone positioning on pulmonary opacities in serial chest x-rays were determined by applying a semiquantitative scoring-system. This investigation is part of the prospective observational PA-COVID-19 study. Findings Patients responded to initiation of invasive high PEEP ventilation with markedly improved oxygenation, which was accompanied by reduced pulmonary opacities within 6 h of mechanical ventilation. Decremental PEEP trials confirmed the need for high PEEP (17.9 (SD ± 3.9) mbar) for optimal oxygenation, while driving pressures remained low. Prone positioning substantially increased oxygenation ( p <0.01). Interpretation In early COVID-19 ARDS, substantial PEEP values were required for optimizing oxygenation. Pulmonary opacities resolved during mechanical ventilation with high PEEP suggesting recruitment of lung volume. Funding German Research Foundation, German Federal Ministry of Education and Research.
PurposeCopy-number variants (CNVs) are generally interpreted by linking the effects of gene dosage with phenotypes. The clinical interpretation of noncoding CNVs remains challenging. We investigated the percentage of disease-associated CNVs in patients with congenital limb malformations that affect noncoding cis-regulatory sequences versus genes sensitive to gene dosage effects.MethodsWe applied high-resolution copy-number analysis to 340 unrelated individuals with isolated limb malformation. To investigate novel candidate CNVs, we re-engineered human CNVs in mice using clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing.ResultsOf the individuals studied, 10% harbored CNVs segregating with the phenotype in the affected families. We identified 31 CNVs previously associated with congenital limb malformations and four novel candidate CNVs. Most of the disease-associated CNVs (57%) affected the noncoding cis-regulatory genome, while only 43% included a known disease gene and were likely to result from gene dosage effects. In transgenic mice harboring four novel candidate CNVs, we observed altered gene expression in all cases, indicating that the CNVs had a regulatory effect either by changing the enhancer dosage or altering the topological associating domain architecture of the genome.ConclusionOur findings suggest that CNVs affecting noncoding regulatory elements are a major cause of congenital limb malformations.
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.
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