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.

Protocols for High-Resolution FluoRespirometry of intact cells, permeabilized cells, permeabilized muscle fibers, isolated mitochondria, and tissue homogenates offer sensitive diagnostic tests of integrated mitochondrial function using standard cell culture techniques, small needle biopsies of muscle, and mitochondrial preparation methods. Multiple substrate-uncoupler-inhibitor titration (SUIT) protocols for analysis of oxidative phosphorylation (OXPHOS) improve our understanding of mitochondrial respiratory control and the pathophysiology of mitochondrial diseases. Respiratory states are defined in functional terms to account for the network of metabolic interactions in complex SUIT protocols with stepwise modulation of coupling control and electron transfer pathway states. A regulated degree of intrinsic uncoupling is a hallmark of oxidative phosphorylation, whereas pathological and toxicological dyscoupling is evaluated as a mitochondrial defect. The noncoupled state of maximum respiration is experimentally induced by titration of established uncouplers (CCCP, FCCP, DNP) to collapse the protonmotive force across the mitochondrial inner membrane and measure the electron transfer (ET) capacity (open-circuit operation of respiration). Intrinsic uncoupling and dyscoupling are evaluated as the flux control ratio between non-phosphorylating LEAK respiration (electron flow coupled to proton pumping to compensate for proton leaks) and ET capacity. If OXPHOS capacity (maximally ADP-stimulated O flux) is less than ET capacity, the phosphorylation pathway contributes to flux control. Physiological substrate combinations supporting the NADH and succinate pathway are required to reconstitute tricarboxylic acid cycle function. This supports maximum ET and OXPHOS capacities, due to the additive effect of multiple electron supply pathways converging at the Q-junction. ET pathways with electron entry separately through NADH (pyruvate and malate or glutamate and malate) or succinate (succinate and rotenone) restrict ET capacity and artificially enhance flux control upstream of the Q-cycle, providing diagnostic information on specific ET-pathway branches. O concentration is maintained above air saturation in protocols with permeabilized muscle fibers to avoid experimental O limitation of respiration. Standardized two-point calibration of the polarographic oxygen sensor (static sensor calibration), calibration of the sensor response time (dynamic sensor calibration), and evaluation of instrumental background O flux (systemic flux compensation) provide the unique experimental basis for high accuracy of quantitative results and quality control in High-Resolution FluoRespirometry.

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Deficient ether lipid biosynthesis in rhizomelic chondrodysplasia punctata and other disorders is associated with a wide range of severe symptoms including small stature with proximal shortening of the limbs, contractures, facial dysmorphism, congenital cataracts, ichthyosis, spasticity, microcephaly, and mental disability. Mouse models are available but show less severe symptoms. In both humans and mice, it has remained elusive which of the symptoms can be attributed to lack of plasmanyl or plasmenyl ether lipids. The latter compounds, better known as plasmalogens, harbor a vinyl ether double bond conferring special chemical and physical properties. Discrimination between plasmanyl and plasmenyl ether lipids is a major analytical challenge, especially in complex lipid extracts with many isobaric species. Consequently, these lipids are often neglected also in recent lipidomic studies. Here, we present a comprehensive LC–MS/MS based approach that allows unequivocal distinction of these two lipid subclasses based on their chromatographic properties. The method was validated using a novel plasmalogen-deficient mouse model, which lacks plasmanylethanolamine desaturase and therefore cannot form plasmenyl ether lipids. We demonstrate that plasmanylethanolamine desaturase deficiency causes an accumulation of plasmanyl species, a too little studied but biologically important substance class.

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.

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.

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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.