Dataset Information

Targeted MS based multi-omic analysis of blood plasma from COVID-19 patients to predict survival

ABSTRACT: Molecular signatures to discriminate patients based on risk of severe disease and mortality from COVID-19 infection are urgently required by the global medical community. Although non-targeted methods are useful for comprehensive ‘omic coverage, targeted MS-based approaches generally provide higher precision, and improved inter-laboratory reproducibility, allowing for more realistic materialization of true biomarkers via validation studies in independent cohorts. We found a relatively small subset of molecular features that can be used to predict the chances of survival of hospitalized COVID-19 patients within the first day of admission, using a robust LC-MRM setup which is already available in many clinical laboratories.

ORGANISM(S): Homo Sapiens

SUBMITTER: Claudia Gaither

PROVIDER: PXD027959 | panorama | Thu Aug 18 00:00:00 BST 2022

REPOSITORIES: PanoramaPublic

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Publications

Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning.

Richard Vincent R VR Gaither Claudia C Popp Robert R Chaplygina Daria D Brzhozovskiy Alexander A Kononikhin Alexey A Mohammed Yassene Y Zahedi René P RP Nikolaev Evgeny N EN Borchers Christoph H CH

Molecular & cellular proteomics : MCP 20220803 10

The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We obser ...[more]

PMID: 35931319

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Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

Dataset Information

Targeted MS based multi-omic analysis of blood plasma from COVID-19 patients to predict survival

Publications

Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning.

Similar Datasets

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