About: Large-scale Multi-omic Analysis of COVID-19 Severity

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Large-scale Multi-omic Analysis of COVID-19 Severity
Creator	Adam, Alejandro Balnis, Joseph Bernstein, Matthew Brademan, Dain Chieng, Hau Chopra, Amit Coon, Joshua Drake, Lisa He, Yuchen Jaitovich, Ariel Judson, Marc Linke, Vanessa Meyer, Jesse Miller, Ian Mosher, Deane Muehlbauer, Laura Overmyer, Katherine Paulson, Brett Peters-Clarke, Trenton Quan, Qiuwen Schwartz, Bradford Serrano, Lia Shishkova, Evgenia Singer, Harold Stewart, Ron Swanson, Scott Tiwari, Anupama Trujillo, Edna Zhu, Yunyun
Source	MedRxiv; Medline; PMC
abstract	We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.
has issue date	2020-07-19(xsd:dateTime)
bibo:doi	10.1101/2020.07.17.20156513
bibo:pmid	32743614
has license	cc-by-nc-nd
sha1sum (hex)	88cf77de04ae9220b7712b8ace2cfda92aa23f44
schema:url	https://doi.org/10.1101/2020.07.17.20156513
resource representing a document's title	Large-scale Multi-omic Analysis of COVID-19 Severity
has PubMed Central identifier	PMC7388490
has PubMed identifier	32743614
schema:publication	medRxiv
resource representing a document's body	covid:88cf77de04ae9220b7712b8ace2cfda92aa23f44#body_text
is schema:about of	named entity 'pathophysiological' named entity 'molecules' named entity 'proteins' named entity 'COVID-19' named entity 'COVID-19' named entity 'compendium' named entity 'blood samples' named entity 'web-based' named entity 'lipid' named entity 'blood coagulation' named entity 'involved' named entity 'positive' named entity 'insights' named entity 'enabling' named entity 'transport' named entity 'activation' named entity 'COVID-19' named entity 'omics' named entity 'COVID-19' named entity 'complement activation' named entity 'metabolite' named entity 'lipids' named entity 'machine learning' named entity 'metabolites' named entity 'COVID-19' named entity 'mass spectrometry' named entity 'lipids' named entity 'COVID-19' named entity 'phenotype' named entity 'COVID' named entity 'cGSN' named entity 'p-values' named entity 'lipids' named entity 'vascular endothelial growth factor' named entity 'disease burden' named entity 'FWHM' named entity 'GSN' named entity 'leukocyte' named entity 'correlation' named entity '5.0' named entity 'HDL' named entity '45 days' named entity 'p-values' named entity 'COVID-19' named entity 'ARDS' named entity 'plasma' named entity 'neutrophil' named entity 'protein' named entity 'kallikrein' named entity 'ion trap' named entity 'VWF' named entity 'transcriptomics' named entity 'machine learning'

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