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Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy.

ABSTRACT: The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.

SUBMITTER: Parikh VN

PROVIDER: S-EPMC9426371 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy.

Parikh Victoria N VN Ioannidis Alexander G AG Jimenez-Morales David D Gorzynski John E JE De Jong Hannah N HN Liu Xiran X Roque Jonasel J Cepeda-Espinoza Victoria P VP Osoegawa Kazutoyo K Hughes Chris C Sutton Shirley C SC Youlton Nathan N Joshi Ruchi R Amar David D Tanigawa Yosuke Y Russo Douglas D Wong Justin J Lauzon Jessie T JT Edelson Jacob J Mas Montserrat Daniel D Kwon Yongchan Y Rubinacci Simone S Delaneau Olivier O Cappello Lorenzo L Kim Jaehee J Shoura Massa J MJ Raja Archana N AN Watson Nathaniel N Hammond Nathan N Spiteri Elizabeth E Mallempati Kalyan C KC Montero-Martín Gonzalo G Christle Jeffrey J Kim Jennifer J Kirillova Anna A Seo Kinya K Huang Yong Y Zhao Chunli C Moreno-Grau Sonia S Hershman Steven G SG Dalton Karen P KP Zhen Jimmy J Kamm Jack J Bhatt Karan D KD Isakova Alina A Morri Maurizio M Ranganath Thanmayi T Blish Catherine A CA Rogers Angela J AJ Nadeau Kari K Yang Samuel S Blomkalns Andra A O'Hara Ruth R Neff Norma F NF DeBoever Christopher C Szalma Sándor S Wheeler Matthew T MT Gates Christian M CM Farh Kyle K Schroth Gary P GP Febbo Phil P deSouza Francis F Cornejo Omar E OE Fernandez-Vina Marcelo M Kistler Amy A Palacios Julia A JA Pinsky Benjamin A BA Bustamante Carlos D CD Rivas Manuel A MA Ashley Euan A EA

Nature communications 20220830 1

The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in No ...[more]

PMID: 36042219

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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

Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy.

Publications

Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy.

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