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Unique COVID-19 Phenotype Emerges Through Multi-Omic Patient Analysis

NEW YORK – Researchers performed a large-scale multi-omic analysis of blood samples from COVID-19-positive and -negative individuals with diverse disease severities and outcomes, and developed a database of molecular features that are highly significant to COVID-19 status and severity.

In a study published this week in Cell Systems, the researchers said they performed RNA-seq and high-resolution mass spectrometry on 128 blood samples. They quantified the more than 17,000 resulting transcripts, proteins, metabolites, and lipids and organized them into a curated relational database, and associated them with clinical outcomes, enabling systems analysis and cross-ome correlations to molecules and patient prognoses.

Overall, they mapped 219 molecular features that were highly correlated with COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. Given their focus on plasma, the data constituted unique insight into the COVID-19 hypercoagulation phenotype, the researchers noted. They also identified sets of covarying molecules that offered pathophysiological insights and possible suggestions for therapeutic interventions.

Further, the researchers developed a web-based tool to enable interactive exploration of the database and illustrated its utility through a machine-learning approach for prediction of COVID-19 severity.

"The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype," the authors wrote. "While patients' comorbidities are powerfully associated with COVID-19 outcomes, our multiomics-based predictive model significantly improved COVID-19 severity predictions over the standardized clinical Charlson comorbidity score."

The researchers performed four individual mass spec-based assays on the plasma samples using high-resolution, high-mass accuracy MS coupled with either gas or liquid chromatography: shotgun proteomics, discovery lipidomics, discovery metabolomics, and targeted metabolomics. They also used RNA-seq to characterize the transcriptomes of leukocytes extracted from the patient samples.

A systems analysis revealed strong biomolecule associations with COVID-19 status and severity. To gain biological insight into the host's response to SARS-CoV-2 and pathways influencing its severity, the researchers integrated their biomolecule measurements with clinical outcome variables. In total, they found that 2,537 leukocyte transcripts, 146 plasma proteins, 168 plasma lipids, and 13 plasma metabolites were significantly associated with COVID-19 status. Further, they found that 511 unidentified metabolites and lipids were also significantly associated with COVID-19 diagnosis.

One ongoing challenge with COVID-19 is the broad and unpredictable range of disease severity, the researchers noted. To find biomolecules associated with severity, they performed an analysis of hospital-free days at day 45 (HFD-45) — a single outcome measure reflecting disease burden by integrating length of hospitalization stay with mortality — against abundance of each biomolecule. Using this approach, they found 6,202 transcripts, 189 plasma proteins, 218 plasma lipids, and 35 plasma small molecules that were associated with disease severity. A further analytical refinement left the investigators with 497 transcripts, 382 proteins, 140 lipids, and 60 metabolites as predictive features for the outcome HFD-45.

In total, they used significance with COVID-19 status, significance with HFD-45, and this refined feature selection to generate the list of 219 features most significantly associated with COVID-19 status and severity.

"Our observations about immunoglobulins offer a glimpse into the early humoral response to SARS-CoV-2 and the relationship between preferential variable segment use and individual response trajectories," the authors wrote. "To better understand production of protective or possibly injurious antibodies, serial studies and characterization of the properties of individual antibodies is required."

One top feature they found, for example, was pulmonary surfactant-associated protein B (SFTPB), which was notable because circulating SFTPB correlates with decreased lung function in smokers and may be a surrogate marker of lung deterioration in COVID-19 individuals. They also noted that features that were reduced in COVID-19 samples and whose abundance was lowest in the most severe cases were enriched in categories that suggested a significant change in plasma lipid regulation.

The researchers noted that follow-up studies should include a larger and broader patient and control population.

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