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1.
Sci Rep ; 12(1): 889, 2022 01 18.
Article in English | MEDLINE | ID: mdl-35042868

ABSTRACT

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.


Subject(s)
COVID-19 , Gene Expression Regulation , RNA, Messenger/blood , SARS-CoV-2/metabolism , Acute Disease , COVID-19/blood , COVID-19/mortality , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Retrospective Studies
2.
Nat Commun ; 10(1): 3422, 2019 07 31.
Article in English | MEDLINE | ID: mdl-31366921

ABSTRACT

Severe influenza infection has no effective treatment available. One of the key barriers to developing host-directed therapy is a lack of reliable prognostic factors needed to guide such therapy. Here, we use a network analysis approach to identify host factors associated with severe influenza and fatal outcome. In influenza patients with moderate-to-severe diseases, we uncover a complex landscape of immunological pathways, with the main changes occurring in pathways related to circulating neutrophils. Patients with severe disease display excessive neutrophil extracellular traps formation, neutrophil-inflammation and delayed apoptosis, all of which have been associated with fatal outcome in animal models. Excessive neutrophil activation correlates with worsening oxygenation impairment and predicted fatal outcome (AUROC 0.817-0.898). These findings provide new evidence that neutrophil-dominated host response is associated with poor outcomes. Measuring neutrophil-related changes may improve risk stratification and patient selection, a critical first step in developing host-directed immune therapy.


Subject(s)
Extracellular Traps/immunology , Influenza, Human/immunology , Influenza, Human/pathology , Neutrophil Activation/immunology , Neutrophils/immunology , Cell Cycle/immunology , Female , Gene Expression/genetics , Humans , Influenza A Virus, H1N1 Subtype/immunology , Influenza A Virus, H1N1 Subtype/isolation & purification , Influenza A Virus, H3N2 Subtype/immunology , Influenza A Virus, H3N2 Subtype/isolation & purification , Influenza B virus/immunology , Influenza B virus/isolation & purification , Influenza, Human/mortality , Lung/immunology , Male , Middle Aged , Prospective Studies , Respiration, Artificial , Respiratory Insufficiency/mortality , Respiratory Insufficiency/pathology , Respiratory Insufficiency/virology
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