Journal article
Minimalistic transcriptomic signatures permit accurate early prediction of COVID-19 mortality
JCI insight, v 10(21), e195436
10 Nov 2025
PMID: 41212055
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
BACKGROUNDAccurate prognostic assays for COVID-19 represent an unmet clinical need. We sought to identify and validate early parsimonious transcriptomic signatures that accurately predict fatal outcomes.METHODSWe studied 894 patients enrolled in the prospective, multicenter Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) with peripheral blood mononuclear cells (PBMC) and nasal swabs collected within 48 hours of admission. Host gene expression was measured with RNA-Seq. We trained parsimonious prognostic classifiers incorporating host gene expression, age, and SARS-CoV-2 viral load to predict 28-day mortality in 70% of the cohort. Classifier performance was determined in the remaining 30% and externally validated in a contemporary COVID-19 cohort (n = 137) with vaccinated patients.RESULTSFatal COVID-19 was characterized by 4,189 differentially expressed genes in the peripheral blood. A COVID-specific 3-gene peripheral blood classifier (CD83, ATP1B2, DAAM2) combined with age and SARS-CoV-2 viral load achieved an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI, 0.82-0.94). A 3-gene nasal classifier (SLC5A5, CD200R1, FCER1A), in comparison, yielded an AUC of 0.74 (95% CI, 0.64-0.83). Notably, OLAH, the most strongly upregulated gene in both PBMC and nasal swab and recently implicated in severe viral infection pathogenesis, yielded AUCs of 0.86 (0.79-0.93) and 0.78 (95% CI, 0.69-0.86), respectively. Both peripheral blood classifiers demonstrated comparable performance in an independent contemporary cohort of vaccinated patients (AUCs 0.74-0.80).CONCLUSIONOur parsimonious blood- and nasal-based classifiers accurately predicted COVID-19 mortality and merit further study as accessible prognostic tools to guide triage, resource allocation, and early therapeutic interventions.FUNDINGNIH: 5R01AI135803-03, R35HL140026, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, 5T32DA018926-18, and K0826161611. National Institute of Allergy and Infectious Diseases, NIH: 3U19AI1289130, U19AI128913-04S1, and R01AI122220. National Center for Advancing Translational Sciences, NIH: UM1TR004528. The National Science Foundation: DMS2310836. The Chan Zuckerberg Biohub San Francisco.
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Details
- Title
- Minimalistic transcriptomic signatures permit accurate early prediction of COVID-19 mortality
- Creators
- Rithwik Narendra - UCLA HealthEmily C Lydon - UCSF, San Francisco, California, USAHoang Van Phan - UCSF, San Francisco, California, USANatasha Spottiswoode - UCSF, San Francisco, California, USALucile P Neyton - UCSF, San Francisco, California, USAJoann Diray-Arce - Boston Children's HospitalPatrice M Becker - National Institute of Allergy and Infectious DiseasesSeunghee Kim-Schulze - Icahn School of Medicine at Mount SinaiAnnmarie Hoch - Boston Children's HospitalHarry Pickering - Icahn School of Medicine at Mount SinaiPatrick van Zalm - Boston Children's HospitalCharles B Cairns - Drexel UniversityMatthew C Altman - University of Washington Applied Physics LaboratoryAlison D Augustine - National Institute of Allergy and Infectious DiseasesSteve Bosinger - Emory UniversityWalter Eckalbar - National Institute of Allergy and Infectious DiseasesLeying Guan - Yale School of Public Health, New Haven, Connecticut, USANaresh Doni Jayavelu - University of WashingtonSteven H Kleinstein - Yale UniversityFlorian Krammer - Icahn School of Medicine at Mount SinaiHolden T Maecker - Stanford UniversityAl Ozonoff - Boston Children's HospitalBjoern Peters - La Jolla Institute for ImmunologyNadine Rouphael - Emory UniversityRuth R Montgomery - La Jolla Institute for ImmunologyElaine Reed - UCLA HealthJoanna Schaenman - UCLA HealthHanno Steen - Boston Children's HospitalOfer Levy - UCLA HealthSidney C Haller - UCSF, San Francisco, California, USADavid Erle - UCSF, San Francisco, California, USACarolyn M Hendrickson - UCSF, San Francisco, California, USAMatthew F Krummel - UCSF, San Francisco, California, USAMichael A Matthay - UCSF, San Francisco, California, USAPrescott Woodruff - UCSF, San Francisco, California, USAElias K Haddad - Drexel UniversityCarolyn S Calfee - UCSF, San Francisco, California, USACharles R Langelier - Chan Zuckerberg Initiative (United States)IMPACC Network
- Publication Details
- JCI insight, v 10(21), e195436
- Publisher
- JCI
- Number of pages
- 18
- Grant note
- United States National Institutes of Health: 5R01AI135803-03, R35HL140026, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, 5T32DA018926-18, K0826161611 NIAID, NIH: 3U19AI1289130, U19AI128913-04S1, R01AI122220 NCATS: UM1TR004528 National Science Foundation: DMS2310836 Chan Zuckerberg Biohub San Francisco
This work is the result of NIH funding, in whole or in part, and is subject to the NIH Public Access Policy. Through acceptance of this federal funding, the NIH has been given a right to make the work publicly available in PubMed Central. center dot United States National Institutes of Health: (5R01AI135803-03, R35HL140026, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, 5T32DA018926-18, and K0826161611). center dot NIAID, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220). center dot NCATS (UM1TR004528). center dot The National Science Foundation (DMS2310836). center dot The Chan Zuckerberg Biohub San Francisco.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine; Infectious Diseases (and HIV Medicine); Emergency Medicine
- Web of Science ID
- WOS:001613716500001
- Scopus ID
- 2-s2.0-105021200951
- Other Identifier
- 991022130633504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Medicine, Research & Experimental