Journal article
Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19: a multi-omics study
eLife, Vol.11, 77943
10/14/2022
DOI: 10.7554/eLife.77943
PMID: 36239699
Abstract
Background: The great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients. Methods: Leveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial. Results: We identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2-7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset. Conclusions: Early immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models.
Details
- Title: Subtitle
- Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19: a multi-omics study
- Creators
- Zicheng Hu - University of California, San FranciscoKattria van der Ploeg - Stanford MedicineSaborni Chakraborty - Stanford MedicinePrabhu S. Arunachalam - Stanford UniversityDiego A. M. Mori - Stanford MedicineKaren B. Jacobson - Stanford MedicineHector Bonilla - Stanford MedicineJulie Parsonnet - Stanford MedicineJason R. Andrews - Stanford MedicineMarisa Holubar - Stanford UniversityAruna Subramanian - Stanford MedicineChaitan Khosla - Stanford UniversityYvonne Maldonado - Stanford MedicineHaley Hedlin - Stanford UniversityLauren de la Parte - Stanford MedicineKathleen Press - Stanford MedicineMaureen Ty - Stanford MedicineGene S. Tan - University of California San DiegoCatherine Blish - Chan Zuckerberg Biohub San FranciscoSaki Takahashi - University of California, San FranciscoIsabel Rodriguez-Barraquer - University of California, San FranciscoBryan Greenhouse - University of California, San FranciscoAtul J. Butte - University of California, San FranciscoUpinder Singh - Stanford MedicineBali Pulendran - Stanford MedicineTaia T. Wang - Stanford UniversityPrasanna Jagannathan - Stanford Medicine
- Resource Type
- Journal article
- Publication Details
- eLife, Vol.11, 77943
- DOI
- 10.7554/eLife.77943
- PMID
- 36239699
- NLM abbreviation
- Elife
- ISSN
- 2050-084X
- eISSN
- 2050-084X
- Publisher
- eLIFE SCIENCES PUBL LTD
- Number of pages
- 24
- Grant note
- U01AI150741; U19AI167903; K24AI144048; T32AI052073 / National Institute of Allergy and Infectious Diseases; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID) U01 AI150741-01S1; T32-AI052073 / National Institute of Allergy and Infectious Diseases; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID) DP1DA046089 / National Institute on Drug Abuse; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Drug Abuse (NIDA) UL1TR003142 / National Center for Advancing Translational Sciences; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Center for Advancing Translational Sciences (NCATS) Stanford University
- Language
- English
- Date published
- 10/14/2022
- Academic Unit
- Internal Medicine
- Record Identifier
- 9985244665302771
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