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Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients infected with SARS-CoV-2

  1. Kelsey E Huntington
  2. Anna D Louie
  3. Chun Geun Lee
  4. Jack A Elias
  5. Eric A Ross
  6. Wafik S El-Deiry  Is a corresponding author
  1. Brown University, United States
  2. Fox Chase Cancer Center, United States
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Cite this article as: eLife 2021;10:e64958 doi: 10.7554/eLife.64958

Abstract

Although the range of immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is variable, cytokine storm is observed in a subset of symptomatic individuals. To further understand the disease pathogenesis and, consequently, to develop an additional tool for clinicians to evaluate patients for presumptive intervention we sought to compare plasma cytokine levels between a range of donor and patient samples grouped by a COVID-19 Severity Score (CSS) based on need for hospitalization and oxygen requirement. Here we utilize a mutual information algorithm that classifies the information gain for CSS prediction provided by cytokine expression levels and clinical variables. Using this methodology, we found that a small number of clinical and cytokine expression variables are predictive of presenting COVID-19 disease severity, raising questions about the mechanism by which COVID-19 creates severe illness. The variables that were the most predictive of CSS included clinical variables such as age and abnormal chest x-ray as well as cytokines such as macrophage colony-stimulating factor (M-CSF), interferon-inducible protein 10 (IP-10) and Interleukin-1 Receptor Antagonist (IL-1RA). Our results suggest that SARS-CoV-2 infection causes a plethora of changes in cytokine profiles and that particularly in severely ill patients, these changes are consistent with the presence of Macrophage Activation Syndrome and could furthermore be used as a biomarker to predict disease severity.

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Source data and source code files have been provided.

Article and author information

Author details

  1. Kelsey E Huntington

    Pathobiology Program, Brown University, Providence, United States
    Competing interests
    No competing interests declared.
  2. Anna D Louie

    Department of Surgery, Brown University, Providence, United States
    Competing interests
    No competing interests declared.
  3. Chun Geun Lee

    Department of Molecular Microbiology and Immunology, Brown University, Providence, United States
    Competing interests
    No competing interests declared.
  4. Jack A Elias

    Department of Molecular Microbiology and Immunology, Brown University, Providence, United States
    Competing interests
    No competing interests declared.
  5. Eric A Ross

    Biostatistics and Bioinformatics, Fox Chase Cancer Center, Philadelphia, United States
    Competing interests
    No competing interests declared.
  6. Wafik S El-Deiry

    Department of Pathology and Laboratory Medicine, Brown University, Providence, United States
    For correspondence
    wafik_el-deiry@brown.edu
    Competing interests
    Wafik S El-Deiry, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9577-8266

Funding

Brown University

  • Wafik S El-Deiry

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Jameel Iqbal, James J. Peters Veterans Affairs Medical Center, United States

Publication history

  1. Received: November 17, 2020
  2. Accepted: January 13, 2021
  3. Accepted Manuscript published: January 14, 2021 (version 1)
  4. Version of Record published: February 9, 2021 (version 2)

Copyright

© 2021, Huntington et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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