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

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.

Data availability

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.

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.

Metrics

  • 1,777
    views
  • 224
    downloads
  • 24
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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
(2021)
Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients infected with SARS-CoV-2
eLife 10:e64958.
https://doi.org/10.7554/eLife.64958

Share this article

https://doi.org/10.7554/eLife.64958

Further reading

    1. Immunology and Inflammation
    Alannah Lejeune, Chunyi Zhou ... Ken Cadwell
    Research Article

    Gastrointestinal (GI) colonization by methicillin-resistant Staphylococcus aureus (MRSA) is associated with a high risk of transmission and invasive disease in vulnerable populations. The immune and microbial factors that permit GI colonization remain unknown. Male sex is correlated with enhanced Staphylococcus aureus nasal carriage, skin and soft tissue infections, and bacterial sepsis. Here, we established a mouse model of sexual dimorphism during GI colonization by MRSA. Our results show that in contrast to male mice that were susceptible to persistent colonization, female mice rapidly cleared MRSA from the GI tract following oral inoculation in a manner dependent on the gut microbiota. This colonization resistance displayed by female mice was mediated by an increase in IL-17A+ CD4+ T cells (Th17) and dependent on neutrophils. Ovariectomy of female mice increased MRSA burden, but gonadal female mice that have the Y chromosome retained enhanced Th17 responses and colonization resistance. Our study reveals a novel intersection between sex and gut microbiota underlying colonization resistance against a major widespread pathogen.

    1. Immunology and Inflammation
    2. Structural Biology and Molecular Biophysics
    Colleen A Maillie, Kiana Golden ... Marco Mravic
    Research Article

    A potent class of HIV-1 broadly neutralizing antibodies (bnAbs) targets the envelope glycoprotein’s membrane proximal exposed region (MPER) through a proposed mechanism where hypervariable loops embed into lipid bilayers and engage headgroup moieties alongside the epitope. We address the feasibility and determinant molecular features of this mechanism using multi-scale modeling. All-atom simulations of 4E10, PGZL1, 10E8, and LN01 docked onto HIV-like membranes consistently form phospholipid complexes at key complementarity-determining region loop sites, solidifying that stable and specific lipid interactions anchor bnAbs to membrane surfaces. Ancillary protein-lipid contacts reveal surprising contributions from antibody framework regions. Coarse-grained simulations effectively capture antibodies embedding into membranes. Simulations estimating protein-membrane interaction strength for PGZL1 variants along an inferred maturation pathway show bilayer affinity is evolved and correlates with neutralization potency. The modeling demonstrated here uncovers insights into lipid participation in antibodies’ recognition of membrane proteins and highlights antibody features to prioritize in vaccine design.