Deciphering the combinatorial landscape of immunity

  1. Antonio Cappuccio
  2. Shane T Jensen
  3. Boris M Hartmann
  4. Stuart C Sealfon
  5. Vassili Soumelis  Is a corresponding author
  6. Elena Zaslavsky  Is a corresponding author
  1. Icahn School of Medicine at Mount Sinai, United States
  2. University of Pennsylvania, United States
  3. Hôpital Saint Louis, France

Abstract

From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial regulation, we present the immune Synergistic/Antagonistic Interaction Learner (iSAIL). iSAIL includes a machine learning classifier to map and interpret interactions, a curated compendium of immunological combination treatment datasets, and their global integration into a landscape of ~30,000 interactions. The landscape is mined to reveal combinatorial control of interleukins, checkpoints, and other immune modulators. The resource helps elucidate the modulation of a stimulus by interactions with other cofactors, showing that TNF has strikingly different effects depending on co-stimulators. We discover new functional synergies between TNF and IFNβ controlling dendritic cell-T cell crosstalk. Analysis of laboratory or public combination treatment studies with this user-friendly web-based resource will help resolve the complex role of interaction effects on immune processes.

Data availability

Gene expression data generated for this study have been deposited in GEO under accession code GSE134209. All other dataset accession codes analysed in this study are included in the manuscript (Table 1). All the analyses have been implemented in R. An interactive R Shiny application of iSAIL can be found at https://isail.shinyapps.io/test_app/. The site also contains downloadable code and documentation to run the software locally.

The following data sets were generated

Article and author information

Author details

  1. Antonio Cappuccio

    Neurology, Icahn School of Medicine at Mount Sinai, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Shane T Jensen

    Statistics, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Boris M Hartmann

    Neurology, Icahn School of Medicine at Mount Sinai, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5649-6776
  4. Stuart C Sealfon

    Neurology, Icahn School of Medicine at Mount Sinai, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Vassili Soumelis

    U976, Hôpital Saint Louis, Paris, France
    For correspondence
    vassili.soumelis@aphp.fr
    Competing interests
    The authors declare that no competing interests exist.
  6. Elena Zaslavsky

    Neurology, Icahn School of Medicine at Mount Sinai, New York, United States
    For correspondence
    elena.zaslavsky@mssm.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4828-7771

Funding

National Institute of Allergy and Infectious Diseases (5U19AI117873)

  • Stuart C Sealfon

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

Copyright

© 2020, Cappuccio 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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  1. Antonio Cappuccio
  2. Shane T Jensen
  3. Boris M Hartmann
  4. Stuart C Sealfon
  5. Vassili Soumelis
  6. Elena Zaslavsky
(2020)
Deciphering the combinatorial landscape of immunity
eLife 9:e62148.
https://doi.org/10.7554/eLife.62148

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https://doi.org/10.7554/eLife.62148