A modular cytokine analysis method reveals novel associations with clinical phenotypes and identifies sets of co-signaling cytokines across influenza natural infection cohorts and healthy controls

Liel Cohen, Andrew Fiore-Gartland, Adrienne G. Randolph, Angela Panoskaltsis-Mortari, Sook San Wong, Jacqui Ralston, Timothy Wood, Ruth Seeds, Q. Sue Huang, Richard J. Webby, Paul G. Thomas, Tomer Hertz

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Cytokines and chemokines are key signaling molecules of the immune system. Recent technological advances enable measurement of multiplexed cytokine profiles in biological samples. These profiles can then be used to identify potential biomarkers of a variety of clinical phenotypes. However, testing for such associations for each cytokine separately ignores the highly context-dependent covariation in cytokine secretion and decreases statistical power to detect associations due to multiple hypothesis testing. Here we present CytoMod—a novel data-driven approach for analysis of cytokine profiles that uses unsupervised clustering and regression to identify putative functional modules of co-signaling cytokines. Each module represents a biosignature of co-signaling cytokines. We applied this approach to three independent clinical cohorts of subjects naturally infected with influenza in which cytokine profiles and clinical phenotypes were collected. We found that in two out of three cohorts, cytokine modules were significantly associated with clinical phenotypes, and in many cases these associations were stronger than the associations of the individual cytokines within them. By comparing cytokine modules across datasets, we identified cytokine “cores”—specific subsets of co-expressed cytokines that clustered together across the three cohorts. Cytokine cores were also associated with clinical phenotypes. Interestingly, most of these cores were also co-expressed in a cohort of healthy controls, suggesting that in part, patterns of cytokine co-signaling may be generalizable. CytoMod can be readily applied to any cytokine profile dataset regardless of measurement technology, increases the statistical power to detect associations with clinical phenotypes and may help shed light on the complex co-signaling networks of cytokines in both health and infection.

Original languageEnglish (US)
Article number1338
JournalFrontiers in immunology
Issue numberJUN
StatePublished - 2019

Bibliographical note

Funding Information:
PICFLU was funded by the National Institutes of Health (NIH AI084011 to AR) and the Centers for Disease Control and Prevention (CDC). The SHIVERS work was supported by the Centers for Disease Control and Prevention (CDC), Department of Health and Human Services (cooperative agreement 1U01IP000480-01 between the Institute for Environmental Science and Research and the CDC’s National Center for Immunization and Respiratory Diseases Influenza Division). The FLU09 study was supported in part by the National Institute of Allergy and Infectious Diseases, the National Institutes of Health, under contract number HHSN266200700005C and ALSAC.

Publisher Copyright:
Copyright © 2019 Cohen, Fiore-Gartland, Randolph, Panoskaltsis-Mortari, Wong, Ralston, Wood, Seeds, Huang, Webby, Thomas and Hertz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


  • Biomarker
  • Chemokines
  • Cytokines
  • Influenza
  • Innate immunology


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