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Cell Rep. 2020 Feb 11;30(6):1702-1713.e6. doi: 10.1016/j.celrep.2020.01.026.

Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes.

Author information

1
Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA.
2
Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA; Laboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA.
3
Laboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA.
4
Rocky Mountain Veterinary Branch, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA.
5
Laboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA; Rocky Mountain Veterinary Branch, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USA.
6
Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, College Station, TX 77843, USA; Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA.
7
Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA. Electronic address: alr2105@cumc.columbia.edu.

Abstract

Host response to infection is a major determinant of disease severity in Ebola virus disease (EVD), but gene expression programs associated with outcome are poorly characterized. Collaborative Cross (CC) mice develop strain-dependent EVD phenotypes of differential severity, ranging from tolerance to lethality. We screen 10 CC lines and identify clinical, virologic, and transcriptomic features that distinguish tolerant from lethal outcomes. Tolerance is associated with tightly regulated induction of immune and inflammatory responses shortly following infection, as well as reduced inflammatory macrophages and increased antigen-presenting cells, B-1 cells, and γδ T cells. Lethal disease is characterized by suppressed early gene expression and reduced lymphocytes, followed by uncontrolled inflammatory signaling, leading to death. We apply machine learning to predict outcomes with 99% accuracy in mice using transcriptomic profiles. This signature predicts outcomes in a cohort of EVD patients from western Africa with 75% accuracy, demonstrating potential clinical utility.

KEYWORDS:

Collaborative Cross; Ebola; classification; host response; pathogenesis; tolerance; transcriptomics; virus

PMID:
32049004
DOI:
10.1016/j.celrep.2020.01.026
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Conflict of interest statement

Declaration of Interests The authors declare no competing interests.

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