Journal
CELL REPORTS
Volume 30, Issue 6, Pages 1702-+Publisher
CELL PRESS
DOI: 10.1016/j.celrep.2020.01.026
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Funding
- DARPA [HR0011-17-2-0009]
- NIH/NIAID [U19 AI10976]
- Intramural Research Program of the National Institute of Allergy and Infectious Diseases
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [ZIGAI001048, ZIAAI001089] Funding Source: NIH RePORTER
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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 gd 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.
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