Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes
| Autor: | Pryanka Sharma, Methinee Artami, David W. Threadgill, Angela L. Rasmussen, Atsushi Okumura, Friederike Feldmann, Elaine Haddock, W. Ian Lipkin, Adam Price, Heinz Feldmann, Kimberly Meade-White |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Disease medicine.disease_cause General Biochemistry Genetics and Molecular Biology Virus Transcriptome Pathogenesis Mice 03 medical and health sciences 0302 clinical medicine Immune system Gene expression Immune Tolerance medicine Animals Humans lcsh:QH301-705.5 Ebola virus business.industry Outbreak Hemorrhagic Fever Ebola Ebolavirus Phenotype Treatment Outcome 030104 developmental biology lcsh:Biology (General) Viral replication Immunology business 030217 neurology & neurosurgery |
| Zdroj: | Cell Reports, Vol 30, Iss 6, Pp 1702-1713.e6 (2020) |
| ISSN: | 2211-1247 |
| Popis: | Host response to infection is a major determinant of disease severity in Ebola virus disease (EVD), but gene expression programs associated with clinical outcome are poorly characterized. Using the Collaborative Cross (CC) mouse model of genetic diversity, we developed a model of differential EVD severity. CC mice develop a strain-dependent spectrum of distinct EVD phenotypes, ranging from tolerance (mild, transient disease with full recovery) to lethality (severe disease that may include hemorrhagic syndrome). We performed a screen of 10 CC lines with differential phenotypes and identified clinical, virologic, and transcriptomic features that distinguish tolerant from lethal outcomes. Tolerance is associated with tightly regulated induction of immune and inflammatory responses early following infection, as well as reduced numbers of inflammatory macrophages and increased numbers of mature antigen-presenting cells, B-1 cells, and γδ T cells, allowing for control of viral replication and subsequent recovery. Lethal disease is characterized by broad suppression of early gene expression and reduced quantitiesof lymphocytes, followed by uncontrolled inflammatory signaling leading to death. Using machine learning classification, we developed and trained a transcriptomic signature that predicted outcome in CC mice at any time point post-infection with 99% accuracy. This signature predicted outcome in a cohort of EVD patients from West Africa with 75% accuracy, demonstrating its utility as a prognostic tool to guide EVD patient treatment in future outbreaks. |
| Databáze: | OpenAIRE |
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