Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes
Summary: 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 tol...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-02-01
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| Series: | Cell Reports |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211124720300358 |
| _version_ | 1830429220882350080 |
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| author | Adam Price Atsushi Okumura Elaine Haddock Friederike Feldmann Kimberly Meade-White Pryanka Sharma Methinee Artami W. Ian Lipkin David W. Threadgill Heinz Feldmann Angela L. Rasmussen |
| author_facet | Adam Price Atsushi Okumura Elaine Haddock Friederike Feldmann Kimberly Meade-White Pryanka Sharma Methinee Artami W. Ian Lipkin David W. Threadgill Heinz Feldmann Angela L. Rasmussen |
| author_sort | Adam Price |
| collection | DOAJ |
| description | Summary: 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. : Using a panel of genetically diverse mice, Price et al. define host responses linked to Ebola virus tolerance and identify distinct gene expression programs underlying pathogenesis. The application of these profiles predicts disease outcomes in mice and human patients. Keywords: Ebola, virus, transcriptomics, pathogenesis, host response, tolerance, Collaborative Cross, classification |
| first_indexed | 2024-12-21T01:28:43Z |
| format | Article |
| id | doaj.art-e5961929006a4691adeebde0d0b076d4 |
| institution | Directory Open Access Journal |
| issn | 2211-1247 |
| language | English |
| last_indexed | 2024-12-21T01:28:43Z |
| publishDate | 2020-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports |
| spelling | doaj.art-e5961929006a4691adeebde0d0b076d42022-12-21T19:20:26ZengElsevierCell Reports2211-12472020-02-0130617021713.e6Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient OutcomesAdam Price0Atsushi Okumura1Elaine Haddock2Friederike Feldmann3Kimberly Meade-White4Pryanka Sharma5Methinee Artami6W. Ian Lipkin7David W. Threadgill8Heinz Feldmann9Angela L. Rasmussen10Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USACenter 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, USALaboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USARocky Mountain Veterinary Branch, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USALaboratory 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, USACenter for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USACenter for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USACenter for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USADepartment 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, USALaboratory of Virology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT 59840, USACenter for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA; Corresponding authorSummary: 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. : Using a panel of genetically diverse mice, Price et al. define host responses linked to Ebola virus tolerance and identify distinct gene expression programs underlying pathogenesis. The application of these profiles predicts disease outcomes in mice and human patients. Keywords: Ebola, virus, transcriptomics, pathogenesis, host response, tolerance, Collaborative Cross, classificationhttp://www.sciencedirect.com/science/article/pii/S2211124720300358 |
| spellingShingle | Adam Price Atsushi Okumura Elaine Haddock Friederike Feldmann Kimberly Meade-White Pryanka Sharma Methinee Artami W. Ian Lipkin David W. Threadgill Heinz Feldmann Angela L. Rasmussen Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes Cell Reports |
| title | Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes |
| title_full | Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes |
| title_fullStr | Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes |
| title_full_unstemmed | Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes |
| title_short | Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes |
| title_sort | transcriptional correlates of tolerance and lethality in mice predict ebola virus disease patient outcomes |
| url | http://www.sciencedirect.com/science/article/pii/S2211124720300358 |
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