Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus

Xuan Liu, Emily Speranza, César Muñoz-Fontela, Sam Haldenby, Natasha Y. Rickett, Isabel Garcia-Dorival, Yongxiang Fang, Yper Hall, Elsa Gayle Zekeng, Anja Lüdtke, Dong Xia, Romy Kerber, Ralf Krumkamp, Sophie Duraffour, Daouda Sissoko, John Kenny, Nichola Rockliffe, E. Diane Williamson, Thomas R. Laws, Magassouba N'FalyDavid A. Matthews, Stephan Günther, Andrew R. Cossins, Armand Sprecher, John H. Connor*, Miles W. Carroll, Julian A. Hiscox

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)


Background: In 2014, Western Africa experienced an unanticipated explosion of Ebola virus infections. What distinguishes fatal from non-fatal outcomes remains largely unknown, yet is key to optimising personalised treatment strategies. We used transcriptome data for peripheral blood taken from infected and convalescent recovering patients to identify early stage host factors that are associated with acute illness and those that differentiate patient survival from fatality. Results: The data demonstrate that individuals who succumbed to the disease show stronger upregulation of interferon signalling and acute phase responses compared to survivors during the acute phase of infection. Particularly notable is the strong upregulation of albumin and fibrinogen genes, which suggest significant liver pathology. Cell subtype prediction using messenger RNA expression patterns indicated that NK-cell populations increase in patients who survive infection. By selecting genes whose expression properties discriminated between fatal cases and survivors, we identify a small panel of responding genes that act as strong predictors of patient outcome, independent of viral load. Conclusions: Transcriptomic analysis of the host response to pathogen infection using blood samples taken during an outbreak situation can provide multiple levels of information on both disease state and mechanisms of pathogenesis. Host biomarkers were identified that provide high predictive value under conditions where other predictors, such as viral load, are poor prognostic indicators. The data suggested that rapid analysis of the host response to infection in an outbreak situation can provide valuable information to guide an understanding of disease outcome and mechanisms of disease.

Original languageEnglish
Article number4
JournalGenome Biology
Issue number1
Publication statusPublished - 19 Jan 2017

Bibliographical note

Funding Information:
The research was funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at the University of Liverpool in partnership with Public Health England (PHE) and Liverpool School of Tropical Medicine (LSTM) to JAH and MWC and directly supported XL and NYR. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or PHE. Bioinformatics pipelines were also developed as part of a Centre of Defence Enterprise award to JAH and DAM. This work was also funded by project EVIDENT (led by SG) (Ebola virus disease: correlates of protection, determinants of outcome and clinical management) that received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 666100 and in the context of service contract IFS/2011/272-372 funded by Directorate-General for International Cooperation and Development. The EMLab is a technical partner in the WHO Emerging and Dangerous Pathogens Laboratory Network (EDPLN), and the Global Outbreak Alert and Response Network (GOARN) and the deployments in West Africa have been coordinated and supported by the GOARN Operational Support Team at WHO/HQ. The work was also funded by the Food and Drug Administration (USA) awarded to MWC and JAH, Ebola Virus Disease: correlates of protection, determinants of outcome and clinical management, number HHSF223201510104C. We acknowledge the support of RO1AI1096159 and BPS/ STP-15-051 to JHC. ES is supported by a National Science Foundation Graduate Research Fellowship under grant no. DGE-1247312. We gratefully acknowledge helpful conversations with Jay Mizgerd (BU, Pulmonary) regarding the acute phase response and Yael Steuerman (Tel Aviv University) regarding DCQ.

Publisher Copyright:
© 2017 The Author(s).


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