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BackgroundIn 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.ResultsThe 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.ConclusionsTranscriptomic 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 publication

DOI

10.1186/s13059-016-1137-3

Type

Journal article

Journal

Genome biology

Publication Date

19/01/2017

Volume

18

Addresses

National Institute of Health Research, Health Protection Research Unit In Emerging and Zoonotic Infections, Liverpool, UK.

Keywords

Killer Cells, Natural, T-Lymphocyte Subsets, Humans, Hemorrhagic Fever, Ebola, Interferons, Viral Load, Cluster Analysis, ROC Curve, Gene Expression Profiling, Computational Biology, Signal Transduction, Guinea, Ebolavirus, Host-Pathogen Interactions, Transcriptome, Coinfection, Disease Resistance, Patient Outcome Assessment