Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Liu, Xuan; Speranza, Emily; Muñoz-Fontela, César; Haldenby, Sam; Rickett, Natasha; Garcia-Dorival, Isabel; Fang, Yongxiang; Hall, Yper; Zekeng, Elsa-Gayle; Lüdtke, Anja; Xia, Dong; Kerber, Romy; Krumkamp, Ralf; Duraffour, Sophie; Sissoko, Daouda; Kenny, John; Rockliffe, Nichola; Williamson, E.; Laws, Thomas; N'Faly, Magassouba; Matthews, David; Günther, Stephan; Cossins, Andrew; Sprecher, Armand; Connor, John; Carroll, Miles; Hiscox, Julian
• Is Part Of: Genome biology. Volume 18:Issue 1(2017)
• Page Start: 1
• Page End: 17
• Publication Date: 2017-12
• Subjects: Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-016-1137-3
• Languages: English
• ISSNs: 1474-760X

Abstract

Abstract 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.