“…Figure 3 shows the five most strongly differentially expressed genes, of which four were associated with the clotting cascade genes, including fibrinogen (FG) alpha chain (FGA, +54-fold), beta chain (FGB, +28-fold) and gamma chain (FGG, +19-fold). The increased abundance of these mRNAs is again consistent with deposited transcriptomic data from EBOV-infected NHP studies [20] (Additional file 4). Fig.…”
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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1137-3) contains supplementary material, which is available to authorized users.
“…Figure 3 shows the five most strongly differentially expressed genes, of which four were associated with the clotting cascade genes, including fibrinogen (FG) alpha chain (FGA, +54-fold), beta chain (FGB, +28-fold) and gamma chain (FGG, +19-fold). The increased abundance of these mRNAs is again consistent with deposited transcriptomic data from EBOV-infected NHP studies [20] (Additional file 4). Fig.…”
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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1137-3) contains supplementary material, which is available to authorized users.
“…Enriched pathways included activation of innate immune pathways [role of pattern recognition receptors, interferon signaling, role of retinoic acid-inducible gene I (RIG-I) in antiviral response] and acute phase response signaling and pathways associated with nitric oxide and reactive oxygen species. These pathways were consistent within each group and were also consistent with host gene expression seen during viremic stages of disease after higher-dose exposure to EBOV/Kikwit (22) and other EBOV/Mak challenges (13). We identified two pathways among the top 20 that were down-regulated (peroxisome proliferatoractivated receptor signaling and LXR/RXR activation) during infection.…”
Section: Innate Immune Pathway Activation During Acute Evdsupporting
Ebola virus disease (EVD), caused by Ebola virus (EBOV), is a severe illness characterized by case fatality rates of up to 90%. The sporadic nature of outbreaks in resource-limited areas has hindered the ability to characterize the pathogenesis of EVD at all stages of infection but particularly early host responses. Pathogenesis is often studied in nonhuman primate (NHP) models of disease that replicate major aspects of human EVD. Typically, NHP models use a large infectious dose, are carried out through intramuscular or aerosol exposure, and have a fairly uniform disease course. By contrast, we report our analysis of the host response to EBOV after intranasal exposure. Twelve cynomolgus macaques were infected with 100 plaque-forming units of EBOV/Makona through intranasal exposure and presented with varying times to onset of EVD. We used RNA sequencing and a newly developed NanoString CodeSet to monitor the host response via changes in RNA transcripts over time. When individual animal gene expression data were phased based on the onset of sustained fever, the first clinical sign of severe disease, mathematical models indicated that interferon-stimulated genes appeared as early as 4 days before fever onset. This demonstrates that lethal EVD has a uniform and predictable response to infection regardless of time to onset. Furthermore, expression of a subset of genes could predict disease development before other host-based indications of infection such as fever.
“…To better understand the differences in pathogenesis caused by ZEBOV-Makona compared to the previously identified ZEBOV variant Kikwit, we compared our transcriptome results to those obtained from a recent study in which male cynomolgus macaques were challenged intramuscularly with 1000 PFU of Kikwit 15 , 16 . Protocols for library preparation, sequencing, and bioinformatics analysis were the same, making these comparisons feasible.…”
Zaire Ebolavirus (ZEBOV) continues to pose a significant threat to human health as highlighted by the recent epidemic that originated in West Africa and the ongoing outbreak in the Democratic Republic of the Congo. Although the ZEBOV variant responsible for this epidemic (Makona) shares significant genetic similarity with previously identified variants (Kikwit and Mayinga), recent reports suggest slower disease progression in nonhuman primates. However, the pathogenesis caused by the new variant is not fully understood. We present the first comprehensive approach in understanding ZEBOV-Makona pathogenesis in cynomolgus macaques by measuring changes in immune cell frequencies, plasma levels of immune mediators, and differentially expressed genes (DEGs) within whole blood (WB) and peripheral blood mononuclear cells (PBMC). Our combined approach revealed a link between: 1) increased interferon-stimulated gene expression, IFNα levels, and activated plasmacytoid dendritic cells; 2) higher proinflammatory gene expression, cytokine and chemokine levels, and non-classical monocytes; 3) gene signature of leukocyte activation and increased granulocytes; and 4) decreased expression of lymphocyte related genes and lymphopenia. In addition, our data strongly indicate delayed disease progression as well as limited overlap (~30%) in host transcriptome changes following ZEBOV-Makona infection compared to ZEBOV-Kikwit. These observations provide novel insight into the molecular mechanisms of ZEBOV-Makona pathogenesis.
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