Ebola viruses cause sever disease in humans and non-human primates. The resulting disease, Ebola virus disease (EVD), can have hemorrhagic manifestations and has mortality rates ranging from 20-90%. There is a strong need for better understanding of the disease as well as improved diagnostics and prognostics. One approach to improving diagnostic and prognostics for severe viral diseases such as EVD is to define how the host response to infection develops and produces indicators of disease and outcome.
To create a better means to understand if a patient is likely to survive or succumb to Ebola (EBOV) infection, I have sought to develop an understanding of the host response to EBOV infection in humans from the recent outbreak. I analyzed RNA-Seq samples from the 2013-2016 West Africa outbreak. I identified that the innate immune pathways are in general over activated in EVD and is stronger in patients who succumbed to disease. Furthermore, I developed a set of 10 genes that can perform as a prognostic indicator of disease independent of the viral load. This is the first demonstration that the circulating transcriptional immune response to EBOV infection can be used to predict infection outcome.
To work towards a diagnostic platform of disease, I analyzed multiple studies of time-resolved datasets in animal models of disease. I analyzed RNA-Seq and NanoString data coupled with telemetry data in EBOV-challenged macaques. The earliest and strongest changes seen in the pre-symptomatic stage of disease is the up-regulation of many innate immune genes. I used this information to develop a NanoString codeset that can act as a pre-symptomatic indicator of disease that was tested in further animal studies as a diagnostic in pre-symptomatic stages of disease.
Together, this work has identified a sets of genes that can work as a diagnostic for pre-symptomatic patients of EBOV and act as a prognostic indicators of disease. In future outbreaks, this type of information will be important to help track primary contacts of infected individuals and first responders, as well as better inform clinical management of patients. This lays the groundwork for similar analysis to be performed on other severe viral diseases such as Lassa Fever and Marburg Fever. / 2019-11-27T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/33147 |
| Date | 27 November 2018 |
| Creators | Speranza, Emily Elizabeth |
| Contributors | Connor, John H. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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