Hemorrhagic fever viruses are a major public health threat in Sub-Saharan Africa. These kinds of viruses cause symptoms ranging from non-specific fevers and body aches to severe, life-threatening bleeding, shock, and multi-organ failure. Previously discovered hemorrhagic fever viruses can cause recurrent or seasonal outbreaks, but new ones continue to emerge. In order to combat these viruses, we need to better understand the aspects of pathogenesis that lead to mortality or survival. I will present analysis of the host immune response to two hemorrhagic fever viruses, Lassa virus and Bundibugyo virus, and how the host response can be used to predict mortality in these diseases.
Lassa virus (LASV) was identified over 50 years ago, but it remains understudied and has hence been denoted a “Neglected Tropical Disease”. Clinical studies and experiments were run by our collaborators in Nigeria and Germany. In all, longitudinal blood samples were collected for over two hundred Nigerian Lassa Fever patients and concentrations of over 60 proteins analyzed. I processed the datasets, performed statistical testing, and created logistic regression models for each protein. This modeling allowed me to determine which proteins could be used as a predictive biomarker of mortality and the level of that protein that could best stratify patients who died and survived. I then compared protein levels for the best biomarkers and other markers in the same biological pathways with those of healthy and other febrile illness (non-Lassa Fever) controls. I examined the best biomarkers over time for their utility as biomarkers at later timepoints in hospitalization. Finally, I produced an application using RShiny that incorporated these and other exploratory analyses of the data, which allows users to visualize all the data we had in addition to the plots that were published.
The filovirus Bundibugyo ebolavirus (BDBV), a relative of the more well-known Ebola virus (EBOV), first caused an outbreak in people fifteen years ago. Animal models are still being developed and characterized for this virus. Our collaborators in Texas experimentally infected cynomolgus macaques with BDBV and gave them post-exposure treatment with a VSV-based vaccine. These collaborators performed RNA-Seq on longitudinal samples from the infected macaques and sent me these data for analysis. I wrote pipelines to perform RNA-Seq and differential expression analyses on over 600 samples, of which I will focus on a subset here. I found differentially expressed genes for different subsets of the data, and I examined these gene lists using gene set enrichment analysis. I then generated logistic regression models to find differentially expressed genes that could predict mortality or survival. Many of these genes could accurately predict outcome at either late or early timepoints. I then used the top genes found by logistic regression to generate random forest models that could predict mortality over the entire course of disease. / 2025-03-20T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/48456 |
| Date | 21 March 2024 |
| Creators | Strampe, Jamie |
| Contributors | Connor, John H. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Page generated in 0.0017 seconds