Section The Ebola epidemic that ravaged West Africa from 2013 to 2016 is by far the largest outbreak ever recorded. Weak healthcare infrastructure, community resistance, and a slow uncoordinated response allowed the epidemic to spin out of control. The region, however, is no stranger to dealing with viral hemorrhagic fevers. Lassa fever is caused by infection with Lassa virus and is hyper-endemic in West Africa. Lassa fever is similar to Ebola in that infection with Lassa virus can lead to a severe hemorrhagic fever. Infections with both Lassa virus and Ebola virus can lead to deaths in more than 70% of hospitalized patients. It is estimated that tens of thousands of people die from Lassa fever each year. These numbers are likely underestimates, as the healthcare infrastructure in the affected countries is extremely weak, surveillance almost non-existent, and most patients never present in the hospital. Despite the high case fatality rates of hospitalized Ebola and Lassa fever patients, however, some people appear to be able to quickly fight the viruses, whereas others die quickly from infection. Yet, what distinguishes fatal from non-fatal disease and the development of symptomatic versus asymptomatic infection, remain largely unknown and severely understudied. The recent COVID-19 global pandemic has presented identical challenges and research questions as Ebola and Lassa Fever. The goal of the Consortium for Viral Systems Biology is to uncover the virus and human factors that determine how infected individuals are able to better fight the viruses. We will achieve this goal by investigating the following three broad aims:
Aim 1. Define virus and host factors responsible for survival and non-survival in Ebola, Lassa fever and COVID-19 patients.
Aim 2. Identify factors that play roles in the development of severe long-term symptoms in survivors.
Aim 3. Define factors that determine whether human individuals develop symptomatic or asymptomatic disease. We will accomplish these aims by applying several ?omics? technologies, physiological measurements, and high-throughput experimental approaches to unique patient and survivor cohorts of COVID-19, Lassa fever and Ebola. We will develop novel predictive statistical models for identifying critical disease correlates and analyze large-scale data sets to pinpoint causal host-pathogen interactions. By elucidation the molecular networks that play critical roles in patient outcomes, this research will allow us to identify new targets for medicines and vaccines and inform personalized treatment strategies. Our study will also provide novel research frameworks and computational algorithms applicable to a wide range of other human pathogens.

Public Health Relevance

Section Lassa virus, Ebola virus and SARS-CoV-2 are three of the most devastating human pathogens, yet some individuals are better able to fight infection than others. We will perform system-wide analyses of host and virus factors that contribute to the severity of human disease and clinical outcome. By creating large integrated data sets of genetic, immunological, biological, microbial, functional, and physiological features, we will build sophisticated predictive models that will reveal the molecular interactions underlying successful clearance of Lassa, Ebola and SARS-CoV- 2 viruses.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Program--Cooperative Agreements (U19)
Project #
5U19AI135995-04
Application #
10087858
Study Section
Special Emphasis Panel (ZAI1)
Program Officer
Shabman, Reed Solomon
Project Start
2018-02-01
Project End
2023-01-31
Budget Start
2021-02-01
Budget End
2022-01-31
Support Year
4
Fiscal Year
2021
Total Cost
Indirect Cost
Name
Scripps Research Institute
Department
Type
DUNS #
781613492
City
La Jolla
State
CA
Country
United States
Zip Code
92037
Saphire, Erica Ollmann; Schendel, Sharon L; Fusco, Marnie L et al. (2018) Systematic Analysis of Monoclonal Antibodies against Ebola Virus GP Defines Features that Contribute to Protection. Cell 174:938-952.e13
Holmes, Edward C; Rambaut, Andrew; Andersen, Kristian G (2018) Pandemics: spend on surveillance, not prediction. Nature 558:180-182
Suchard, Marc A; Lemey, Philippe; Baele, Guy et al. (2018) Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol 4:vey016
Gunn, Bronwyn M; Yu, Wen-Han; Karim, Marcus M et al. (2018) A Role for Fc Function in Therapeutic Monoclonal Antibody-Mediated Protection against Ebola Virus. Cell Host Microbe 24:221-233.e5
Saphire, Erica Ollmann; Schendel, Sharon L; Gunn, Bronwyn M et al. (2018) Antibody-mediated protection against Ebola virus. Nat Immunol 19:1169-1178
Rambaut, Andrew; Drummond, Alexei J; Xie, Dong et al. (2018) Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Syst Biol 67:901-904
Siddle, Katherine J; Eromon, Philomena; Barnes, Kayla G et al. (2018) Genomic Analysis of Lassa Virus during an Increase in Cases in Nigeria in 2018. N Engl J Med 379:1745-1753