Not everyone who catches Ebola dies of the hemorrhagic virus infection. Some people mount a robust immune defense and recover fully. Yet risk factors for susceptibility to infection and disease severity remain poorly understood.
A team at the Center for Infection and Immunity (CII) has used a specially bred population of laboratory mice that mimics human patterns of tolerance and susceptibility to the Ebola virus to identify human immune factors that predict outcomes among people infected with the disease.
In a new paper published by Cell Reports, the team describes how they identified differences in immune response among mice who recovered from Ebola and those who perished. Further analysis revealed differences in gene expression between the two groups that account for disparities in immune response. Using machine learning, the team created a model that accurately predicts human disease outcomes based on expression of a small subset of genes.
The researchers tested their model using a data set collected from Ebola patients in western Africa; the data set contains details on the presence of types of RNA associated with immune function in people's blood, as well as whether they lived or died after infection. The model predicted the patients' outcomes with 75 percent accuracy, confirming the factors identified as valid in the mouse model were also associated with outcomes among humans infected with Ebola.
Host resistance to RNA viruses requires RIG-I-like receptor (RLR) signaling through the adaptor protein, mitochondrial antiviral signaling protein (MAVS), but the role of RLR-MAVS in orchestrating anti-EBOV responses in vivo is not known.
The authors applied systems approach to MAVS−/− mice infected with either wild-type or mouse-adapted EBOV. MAVS controlled EBOV replication through the expression of IFNα, regulation of inflammatory responses in the spleen, and prevention of cell death in the liver, with macrophages implicated as a major cell type influencing host resistance.
A dominant role for RLR signaling in macrophages was confirmed following conditional MAVS deletion in LysM+ myeloid cells. These findings reveal tissue-specific MAVS-dependent transcriptional pathways associated with resistance to EBOV, and they demonstrate that EBOV adaptation to cause disease in mice involves changes in two distinct events, RLR-MAVS antagonism and suppression of RLR-independent IFN-I responses.
While the work has not yet been translated into a clinically approved test for use among human patients, the findings could guide the development of new tools to triage patients in resource-poor countries, support immune function among those at higher risk of death, and boost vaccine response among those at greatest risk. "Since the current Ebola therapeutics being tested in the DRC [Democratic Republic of Congo] are most effective when given as early as possible in infection our model could be used to develop tests with a huge impact on clinical care and patient outcomes," says the senior author.
Studies of Ebola infection and response among humans have historically been limited by the difficulty of obtaining samples during an outbreak, says co-first author. "The human data set we used to test our machine learning model is one of the only ones in existence that contains the information we needed on gene transcription among people infected with Ebola," the author explains.
Due to its size and low cost, the new mouse model removes research hurdles by making it easier for scientists to perform pre-clinical studies in maximum biocontainment, and with more robust statistical power than can be achieved with larger animal models. Using AI-driven approaches like machine learning to apply this to patients also overcomes the challenge of obtaining human samples.
The fidelity of the team's mouse model in replicating human responses to Ebola may prove helpful in understanding other viruses, such as the coronavirus outbreak that began in Wuhan, China. While much is still unknown about the new coronavirus, Rasmussen explains that the mouse model may be a useful model in guiding further understanding of the disease.
New method predicts individual response to Ebola infection
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