A team at the Center for Infection and Immunity (CII) at Columbia University Mailman School of Public Health used a specially bred population of laboratory mice that mimic 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 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 the expression of a small subset of genes.
The new mouse model makes 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.
The researchers tested the model using a data set collected from Ebola patients in western Africa; 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.
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 risk.
The new mouse model makes it easier for scientists to perform pre-clinical studies in maximum biocontainment. Using Artificial Intelligence (AI)-driven approaches to apply this to patients also overcomes the challenge of obtaining human samples.
The fidelity of the model in replicating human responses to Ebola may prove helpful in understanding outbreaks like the coronavirus.Friday Letter Submission, Publish on February 28