Gene Panel Differentiates Survival Outcomes in Humans Infected with Ebola

It differs from the exome in that it includes only those RNA molecules found in a specified cell population, and usually includes the amount or concentration of each RNA molecule in addition to the molecular identities.

Investigators at the University of Liverpool used genomic techniques to identify and quantify messenger RNA (mRNA) expression in the blood of Ebola patients in Guinea who either went on to survive or die from the acute infection. These results were compared to blood samples from a separate group of survivors who had recovered from infection and were now free of the Ebola virus.

Results revealed that individuals who succumbed to the disease showed stronger upregulation of interferon signaling and acute phase responses compared to survivors during the acute phase of infection. Particularly notable was the strong upregulation of albumin and fibrinogen genes, which suggested significant liver pathology. Cell subtype prediction using messenger RNA expression patterns indicated that NK-cell populations increased in patients who survived infection.

Differences in immune cell populations predicted through analysis of gene expression patterns were validated on an independent group of Ebola virus patients using flow cytometry to directly measure the same cell types in patient blood. Machine learning was used to identify a panel of genes whose abundance could be used to predict the outcome of infection at the acute phase. This panel was validated on a separate independent group of patients with fatal or non-fatal outcome and whose viral loads were similar and was found to accurately predict outcome.

Senior author Dr. Julian Hiscox, professor of virology at the University of Liverpool, said, "Our study provides a benchmark of Ebola virus infection in humans and suggests that rapid analysis of a patient's response to infection in an outbreak could provide valuable predictive information on disease outcome."

The study was published in the January 19, 2017, online edition of the journal Genome Biology.