Could a transcriptomic signature predict the survival likelihood of Ebola patients?
The West African Ebola outbreak of 2014 was the largest epidemic of the disease in recorded history (1). Though the prevalence has declined, it remains difficult to differentiate between those diagnosed cases that may be fatal, from those that are not. In a bid to fill that gap, a team of investigators has made a discovery that may just help, by revealing that transcriptomic analysis can yield mechanisms of pathogenesis in Ebola patients (2) – information that helps produce a clearer prognosis.
“Initially, the goal was to sequence the virus in blood samples to track its evolution and to determine how this would inform both epidemiology and therapeutics,” says Julian Hiscox, lead researcher and Chair in infection and global health at the University of Liverpool. “We realized the same approaches could be used to look not only at the virus, but also at what was happening inside an infected patient.” To gain additional insight, the researchers looked into the transcriptomic profiles of 30,000–40,000 genes in infected patients and found that fatalities of the disease displayed a stronger upregulation of interferon signaling, whereas patients who survived showed an increased presence of natural killer cells. Transcriptomic analysis allowed the researchers to pinpoint a panel of various genes that triggered these changes and more, and used them as “strong predictors of patient outcome, independent of viral load” (2).
“Being able to look at the disease process at a molecular level provides important insights into the pathology surrounding the infection. For example, markers in the blood showing that there was significant liver damage occurring – but this cannot really be determined in a resource-poor setting,” says Hiscox. That’s the new technique’s biggest challenge: it can’t currently be carried out in the regions that needed it most during the outbreak.
Hiscox adds, for this approach to be useful going forwards, the results can’t be used indiscriminately. “The data has to be taken in a wider context for future outbreaks of this nature. In this scenario, the triage of patients would allow resources to be directed to those who need it most, and will also provide a framework for how to implement placebo-controlled clinical trials in an ethical framework. For instance, to give the experimental therapy to those most in need, and the placebo to those more likely to recover.”
The team continues to investigate Ebola and its role in the outcome of co-infections, while exploring more forecasting opportunities. “We are taking the predictors further and seeing whether these are specific to Ebola, or more reflective of acute febrile illness. More generally, our laboratory work has set a framework for ongoing studies of hantavirus, influenza virus, and respiratory syncytial virus,” adds Hiscox.
- Centers for Disease Control and Prevention, “2014-2016 Ebola outbreak in West Africa”, (2016). Available at: bit.ly/2k1MklY. Accessed February 8, 2017.
- X Liu et al., “Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus”, Genome Biol, 18, 4 (2017). PMID: 28100256.
My fascination with science, gaming, and writing led to my studying biology at university, while simultaneously working as an online games journalist. After university, I travelled across Europe, working on a novel and developing a game, before finding my way to Texere. As Associate Editor, I’m evolving my loves of science and writing, while continuing to pursue my passion for gaming and creative writing in a personal capacity.