LANL: New Tool Can Help Better Understand High-Mutating Viruses Including COVID-19

FCI Entrepreneurial Spirit Award Winner Jessica Kubicek-Sutherland working in her lab space at LANL’s TA-46 in July. Photo by Carlos Trujillo


As we enter the third year of the COVID-19 pandemic, it’s clear that a better understanding of potentially deadly viruses is critical to public health and national security. Emerging pathogens are driving an important need for biosurveillance and diagnostic applications for rapid response. Recognizing this, Los Alamos National Laboratory scientists have developed FEVER (Fast Evaluation of Viral Emerging Risks), a computational approach that can generate flexible diagnostic assays and allow the same detection platform to be used for both broad-based biosurveillance and targeted diagnostic applications.

“Viruses mutate a lot, which makes them difficult to diagnose,” said Jessica Kubicek-Sutherland, one of the scientists on the project. “At Los Alamos, we are developing new methods to detect viruses that have very high mutation rates so that infections are accurately diagnosed, patients get the treatment they need, and we can monitor how the virus is changing and spreading all in real-time.”

The computational tool helps to design measurement assays that simultaneously detect entire classes of viruses for biosurveillance, accurately diagnosis an outbreak strain, and perform mutation typing to detect variants impacting public health.

“We applied FEVER to COVID-19 and showed that we can indeed perform both highly specific SARS-CoV-2 diagnostics in 100 clinical samples while performing mutation typing for spike variants all at the same time,” Kubicek-Sutherland said.

Paper: Fast Evaluation of Viral Emerging Risks (FEVER): A computational tool for biosurveillance, diagnostics, and mutation typing of emerging viral pathogensPLOS Global Public Health,

Zachary R. Stromberg, James Theiler, Brian T. Foley, Adán Myers y Gutiérrez, Attelia Hollander, Samantha J. Courtney, Jason Gans, Alina Deshpande, Ebany J. Martinez-Finley, Jason Mitchell, Harshini Mukundan, Karina Yusim, Jessica Z. Kubicek-Sutherland.