UMass Medical School has received approximately $10 million from the Department of Defense Peer Reviewed Medical Research Program for Anticipating Influenza Resistance Evolution (AIRe), a five-year project to develop new technologies and strategies that will anticipate and predict drug resistance and susceptibility in influenza. Understanding how and when the virus evolves in real-time will allow clinicians and scientists to design better antibodies and drugs to treat the flu.
Leading the multi-disciplinary approach that brings together experts in immunology, virology, genomics, evolutionary biology, structural biology and computational biology is Robert Finberg, MD, chair and professor of medicine.
“Our goal is personalized medicine for viruses,” said Dr. Finberg. “We believe that by looking at the sequence of a virus that we should be able to make predictions based on our understanding of its structure and certain sequences that will allow us to predict whether it will be resistant to a vaccine or an antibody. That should lead to new ways to treat viruses, in particular the flu.”
A major health concern world-side, influenza infects as many as 5 million people each year and is responsible for 250,000 deaths annually. In the cases of a pandemic, the number of deaths could potentially reach into the millions. During the influenza pandemics of 1918-1919, for instance, it is estimated that between 50 million and 150 million people died due to the virus. In the U.S., the military reported 12,000 troop infections during peak flu season in 2014.
“The great challenge to treating the flu successfully, and the reason why there are epidemics every year, is that it does things that other viruses don’t do,” said Finberg. “It changes its phenotype so rapidly and re-assorts so often that it’s difficult to develop a single antibody or drug that is effective against the virus.”
Re-assortment is when two separate strains of the virus infect the same host or person, and the eight segments carrying their genetic material mix to produce a genetically unique third strain with new mutations.
Adding to the complexity of the virus is its ability to readily interact with and jump between different hosts, such as humans, chickens and pigs. It’s believed this ability plays a large role in its ability to change so often.
In order to combat these challenges, Finberg has brought together a team of scientists to explore four different facets of the virus and assemble what they learn individually into a predicative model for the virus. The first team, led by Finberg and Jennifer Wang, MD, associate professor of medicine, will grow different strains of the virus and explore how each changes when exposed to selective pressures, such as antiviral agents and monoclonal antibodies. They will also look at the effect of re-assortment on specific drug resistance.
The second team, which includes Timothy F. Kowalik, PhD, associate professor of microbiology & physiology systems; Daniel R. Caffrey, PhD, assistant professor of medicine; and Nicholas Renzette, PhD, postdoctoral fellow, will use new approaches to sequencing the virus developed at UMMS to look at how the virus adapts to different hosts and how the host interacts with the different strains of the virus. The third team, led by Daniel Bolon, PhD, associate professor of biochemistry & molecular pharmacology, will look at how individual changes to amino acids that make up viral proteins affect its ability to invade host cells, replicate and spread.
Celia A Schiffer, PhD, professor of biochemistry & molecular pharmacology, leads the fourth team, which will investigate the structure of the viral proteins and where they bind to determine what effect this might have on its ability to respond or be resistant to certain monoclonal antibodies. This would help determine if certain monoclonal antibodies that bind to virus could be developed to prevent host cell infection.
Finally, Konstantin B. Zeldovich, PhD, assistant professor of biochemistry & molecular pharmacology, will develop the algorithms and computational tools that will bring these various data sets together into models that can predict how the virus evolves and whether it will be resistant or susceptible to certain treatments.
“How the virus does all these things is something you need a whole group of people to look at because you need to determine the genetic sequence of the virus, the structure of the viral proteins, and finally how they interact with antibodies and drugs,” said Finberg. “Being at UMass Medical School has allowed us to assemble a whole team of experts across different disciplines with access to deep sequencing and bioinformatic tools. Because of this, we’ve been able to make breakthroughs that wouldn’t have otherwise been possible.”