Porcine reproductive and respiratory syndrome (PRRS) is a viral disease that impacts the health of pigs and costs the global swine industry a staggering $664 million per year. Controlling PRRS has proven to be a major challenge in part because of the diversity of genetic variants of the virus that are in circulation. In a recent USDA-funded study led by CVM’s Dr. Kimberly VanderWaal and Dr. Dennis Makau, researchers developed a model to predict the likely difference in pig immune response to new strains—information that can help guide immunization protocols and drive new vaccine development for more effective disease management.
Like other viruses such as influenza and SARS-CoV-2, PRRS virus evolves to evade the immune system. So while a vaccine may provide protection against some strains, new strains may not be effectively neutralized. For farmers and producers, the challenge is to understand if their herds are protected from a given strain—and how best to protect them if they’re not. Moreover, these decisions have to be made quickly to avoid the potential for an outbreak and mitigate financial losses.
The team used routinely collected data from genetic sequencing of the virus to examine how specific genetic differences between strains contributed to different immune responses in infected animals. Using these data, they developed an algorithm to estimate if a pig’s existing immunity will be adequate to neutralize a new strain. Their model was able to estimate this cross-protection with 81% accuracy—providing fast turn-around information crucial for improving PRRS management.
The model the team developed in this study, which they also presented at the North American PRRS Symposium, is for PRRS type 1—the dominant type in Europe. The team will continue to refine the model and plans to develop a model for type 2 as well, which will have relevance for producers in the US where type 2 is dominant. These tools have the potential to help the swine industry gain significant ground in the fight against this persistent foe.Source : umn.edu