Next generation sequencing could improve reovirus vaccines

New sequencing technology could help researchers detect and treat avian reoviruses in broilers.

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Peshkova |
Peshkova |

New sequencing technology could help researchers detect and treat avian reoviruses in broilers.

“Avian reoviruses are RNA viruses that have ten different RNA segments on its genome. All these segments have variation capabilities. Some of them more than others, it is important to know which gene segments vary more and link them to classical or immunological outcomes,” Rodrigo Gallardo DVM, Ph.D., Dipl. ACPV, Associate Professor in Poultry Medicine in the School of Veterinary Medicine at the University of California, Davis, said.

“The speed at which the virus can mutate makes avian reoviruses very difficult to surveil and prevent. From a practical perspective, we are now mainly battling reoviruses in broilers with autogenous vaccines applied in breeders. Autogenous vaccines don’t produce the full spectrum immunity of a live vaccine. These autogenous vaccines are killed viruses that mainly generate antibodies in the hen. Those antibodies get transferred via yolk to broilers and last only up to 3 weeks. There are other elements of the immune response that need to be activated in broilers to effectively protect against RNA viruses like reoviruses.”

Avian reoviruses are costly to producers, leading to increased culling and mortality, increased condemnation rates, low rates of weight gain and poor feed efficiency. There are also reoviruses that can infect turkeys causing a range of health problems.

An improvement over traditional approaches

Traditionally, researchers have used serotyping to characterize avian reoviruses. However, this approach can have difficulty identifying virus strains that mutate quickly.

“Using next generation sequencing methods, we can sequence the whole genome and identify regions of the genome that are highly variable, our goal is to associate those variable regions with antigenicity and pathogenicity in order to streamline reovirus characterization,” explained Gallardo.

The method uses machine learning. Through algorithms a software scans full genomes every 15 nucleotides in a nonorganized way detecting differences or similarities between different sequences of these viruses. The comparison shows graphically where the variability is.

“In the case of reovirus, we want to understand how to do better disease surveillance and better virus selection for autogenous vaccine production” Gallardo said. “The other goal, which I believe will have a long-term impact, is to better understand the basics of this virus, if we know where antigenic determinants are located in the genome, we could use that knowledge to craft better vaccines.”

The project was a collaboration between the University of California, Davis, Ceva Animal Health, the California Animal Health and Food Safety (CAHFS) Laboratory System and Foster Farms. The U.S. Poultry & Egg (US POULTRY) Harold E. Ford Foundation funded the research.

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