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Abstract

Avian reovirus (ARV) infections are an important cause of economic losses in commercial poultry worldwide, including the United States. Since 2011, increased cases of ARV-associated tenosynovitis in the progeny of vaccinated breeder flocks have raised concerns about the declining efficacy of existing vaccines. This is compounded by limitations in current virus characterization schemes, which offer little insight into key pathobiological traits such as virulence, tissue tropism, and lesion type. These limitations hinder our ability to distinguish pathogenic strains from commensals, especially given ARV’s ubiquity in poultry environments.A further challenge in ARV characterization is the frequent presence of multiple co-infecting strains in clinical samples. Accurate identification of these strains is essential for outbreak tracking and evaluating control strategies. To address these challenges, we conducted three complementary studies. First, we assessed antigenic relatedness among ARV field isolates through cross-neutralization assays involving 10 field strains and their respective antisera, representing all seven known genotypic clusters (GCs). Second, we evaluated the Oxford Nanopore Technologies MinION platform for its ability to resolve mixed ARV populations in clinical samples. Third, we sequenced the complete genomes of 12 field isolates and performed phylogenetic analyses using amino acid sequences from individual proteins and the full viral proteome. Our findings show that ARV antigenic diversity is broader than previously recognized, with all seven GCs and three sub-clusters tested in these studies representing distinct serotypes. Third-generation sequencing provided deeper resolution of ARV subpopulations compared to conventional methods. Predicted whole proteome phylogeny was largely congruent with the current σC-based classification suggesting σC evolution reflects whole virus evolution. In addition, we report the first full genome sequences of GC7 isolates, offering new insights into the genetic makeup of this understudied cluster.

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