Hsueh Cheng-Shun, Zeller Michael, Hashish Amro, Fasina Olufemi, Piñeyro Pablo, Aminu Oluwatobiloba, El-Gazzar Mohamed, Sato Yuko
Department of Veterinary Pathology, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.
Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.
Front Vet Sci. 2025 Sep 10;12:1648247. doi: 10.3389/fvets.2025.1648247. eCollection 2025.
The US poultry industry suffers significant economic losses due to Avian Reovirus (ARV) infections, which mainly cause arthritis/tenosynovitis in turkeys and chickens. The emergence of outbreaks since 2012 highlights the urgent need for improved epidemiological tools. Given the distinct evolutionary history of each segment of the virus and limited resolution of existing typing methods for ARV based on a single gene, a novel genotyping scheme was developed utilizing a constellation-based genotyping approach to enhance source tracing and control strategies especially for ARV in turkeys. A dataset of 199 ARV sequences from turkey hosts was curated and organized based on branch distances from maximum likelihood phylogenetic trees using TreeCluster. The grouping performance was evaluated and optimized according to established criteria described in this study. The proposed methods selected the M2, S1 σC-encoding region, and L3 genomic segments due to their non-random reassortment and biological significance. The novel scheme identified 8 major genotypes and revealed clear epidemiological links between turkey breeder and meat-type farms, as well as common shared sources among different meat-type farms, suggesting both vertical and horizontal transmission pathways. Additionally, reassortment events were detected using our novel typing scheme, highlighting the complex evolutionary dynamics of ARV. By correlating genotypic patterns with epidemiological data, this study provides a foundation for improved ARV monitoring and disease management.
美国家禽业因禽呼肠孤病毒(ARV)感染而遭受重大经济损失,该病毒主要导致火鸡和鸡发生关节炎/腱鞘炎。自2012年以来疫情的出现凸显了改进流行病学工具的迫切需求。鉴于该病毒每个基因片段独特的进化历史以及基于单个基因的现有ARV分型方法分辨率有限,因此开发了一种新的基因分型方案,利用基于星群的基因分型方法来加强溯源和控制策略,特别是针对火鸡中的ARV。基于使用TreeCluster从最大似然系统发育树得出的分支距离,整理并组织了一个来自火鸡宿主的199个ARV序列的数据集。根据本研究中描述的既定标准对分组性能进行评估和优化。由于M2、编码S1 σC的区域和L3基因组片段具有非随机重配和生物学意义,因此所提出的方法选择了这些片段。该新方案识别出8种主要基因型,并揭示了火鸡种鸡场和肉鸡场之间明确的流行病学联系,以及不同肉鸡场之间的共同共享来源,表明存在垂直和水平传播途径。此外,使用我们的新分型方案检测到了重配事件,突出了ARV复杂的进化动态。通过将基因型模式与流行病学数据相关联,本研究为改进ARV监测和疾病管理奠定了基础。