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猪繁殖与呼吸综合征病毒2型变异体分类:用于加强监测和监督的动态命名法。

PRRSV-2 variant classification: a dynamic nomenclature for enhanced monitoring and surveillance.

作者信息

VanderWaal Kimberly, Pamornchainavakul Nakarin, Kikuti Mariana, Zhang Jianqiang, Zeller Michael, Trevisan Giovani, Rossow Stephanie, Schwartz Mark, Linhares Daniel C L, Holtkamp Derald J, da Silva João Paulo Herrera, Corzo Cesar A, Baker Julia P, Anderson Tavis K, Makau Dennis N, Paploski Igor A D

机构信息

Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA.

Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, Iowa, USA.

出版信息

mSphere. 2025 Feb 25;10(2):e0070924. doi: 10.1128/msphere.00709-24. Epub 2025 Jan 23.

Abstract

Existing genetic classification systems for porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2), such as restriction fragment length polymorphisms and sub-lineages, are unreliable indicators of close genetic relatedness or lack sufficient resolution for epidemiological monitoring routinely conducted by veterinarians. Here, we outline a fine-scale classification system for PRRSV-2 genetic variants in the United States. Based on >25,000 U.S. open reading frame 5 (ORF5) sequences, sub-lineages were divided into genetic variants using a clustering algorithm. Through classifying new sequences every 3 months and systematically identifying new variants across 8 years, we demonstrated that prospective implementation of the variant classification system produced robust, reproducible results across time and can dynamically accommodate new genetic diversity arising from virus evolution. From 2015 to 2023, 118 variants were identified, with ~48 active variants per year, of which 26 were common (detected >50 times). Mean within-variant genetic distance was 2.4% (max: 4.8%). The mean distance to the closest related variant was 4.9%. A routinely updated webtool (https://stemma.shinyapps.io/PRRSLoom-variants/) was developed and is publicly available for end users to assign newly generated sequences to a variant ID. This classification system relies on U.S. sequences from 2015 onward; further efforts are required to extend this system to older or international sequences. Finally, we demonstrate how variant classification can better discriminate between previous and new strains on a farm, determine possible sources of new introductions into a farm/system, and track emerging variants regionally. Adoption of this classification system will enhance PRRSV-2 epidemiological monitoring, research, and communication, and improve industry responses to emerging genetic variants.IMPORTANCEThe development and implementation of a fine-scale classification system for PRRSV-2 genetic variants represent a significant advancement for monitoring PRRSV-2 occurrence in the swine industry. Based on systematically applied criteria for variant identification using national-scale sequence data, this system addresses the shortcomings of existing classification methods by offering higher resolution and adaptability to capture emerging variants. This system provides a stable and reproducible method for classifying PRRSV-2 variants, facilitated by a freely available and regularly updated webtool for use by veterinarians and diagnostic labs. Although currently based on U.S. PRRSV-2 ORF5 sequences, this system can be expanded to include sequences from other countries, paving the way for a standardized global classification system. By enabling accurate and improved discrimination of PRRSV-2 genetic variants, this classification system significantly enhances the ability to monitor, research, and respond to PRRSV-2 outbreaks, ultimately supporting better management and control strategies in the swine industry.

摘要

现有的2型猪繁殖与呼吸综合征病毒(PRRSV-2)基因分类系统,如限制性片段长度多态性和亚谱系,对于密切的基因相关性而言是不可靠的指标,或者缺乏足够的分辨率以供兽医常规进行流行病学监测。在此,我们概述了美国PRRSV-2基因变异的精细分类系统。基于超过25000条美国开放阅读框5(ORF5)序列,使用聚类算法将亚谱系划分为基因变异型。通过每3个月对新序列进行分类,并在8年时间里系统地识别新的变异型,我们证明了变异型分类系统的前瞻性实施能在不同时间产生稳健、可重复的结果,并且能够动态适应病毒进化产生的新的遗传多样性。2015年至2023年期间,共识别出118个变异型,每年约有48个活跃变异型,其中26个为常见变异型(检测次数>50次)。变异型内的平均基因距离为2.4%(最大值:4.8%)。与最接近的相关变异型的平均距离为4.9%。开发了一个定期更新的网络工具(https://stemma.shinyapps.io/PRRSLoom-variants/),可供终端用户将新生成的序列指定为一个变异型ID并公开使用。该分类系统依赖于2015年以后的美国序列;需要进一步努力将该系统扩展到更早期或国际序列。最后,我们展示了变异型分类如何能更好地区分猪场中先前的毒株和新毒株,确定猪场/系统中新引入毒株的可能来源,并在区域内追踪新出现的变异型。采用这一分类系统将加强PRRSV-2的流行病学监测、研究和交流,并改善行业对新出现的基因变异型的应对措施。

重要性

PRRSV-2基因变异精细分类系统的开发和实施代表了猪产业中监测PRRSV-2发生情况的一项重大进展。基于使用国家规模序列数据系统应用的变异型识别标准,该系统通过提供更高的分辨率和对捕获新出现变异型的适应性,解决了现有分类方法的缺点。该系统为PRRSV-2变异型分类提供了一种稳定且可重复的方法,借助一个可供兽医和诊断实验室免费使用且定期更新的网络工具得以实现。尽管目前基于美国PRRSV-2的ORF5序列,但该系统可扩展到包括其他国家的序列,为标准化的全球分类系统铺平道路。通过能够准确且更好地区分PRRSV-2基因变异型,这一分类系统显著增强了监测、研究和应对PRRSV-2疫情的能力,最终支持猪产业中更好的管理和控制策略。

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