PRRSV-2变体分类:加强监测和监督的动态命名法。

IF 3.7 2区 生物学 Q2 MICROBIOLOGY
mSphere Pub Date : 2025-02-25 Epub Date: 2025-01-23 DOI:10.1128/msphere.00709-24
Kimberly VanderWaal, Nakarin Pamornchainavakul, Mariana Kikuti, Jianqiang Zhang, Michael Zeller, Giovani Trevisan, Stephanie Rossow, Mark Schwartz, Daniel C L Linhares, Derald J Holtkamp, João Paulo Herrera da Silva, Cesar A Corzo, Julia P Baker, Tavis K Anderson, Dennis N Makau, Igor A D Paploski
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引用次数: 0

摘要

猪繁殖与呼吸综合征病毒2型(PRRSV-2)现有的遗传分类系统,如限制性片段长度多态性和亚谱系,是不可靠的遗传亲缘性指标,或缺乏足够的分辨率,无法用于兽医常规进行的流行病学监测。在这里,我们概述了美国PRRSV-2遗传变异的精细分类系统。基于bbbb25 000个美国开放阅读框5 (ORF5)序列,采用聚类算法将子谱系划分为遗传变异。通过每3个月对新序列进行分类,并在8年内系统地识别新的变异,我们证明了变异分类系统的前瞻性实施可以产生稳健的、可重复的结果,并且可以动态地适应病毒进化产生的新的遗传多样性。从2015年到2023年,共鉴定出118个变异,每年约有48个活跃变异,其中26个为常见变异(共检测50次)。变异内遗传距离平均为2.4%(最大为4.8%)。与最近相关变异的平均距离为4.9%。开发了一个定期更新的webtool (https://stemma.shinyapps.io/PRRSLoom-variants/),并公开供最终用户将新生成的序列分配给变体ID。该分类系统依赖于2015年以后的美国序列;需要进一步努力将这一系统扩展到更旧的或国际序列。最后,我们展示了变异分类如何更好地区分农场上的旧菌株和新菌株,确定新菌株引入农场/系统的可能来源,并在区域内跟踪新出现的变异。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遗传变异的反应。重要意义PRRSV-2遗传变异精细分类系统的开发和实施代表了监测养猪业中PRRSV-2发生的重大进展。该系统基于系统应用的基于国家尺度序列数据的变异识别标准,通过提供更高的分辨率和适应性来捕获新出现的变异,解决了现有分类方法的不足。该系统提供了一种稳定和可重复的方法来对PRRSV-2变异进行分类,并由兽医和诊断实验室使用的免费和定期更新的网络工具提供便利。虽然目前基于美国PRRSV-2 ORF5序列,但该系统可以扩展到包括来自其他国家的序列,为标准化的全球分类系统铺平道路。通过精确和改进的PRRSV-2遗传变异的区分,该分类系统显著提高了监测、研究和应对PRRSV-2疫情的能力,最终支持养猪业更好的管理和控制策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PRRSV-2 variant classification: a dynamic nomenclature for enhanced monitoring and surveillance.

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.

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来源期刊
mSphere
mSphere Immunology and Microbiology-Microbiology
CiteScore
8.50
自引率
2.10%
发文量
192
审稿时长
11 weeks
期刊介绍: mSphere™ is a multi-disciplinary open-access journal that will focus on rapid publication of fundamental contributions to our understanding of microbiology. Its scope will reflect the immense range of fields within the microbial sciences, creating new opportunities for researchers to share findings that are transforming our understanding of human health and disease, ecosystems, neuroscience, agriculture, energy production, climate change, evolution, biogeochemical cycling, and food and drug production. Submissions will be encouraged of all high-quality work that makes fundamental contributions to our understanding of microbiology. mSphere™ will provide streamlined decisions, while carrying on ASM''s tradition for rigorous peer review.
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