利用多重RT-dPCR追踪废水中野生型麻疹病毒——一种新的麻疹监测工具

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Véronica Roman , Frédéric Jourdain , Emilie Pele , Elise Brottet , Anne Guinard , Damien Mouly , Sandra Medragh , Christophe Cordevant , Benoît Gassilloud , Julia Dina
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引用次数: 0

摘要

建立了一种针对麻疹病毒基因组三个不同区域的多重数字RT-PCR (RT-dPCR)检测方法,用于废水监测。该方法应用于两个法国污水处理厂的入口采集的40 mL样本,这些污水处理厂位于报告了涉及基因型B3和D8的临床麻疹病例的城市地区。对2024年1月至7月期间收集的每周样本进行回顾性检测,这些样本是国家常规SARS - CoV-2废水监测网络的一部分。检测结果分别为27.3%和66.7%,病毒浓度范围分别为5.8 × 102 gc/L - 4.6 × 103 gc/L和1.8 × 103 gc/L - 2.2 × 104 gc/L。专门针对疫苗株基因组的互补RT-dPCR证实,检测到的信号来自野生型病毒,而不是来自最近的疫苗接种。这些结果证实,所开发的多重RT-dPCR方法可以可靠地检测废水中的麻疹病毒,即使在报告病例数较少的地区也是如此。因此,废水监测是在社区一级监测麻疹传播的一种有希望的补充工具,可以通过检测临床监测可能无法检测到的低水平残留循环来支持消除疾病的努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Tracking wild-type measles virus in wastewater using multiplex RT-dPCR, A novel tool for measles surveillance

Tracking wild-type measles virus in wastewater using multiplex RT-dPCR, A novel tool for measles surveillance

Tracking wild-type measles virus in wastewater using multiplex RT-dPCR, A novel tool for measles surveillance
A multiplex digital RT-PCR (RT-dPCR) assay targeting three distinct regions of the measles virus genome was developed for wastewater surveillance. This method was applied to 40 mL samples collected at the inlets of two French wastewater treatment plants located in urban areas where clinical measles cases involving genotypes B3 and D8 had been reported. Detection was performed retrospectively on weekly samples collected between January and July 2024 as part of the routine national SARS CoV-2 wastewater surveillance network. Positive results were obtained in 27.3 % and 66.7 % of samples, with viral concentrations ranging between 5.8 × 102 gc/L – 4.6 × 103 gc/L and 1.8 × 103 gc/L – 2.2 × 104 gc/L, respectively. A complementary RT-dPCR, specifically targeting vaccine strain genomes, confirmed that detected signals originated from wild-type viruses and not from recent vaccination. These results confirm that the developed multiplex RT-dPCR assay can reliably detect measles virus in wastewater, even in areas with a low number of reported cases. Wastewater surveillance therefore represents a promising complementary tool for monitoring measles circulation at the community level and could support disease elimination efforts by detecting low-level residual circulation that may go undetected by clinical surveillance.
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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