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对一个农村社区的废水进行测序,有助于识别严重急性呼吸综合征冠状病毒2(SARS-CoV-2)α变异株中广泛存在的适应性突变。

Wastewater sequencing from a rural community enables identification of widespread adaptive mutations in a SARS-CoV-2 alpha variant.

作者信息

Conway Michael J, Novay Michael P, Pusch Carson M, Ward Avery S, Abel Jackson D, Williams Maggie R, Uzarski Rebecca L, Alm Elizabeth W

机构信息

Foundational Sciences, Central Michigan University College of Medicine, Mount Pleasant, MI, 48859, USA.

Institute for Great Lakes Research, Central Michigan University, Mount Pleasant, MI, USA.

出版信息

Sci Rep. 2025 May 28;15(1):18657. doi: 10.1038/s41598-025-03771-5.

DOI:10.1038/s41598-025-03771-5
PMID:40437225
Abstract

Central Michigan University (CMU) participated in a state-wide wastewater monitoring program starting in 2021. One rural site consistently produced higher concentrations of SARS-CoV-2 genome copies. Samples from this site were sequenced retrospectively and exclusively contained a derivative of Alpha variant lineage B.1.1.7 that shed from the same site for 20-28 months. Complete reconstruction of each SARS-CoV-2 open reading frame (ORF) and alignment to an early B.1.1.7 clinical isolate identified novel mutations that were selected in non-structural (nsp1, nsp2, nsp3, nsp4, nsp5/3CLpro, nsp6, RdRp, nsp15, nsp16, ORF3a, ORF6, ORF7a, and ORF7b) and structural genes (Spike, M, and N). These were rare mutations that have not accumulated in clinical samples worldwide. Mutational analysis revealed divergence from the reference Alpha variant lineage sequence over time. We present each of the mutations on available structural models and discuss the potential role of these mutations during a chronic infection. This study further supports that small wastewater treatment plants can enhance resolution of rare events and facilitate reconstruction of viral genomes due to the relative lack of contaminating sequences and identifies mutations that may be associated with chronic infections.

摘要

中密歇根大学(CMU)自2021年起参与了一项全州范围的废水监测项目。一个农村监测点持续检测出较高浓度的严重急性呼吸综合征冠状病毒2(SARS-CoV-2)基因组拷贝。对该监测点的样本进行了回顾性测序,结果显示只含有B.1.1.7亚型阿尔法变异株的一个衍生毒株,该毒株在同一监测点持续出现了20至28个月。对每个SARS-CoV-2开放阅读框(ORF)进行完整重建,并与一个早期的B.1.1.7临床分离株进行比对,确定了在非结构基因(nsp1、nsp2、nsp3、nsp4、nsp5/3CLpro、nsp6、RNA依赖的RNA聚合酶(RdRp)、nsp15、nsp16、ORF3a、ORF6、ORF7a和ORF7b)和结构基因(刺突蛋白(Spike)、膜蛋白(M)和核衣壳蛋白(N))中出现的新突变。这些都是罕见的突变,在全球临床样本中尚未积累。突变分析显示,随着时间推移,该毒株与参考阿尔法变异株谱系序列出现了差异。我们在现有的结构模型上展示了每个突变,并讨论了这些突变在慢性感染过程中的潜在作用。这项研究进一步支持,由于污染序列相对较少,小型污水处理厂能够提高对罕见事件的分辨率,并有助于病毒基因组的重建,同时还识别出了可能与慢性感染相关的突变。

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本文引用的文献

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Wastewater-based surveillance is an efficient monitoring tool for tracking influenza A in the community.基于污水的监测是追踪社区中甲型流感的有效监测工具。
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意大利三次疫情浪潮期间空军机场军事和文职人员中新型冠状病毒变异株的特征分析
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SARS-CoV-2 wastewater monitoring in rural and small metropolitan communities in Central Michigan.密歇根州中部农村和小城市社区的 SARS-CoV-2 污水监测。
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