Combining short and long read sequencing technologies to identify SARS-CoV-2 variants in wastewater

Gabrielle Jayme, Ju-Ling Liu, Jose Hector Galvez, Sarah Julia Reiling, Sukriye Celikkol Aydin, Arnaud N'Guessan, Sally Lee, Shu-Huang Chen, Alexandra Tsitouras, Fernando Sanchez-Quete, Thomas Maere, Eyerusalem Goitom, Mounia Hachad, Elisabeth Mercier, Stephanie Katharine Loeb, Peter Vanrolleghem, Sarah Dorner, Robert Delatolla, B. Jesse Shapiro, Dominic Frigon, Jiannis Ragoussis, Terrance P. Snutch
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Abstract

During the COVID-19 pandemic, the monitoring of SARS-COV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare two next-generation sequencing technologies, Illumina and Nanopore, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities. Our study used two analytical approaches to identify main variants in the samples: the presence of signature and marker mutations, and the co-occurrence of signature mutations within the same amplicon. We observed that each sequencing method detected certain variants at different frequencies as each method preferentially detects mutations of distinct variants. Illumina sequencing detected more mutations with a predominant lineage that is in low abundance across the population or unknown for that time period, while Nanopore sequencing had a higher detection rate of mutations that are predominantly found in the high abundance B.1.1.7 (Alpha) lineage as well as a higher sequencing rate of co-occurring mutations in the same amplicon. We present a workflow that integrates short read and long read sequencing to improve the detection of SARS-CoV-2 variant lineages in mixed population samples, such as wastewater.
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结合长短读测序技术识别废水中的 SARS-CoV-2 变异体
在 COVID-19 大流行期间,通过监测废水中的 SARS-COV-2 RNA 来跟踪变异系的演变和出现情况,并评估社区的感染水平,从而为适当的公共卫生应对措施提供信息,而无需完全依赖临床检测。随着越来越多的亚系被发现,在混合人群样本中识别不同变异株的难度也随之增加,尤其是那些没有已知亚系的变异株。在这里,我们使用来自魁北克省和安大略省不同城市的 248 份废水样本,比较了 Illumina 和 Nanopore 两种下一代测序技术,以确定它们在检测不同丰度变体方面的功效。我们的研究采用了两种分析方法来识别样本中的主要变异:特征变异和标记变异的存在,以及同一扩增片段中特征变异的共同出现。我们发现,每种测序方法检测到某些变异的频率不同,因为每种方法都优先检测不同变异的突变。Illumina测序法检测到的变异更多的是在整个人群中丰度较低或在该时间段内未知的主要品系,而Nanopore测序法对主要存在于高丰度B.1.1.7(Alpha)品系中的变异的检测率更高,同时对同一扩增片段中的共现变异的测序率也更高。我们介绍了一种整合了短读和长读测序的工作流程,以改进对废水等混合人群样本中 SARS-CoV-2 变异系的检测。
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