Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC.
Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC.
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DOI：
10.1038/s41564-022-01185-x
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发表时间：
2022-08
影响因子：
28.3
通讯作者：
中科院分区：
文献类型：
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作者：
令人关注和感兴趣的SARS-CoV-2变体的不断出现，强调了对新变体进行早期发现和流行病学监测的必要性。我们对来自瑞士三个地点的122份废水样本进行了基因组测序，以监测SARS-CoV-2的B.1.1.7 （Alpha）、B.1.351 （Beta）和P.1 （Gamma）变体在人群水平上的本地传播。我们设计了一种名为COJAC （Co-Occurrence adJusted Analysis and Calling）的生物信息学方法，该方法使用携带多个变异特异性特征突变的读对作为低频变异的稳健指标。COJAC的应用表明，在首次在临床样本中报告之前，在瑞士两个城市的废水中可观察到α变体的局部爆发长达13天。通过分析另外1339份废水样本，我们进一步证实了COJAC能够早期发现Delta变异的新变体。虽然单个废水样本的测序数据对一种变体的相对流行度的定量提供了有限的精度，但我们表明，重复和紧密网格纵向测序不仅可以对当地流行度进行稳健估计，还可以对任何变体的传播适应度优势进行稳健估计。我们得出的结论是，基因组测序和我们的计算分析可以在比临床样本少得多的样本基础上，更早地从废水样本中提供对新出现变异的患病率和适应度的种群水平估计。我们的框架在瑞士和英国的大型国家项目中经常使用。生物信息学方法COJAC能够改善对废水中SARS-CoV-2变体的出现和传播的人群水平监测。
The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK. The bioinformatics method COJAC enables improved population-level surveillance of the emergence and spread of SARS-CoV-2 variants in wastewater.
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DOI：
10.1016/j.scitotenv.2021.148002
发表时间：
2021-10-01
期刊：
The Science of the total environment
影响因子：
--
作者：
Bar-Or I;Weil M;Indenbaum V;Bucris E;Bar-Ilan D;Elul M;Levi N;Aguvaev I;Cohen Z;Shirazi R;Erster O;Sela-Brown A;Sofer D;Mor O;Mendelson E;Zuckerman NS
通讯作者：
Zuckerman NS
SARS-COV-2血统的估计可传播和影响B.1.1.7在英国。
DOI：
10.1126/science.abg3055
发表时间：
2021-04-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Davies NG;Abbott S;Barnard RC;Jarvis CI;Kucharski AJ;Munday JD;Pearson CAB;Russell TW;Tully DC;Washburne AD;Wenseleers T;Gimma A;Waites W;Wong KLM;van Zandvoort K;Silverman JD;CMMID COVID-19 Working Group;COVID-19 Genomics UK (COG-UK) Consortium;Diaz-Ordaz K;Keogh R;Eggo RM;Funk S;Jit M;Atkins KE;Edmunds WJ
通讯作者：
Edmunds WJ
影响因子：
3.1
作者：
通讯作者：
--
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
10.9
作者：
Medema, Gertjan;Heijnen, Leo;Brouwer, Anke
通讯作者：
Brouwer, Anke