Detection and surveillance of SARS-CoV-2 genomic variants in wastewater

Jahn, Katharina; Dreifuss, David; Topolsky, Ivan; Kull, Anina; Ganesanandamoorthy, Pravin; Fernández-Cassi, Xavier; Bänziger, Carola; Devaux, Alexander J.; Stachler, Elyse; Caduff, Lea; Cariti, Federica; Corzón, Álex Tuñas; Fuhrmann, Lara; Chen, Chaoran; Jablonski, Kim Philipp; Nadeau, Sarah; Feldkamp, Mirjam; Beisel, Christian; Aquino, Catharine; Stadler, Tanja; Ort, Christoph; Kohn, Tamar; Julian, Timothy R.; Beerenwinkel, Niko

doi:10.1101/2021.01.08.21249379

Cited by 87 publications

(110 citation statements)

References 44 publications

(45 reference statements)

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“…Several studies have shown that the dynamics of SARS-CoV-2 RNA in raw wastewater or sludge coincide with, or even anticipate, the dynamics of confirmed cases 7,10,11 . In addition, WBE was able to capture the introduction and spread of SARS-CoV-2 variants of concern 12 , and identify mutations that were not captured in clinical samples 13 . WBE may thus serve as a useful tool to support COVID-19 monitoring, and WBE data have already been integrated into multiple national or local COVID-19 dashboards [14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high

Fernández-Cassi

Scheidegger

Baenziger

et al. 2021

Preprint

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Wastewater-based epidemiology (WBE) has been shown to coincide with, or anticipate, confirmed COVID-19 case numbers. During periods with high test positivity rates, however, case numbers may be underreported, whereas wastewater does not suffer from this limitation. Here we investigated how the dynamics of new COVID-19 infections estimated based on wastewater monitoring or confirmed cases compare to true COVID-19 incidence dynamics. We focused on the first pandemic wave in Switzerland (February to April, 2020), when test positivity ranged up to 26%. SARS-CoV-2 RNA loads were determined 2-4 times per week in three Swiss wastewater treatment plants (Lugano, Lausanne and Zurich). Wastewater and case data were combined with a shedding load distribution and an infection-to-case confirmation delay distribution, respectively, to estimate incidence dynamics. Finally, the estimates were compared to reference incidence dynamics determined by a validated compartmental model. Incidence dynamics estimated based on wastewater data were found to better track the timing and shape of the reference infection peak compared to estimates based on confirmed cases. In contrast, case confirmations provided a better estimate of the subsequent decline in infections. Under a regime of high-test positivity rates, WBE thus provides critical information that is complementary to clinical data to monitor the pandemic trajectory.

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Section: Introductionmentioning

confidence: 99%

Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high

Fernández-Cassi

Scheidegger

Baenziger

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…However, the most common method applied to wastewater is currently next generation sequencing (NGS), either by sequencing the viral metagenome of wastewater (Crits-Christoph et al, 2020) or by amplifying and then sequencing SARS-CoV-2 genome extracted from wastewater samples (Prado et al, 2021). While these early results are promising, many report inconsistent and/or low sequencing coverage, likely due to the low input concentrations of SARS-CoV-2 in wastewater (Fontenele et al, 2021;Jahn et al, 2021;Prado et al, 2021). Therefore, NGS-based approaches for WBE will require considerable additional optimization before they can be robustly applied to a broad variety of wastewater samples and to generate reliable high-quality data that can be used to inform public health responses.…”

Section: Introductionmentioning

confidence: 99%

Quantitative detection of SARS-CoV-2 B.1.1.7 variant in wastewater by allele-specific RT-qPCR

Lee

McElroy²,

Armas

et al. 2021

Preprint

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Wastewater-based epidemiology (WBE) has emerged as a critical public health tool in tracking the SARS-CoV-2 epidemic. Monitoring SARS-CoV-2 variants of concern in wastewater has to-date relied on genomic sequencing, which lacks sensitivity necessary to detect low variant abundances in diluted and mixed wastewater samples. Here, we develop and present an open-source method based on allele specific RT-qPCR (AS RT-qPCR) that detects and quantifies the B.1.1.7 variant, targeting spike protein mutations at three independent genomic loci highly predictive of B.1.1.7 (HV69/70del, Y144del, and A570D). Our assays can reliably detect and quantify low levels of B.1.1.7 with low cross-reactivity, and at variant proportions between 0.1% and 1% in a background of mixed SARS-CoV-2. Applying our method to wastewater samples from the United States, we track B.1.1.7 occurrence over time in 19 communities. AS RT-qPCR results align with clinical trends, and summation of B.1.1.7 and wild-type sequences quantified by our assays strongly correlate with SARS-CoV-2 levels indicated by the US CDC N1/N2 assay. This work paves the path for rapid inexpensive surveillance of B.1.1.7 and other SARS-CoV-2 variants in wastewater.

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“…For further analysis, we check the presence of the signature mutations of B.1.1.7; T1001I, A1708D, I2230T, SGF 3675-3677 deletion, HV 69-70 deletion, Y144 deletion, N501Y, A570D, P681H, T716I, S982A, D1118H, Q27stop, R52I, Y73C, D3L and S235F of variant B.1.1.7 59 . To verify the presence of mutations, we generate 21-bps sequences, with 10 bps before and after the mutation, and search for their presence: e.g., mutation N501Y (A23063T) corresponds to sequence CCAACCCACT T ATGGTGTTGG.…”

Section: Variant B117mentioning

confidence: 99%

Design of Specific Primer Sets for the Detection of SARS-CoV-2 Variants of Concern B.1.1.7, B.1.351, P.1, B.1.617.2 using Artificial Intelligence

Perez-Romero¹,

Tonda

Mendoza‐Maldonado

et al. 2021

Preprint

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As the COVID-19 pandemic persists, new SARS-CoV-2 variants with potentially dangerous features have been identified by the scientific community. Variant B.1.1.7 lineage clade GR from Global Initiative on Sharing All Influenza Data (GISAID) was first detected in the UK, and it appears to possess an increased transmissibility. At the same time, South African authorities reported variant B.1.351, that shares several mutations with B.1.1.7, and might also present high transmissibility. Even more recently, a variant labeled P.1 with 17 non-synonymous mutations was detected in Brazil. In such a situation, it is paramount to rapidly develop specific molecular tests to uniquely identify, contain, and study new variants. Using a completely automated pipeline built around deep learning techniques, we design primer sets specific to variant B.1.1.7, B.1.351, and P.1, respectively. Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets in just under 16 hours for each case study. In-silico tests show that the sequences in the primer sets present high accuracy and do not appear in samples from different viruses, nor in other coronaviruses or SARS-CoV-2 variants. The presented methodology can be exploited to swiftly obtain primer sets for each independent new variant, that can later be a part of a multiplexed approach for the initial diagnosis of COVID-19 patients. Furthermore, since our approach delivers primers able to differentiate between variants, it can be used as a second step of a diagnosis in cases already positive to COVID-19, to identify individuals carrying variants with potentially threatening features.

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Detection and surveillance of SARS-CoV-2 genomic variants in wastewater

Cited by 87 publications

References 44 publications

Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high

Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high

Quantitative detection of SARS-CoV-2 B.1.1.7 variant in wastewater by allele-specific RT-qPCR

Design of Specific Primer Sets for the Detection of SARS-CoV-2 Variants of Concern B.1.1.7, B.1.351, P.1, B.1.617.2 using Artificial Intelligence

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