Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater

Sapoval, Nicolae; Liu, Yunxi; Lou, Esther; Hopkins, Loren; Ensor, Katherine B.; Schneider, Rebecca; Stadler, Lauren B.; Treangen, Todd J.

doi:10.1038/s41467-023-38184-3

Cited by 18 publications

(15 citation statements)

References 31 publications

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“…A time-dependent and accurate reference barcode for circulating VoCs are often required to identify emerging variants 34,35 . Therefore, these methods (such as Freyja and COJAC) are potentially restricted from identifying or forecasting potential novel variants in the absence of clinical sequencing data and require a "correct" barcode of circulating VoCs for accurate detection.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to enhancing statistical power, the inclusion of deletions as additional sites in the proposed pipeline (indicated by orange boxes in Supplementary Figure 2) played a significant role in the earlier detections of BA.2, BA.4, and BA.5 variants compared to Freyja. This advantage arises from the fact that no deletions were utilized in the inference process within the default settings of Freyja, a result previously discussed in another computational pipeline designed to analyze wastewater sequencing data 35 .…”

Section: Large-scale Genome Sequencing and The Development Of A Compu...mentioning

confidence: 99%

“…To overcome this shortfall, targeted hybridization and amplicon-based sequencing methods have been implemented to characterize the viral composition within a sample [24][25][26][27][28][29][30][31] . Complementing these sequencing approaches, bioinformatic pipelines have then been developed with unique computational considerations that support the identification and quantification of SARS-CoV-2 variants 18,21,[32][33][34][35][36] . For example, the COJAC pipeline 33 utilizes a variantspecific mutation pattern and counts aligned read pairs to detect the presence of a VoC in wastewater samples, even with a relatively low viral load.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the Vpipe 37 , LoFreq 38 , or iVar 39 pipeline can be used to define single nucleotide polymorphisms (SNPs) with alternative allele frequencies within the SARS-CoV-2 genome. To determine the presence and abundance of VoCs, these pooled SNP alternative allele frequencies are generally modeled as a linear combination of predefined VoCs using GISAID or UshERcurated reference barcodes 20,21,[34][35][36] . Among various linear regression models and optimization methods to estimate the abundance of variants, a pipeline called Freyja 34 is frequently employed due to its simplicity in modeling and interpretation.…”

Section: Introductionmentioning

confidence: 99%

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Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

Zhuang,

Vo,

Moshi

et al. 2024

Preprint

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Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference barcodes, we demonstrated the model accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.

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Section: Discussionmentioning

confidence: 99%

Section: Large-scale Genome Sequencing and The Development Of A Compu...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

Zhuang,

Vo,

Moshi

et al. 2024

Preprint

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“…A multitude of studies have confirmed that SARS-CoV-2 can be detected in wastewater samples [5] and that we can estimate COVID-19 case numbers from such samples [33, 40, 41, 60]. Moreover, we can estimate the relative abundances of different SARS-CoV-2 lineages (and therefore different variants) from wastewater samples using amplicon-based sequencing [4, 15, 28, 30, 47, 48, 63], further emphasizing the applicability of amplicon-based sequencing. The key step in performing amplicon-based sequencing is designing a set of primers such that the genetic material of interest can be amplified [14].…”

Section: Introductionmentioning

confidence: 99%

AmpliDiff: An Optimized Amplicon Sequencing Approach to Estimating Lineage Abundances in Viral Metagenomes

van Bemmelen,

Smyth,

Baaijens

2023

Preprint

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Metagenomic profiling algorithms commonly rely on genomic differences between lineages, strains or species to infer the relative abundances of sequences present in a sample. This observation plays an important role in the analysis of diverse microbial communities, where targeted sequencing of 16S and 18S rRNA, both well-known hypervariable genomic regions, have led to insights in microbial diversity and the discovery of novel organisms. However, the variable nature of discriminatory regions can also act as a double-edged sword, as the sought after variability can make it difficult to design primers for their amplification through PCR. Moreover, the most variable regions are not necessarily the most informative regions for the purpose of differentiation; one should focus on regions which maximize the number of lineages that can be distinguished. Here we present AmpliDiff, a computational tool that simultaneously finds such highly discriminatory genomic regions, as well as primers allowing for the amplification of these regions. We show that regions and primers found by AmpliDiff can be used to accurately estimate relative abundances of SARS-CoV-2 lineages, for example in wastewater sequencing data. We obtain mean absolute prediction errors that are comparable with using whole genome information to estimate relative abundances. Furthermore, our results show that AmpliDiff is robust against incomplete input data, and that primers designed by AmpliDiff continue to bind to genomes originating from months after the primers were selected. With AmpliDiff we provide an effective and efficient alternative to whole genome sequencing for estimating lineage abundances in viral metagenomes.

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Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain

Trigo-Tasende

Vallejo

Rumbo‐Feal

et al. 2023

Environ Sci Pollut Res

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Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 103 and 106 SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8–36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.

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Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater

Cited by 18 publications

References 31 publications

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

AmpliDiff: An Optimized Amplicon Sequencing Approach to Estimating Lineage Abundances in Viral Metagenomes

Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain

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