The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission
Open accessAbstract
As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases 1–3 . SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing 4,5 . Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.
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Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era
Minh, B. Q., Schmidt, H. A., Chernomor, O., Schrempf, D., Woodhams, M. D., von Haeseler, A., & Lanfear, R. (2020) · Molecular Biology and Evolution · doi:10.1093/molbev/msaa015 · Cited by 16,873
Minimap2: Pairwise alignment for nucleotide sequences
Li, H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty191 · Cited by 14,807
SARS-CoV-2 variants, spike mutations and immune escape
Harvey, W. T., Carabelli, A. M., Jackson, B., Gupta, R. K., Thomson, E. C., Harrison, E. M., Ludden, C., Reeve, R., Rambaut, A., COVID-19 Genomics UK (COG-UK) Consortium, et al. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00573-0 · Cited by 3,528
A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology
Rambaut, A., Holmes, E. C., O’Toole, Á., Hill, V., McCrone, J. T., Ruis, C., du Plessis, L., & Pybus, O. G. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0770-5 · Cited by 2,543
CVXPY: A Python-Embedded Modeling Language for Convex Optimization.
Diamond, S., & Boyd, S. (2016) · PubMed · Cited by 1,648
Prolonged presence of SARS-CoV-2 viral RNA in faecal samples
Wu, Y., Guo, C., Tang, L., Hong, Z., Zhou, J., Dong, X., Yin, H., Xiao, Q., Tang, Y., Qu, X., et al. (2020) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(20)30083-2 · Cited by 1,547
Xu, Y., Li, X., Zhu, B., Liang, H., Fang, C., Gong, Y., Guo, Q., Sun, X., Zhao, D., Shen, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0817-4 · Cited by 1,415
Cevik, M., Tate, M., Lloyd, O., Maraolo, A. E., Schafers, J., & Ho, A. (2021) · The Lancet Microbe · doi:10.1016/s2666-5247(20)30172-5 · Cited by 1,350
SARS-CoV-2 RNA in wastewater anticipated COVID-19 occurrence in a low prevalence area
Randazzo, W., Truchado, P., Cuevas-Ferrando, E., Simón, P., Allende, A., & Sánchez, G. (2020) · Water Research · doi:10.1016/j.watres.2020.115942 · Cited by 1,121
Cited by
Markov, P. V., Ghafari, M., Beer, M., Lythgoe, K., Simmonds, P., Stilianakis, N. I., & Katzourakis, A. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-023-00878-2 · Cited by 1,179
A multinational Delphi consensus to end the COVID-19 public health threat
Lazarus, J. V., Romero, D., Kopka, C. J., Karim, S. A., Abu-Raddad, L. J., Almeida, G., Baptista-Leite, R., Barocas, J. A., Barreto, M. L., Bar-Yam, Y., et al. (2022) · Nature · doi:10.1038/s41586-022-05398-2 · Cited by 226
Self-citationRuiz, F., Foreman, W. B., Lilly, M., Baharani, V. A., Depierreux, D. M., Chohan, V., Taylor, A. L., Guenthoer, J., Ralph, D., Matsen IV, F. A., et al. (2024) · PLOS Pathogens · doi:10.1371/journal.ppat.1012650 · Cited by 7
Metadata
- Authors
- Karthikeyan, S., Levy, J. I., De Hoff, P., Humphrey, G., Birmingham, A., Jepsen, K., Farmer, S., Tubb, H. M., Valles, T., Tribelhorn, C. E., Tsai, R., Aigner, S., Sathe, S., Moshiri, N., Henson, B., Mark, A. M., Hakim, A., Baer, N. A., Barber, T., Belda-Ferre, P., Chacón, M., Cheung, W., Cresini, E. S., Eisner, E. R., Lastrella, A. L., Lawrence, E. S., Marotz, C. A., Ngo, T. T., Ostrander, T., Plascencia, A., Salido, R. A., Seaver, P., Smoot, E. W., McDonald, D., Neuhard, R. M., Scioscia, A. L., Satterlund, A. M., Simmons, E. H., Abelman, D. B., Brenner, D., Bruner, J. C., Buckley, A., Ellison, M., Gattas, J., Gonias, S. L., Hale, M., Hawkins, F., Ikeda, L., Jhaveri, H., Johnson, T., Kellen, V., Kremer, B., Matthews, G., McLawhon, R. W., Ouillet, P., Park, D., Pradenas, A., Reed, S., Riggs, L., Sanders, A., Sollenberger, B., Song, A., White, B., Winbush, T., Aceves, C. M., Anderson, C., Gangavarapu, K., Hufbauer, E., Kurzban, E., Lee, J., Matteson, N. L., Parker, E., Perkins, S. A., Ramesh, K. S., Robles-Sikisaka, R., Schwab, M. A., Spencer, E., Wohl, S., Nicholson, L., McHardy, I. H., Dimmock, D. P., Hobbs, C. A., Bakhtar, O., Harding, A., Mendoza, A., Bolze, A., Becker, D., Cirulli, E. T., Isaksson, M., Schiabor Barrett, K. M., Washington, N. L., Malone, J. D., Schafer, A. M., Gurfield, N., Stous, S., Fielding-Miller, R., Garfein, R. S., Gaines, T., Anderson, C., Martin, N. K., Schooley, R., Austin, B., MacCannell, D. R., Kingsmore, S. F., Lee, W., Shah, S., McDonald, E., Yu, A. T., Zeller, M., Fisch, K. M., Longhurst, C., Maysent, P., Pride, D., Khosla, P. K., Laurent, L. C., Yeo, G. W., Andersen, K. G., & Knight, R.
- Venue
- Nature
- Published
- 2022-07-07
- Volume
- 609
- Issue
- 7925
- Pages
- 101-108
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4284696817
- Cited by
- 498
- References
- 31
- FWCI
- 48.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Karthikeyan, S., Levy, J. I., De Hoff, P., Humphrey, G., Birmingham, A., Jepsen, K., Farmer, S., Tubb, H. M., Valles, T., Tribelhorn, C. E., Tsai, R., Aigner, S., Sathe, S., Moshiri, N., Henson, B., Mark, A. M., Hakim, A., Baer, N. A., Barber, T., . . . Knight, R. (2022). Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission. Nature. https://doi.org/10.1038/s41586-022-05049-6
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