Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR
The critical need for surveillance of SARS-CoV-2 variants of concern has prompted the development of methods that can track variants in wastewater. 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, target...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/159844 |
| _version_ | 1826110086585516032 |
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| author | Lee, Wei Lin Imakaev, Maxim Armas, Federica McElroy, Kyle A. Gu, Xiaoqiong Duvallet, Claire Chandra, Franciscus Chen, Hongjie Leifels, Mats Mendola, Samuel Floyd-O'Sullivan, Róisín Powell, Morgan M. Wilson, Shane T. Berge, Karl L. J. Lim, Claire Y. J. Wu, Fuqing Xiao, Amy Moniz, Katya Ghaeli, Newsha Matus, Mariana Thompson, Janelle R. Alm, Eric J. |
| author2 | Asian School of the Environment |
| author_facet | Asian School of the Environment Lee, Wei Lin Imakaev, Maxim Armas, Federica McElroy, Kyle A. Gu, Xiaoqiong Duvallet, Claire Chandra, Franciscus Chen, Hongjie Leifels, Mats Mendola, Samuel Floyd-O'Sullivan, Róisín Powell, Morgan M. Wilson, Shane T. Berge, Karl L. J. Lim, Claire Y. J. Wu, Fuqing Xiao, Amy Moniz, Katya Ghaeli, Newsha Matus, Mariana Thompson, Janelle R. Alm, Eric J. |
| author_sort | Lee, Wei Lin |
| collection | NTU |
| description | The critical need for surveillance of SARS-CoV-2 variants of concern has prompted the development of methods that can track variants in wastewater. 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 that are 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 down to 1% in a background of mixed SARS-CoV-2. Applying our method to wastewater samples from the United States, we track the occurrence of B.1.1.7 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 matches SARS-CoV-2 levels indicated by the U.S. CDC N1 and N2 assays. This work paves the way for AS RT-qPCR as a method for rapid inexpensive surveillance of SARS-CoV-2 variants in wastewater. |
| first_indexed | 2024-10-01T02:28:43Z |
| format | Journal Article |
| id | ntu-10356/159844 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:28:43Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1598442022-07-16T20:11:52Z Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR Lee, Wei Lin Imakaev, Maxim Armas, Federica McElroy, Kyle A. Gu, Xiaoqiong Duvallet, Claire Chandra, Franciscus Chen, Hongjie Leifels, Mats Mendola, Samuel Floyd-O'Sullivan, Róisín Powell, Morgan M. Wilson, Shane T. Berge, Karl L. J. Lim, Claire Y. J. Wu, Fuqing Xiao, Amy Moniz, Katya Ghaeli, Newsha Matus, Mariana Thompson, Janelle R. Alm, Eric J. Asian School of the Environment Campus for Research Excellence and Technological Enterprise (CREATE) Singapore Centre for Environmental Life Sciences and Engineering (SCELSE) Engineering::Environmental engineering DNA Amplification The critical need for surveillance of SARS-CoV-2 variants of concern has prompted the development of methods that can track variants in wastewater. 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 that are 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 down to 1% in a background of mixed SARS-CoV-2. Applying our method to wastewater samples from the United States, we track the occurrence of B.1.1.7 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 matches SARS-CoV-2 levels indicated by the U.S. CDC N1 and N2 assays. This work paves the way for AS RT-qPCR as a method for rapid inexpensive surveillance of SARS-CoV-2 variants in wastewater. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research is supported by the National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program, Intra-CREATE Thematic Grant (Cities) Grant NRF2019-THE001-0003a to J.T. and E. J.A., and funding from the Singapore Ministry of Education and National Research Foundation through an RCE award to the Singapore Centre for Environmental Life Sciences Engineering (SCELSE) to J.T. This work was also supported by funds from the Massachusetts Consortium on Pathogen Readiness and China Evergrande Group (M.M. and E.J.A.). 2022-07-04T07:10:46Z 2022-07-04T07:10:46Z 2021 Journal Article Lee, W. L., Imakaev, M., Armas, F., McElroy, K. A., Gu, X., Duvallet, C., Chandra, F., Chen, H., Leifels, M., Mendola, S., Floyd-O'Sullivan, R., Powell, M. M., Wilson, S. T., Berge, K. L. J., Lim, C. Y. J., Wu, F., Xiao, A., Moniz, K., Ghaeli, N., ...Alm, E. J. (2021). Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR. Environmental Science and Technology Letters, 8(8), 675-682. https://dx.doi.org/10.1021/acs.estlett.1c00375 2328-8930 https://hdl.handle.net/10356/159844 10.1021/acs.estlett.1c00375 2-s2.0-85111508893 8 8 675 682 en NRF2019-THE001-0003a Environmental Science and Technology Letters © 2021 The Authors. Published by American Chemical Society. This is an open-access article distributed under the terms of the Creative Commons Attribution License. application/pdf |
| spellingShingle | Engineering::Environmental engineering DNA Amplification Lee, Wei Lin Imakaev, Maxim Armas, Federica McElroy, Kyle A. Gu, Xiaoqiong Duvallet, Claire Chandra, Franciscus Chen, Hongjie Leifels, Mats Mendola, Samuel Floyd-O'Sullivan, Róisín Powell, Morgan M. Wilson, Shane T. Berge, Karl L. J. Lim, Claire Y. J. Wu, Fuqing Xiao, Amy Moniz, Katya Ghaeli, Newsha Matus, Mariana Thompson, Janelle R. Alm, Eric J. Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR |
| title | Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR |
| title_full | Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR |
| title_fullStr | Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR |
| title_full_unstemmed | Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR |
| title_short | Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR |
| title_sort | quantitative sars cov 2 alpha variant b 1 1 7 tracking in wastewater by allele specific rt qpcr |
| topic | Engineering::Environmental engineering DNA Amplification |
| url | https://hdl.handle.net/10356/159844 |
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