dc.contributor.author | Lee, Wei Lin | |
dc.contributor.author | Imakaev, Maxim | |
dc.contributor.author | Armas, Federica | |
dc.contributor.author | McElroy, Kyle A. | |
dc.contributor.author | Gu, Xiaoqiong | |
dc.contributor.author | Duvallet, Claire | |
dc.contributor.author | Chandra, Franciscus | |
dc.contributor.author | Chen, Hongjie | |
dc.contributor.author | Leifels, Mats | |
dc.contributor.author | Mendola, Samuel | |
dc.contributor.author | Floyd-O’Sullivan, Róisín | |
dc.contributor.author | Powell, Morgan M. | |
dc.contributor.author | Wilson, Shane T. | |
dc.contributor.author | Berge, Karl L. J. | |
dc.contributor.author | Lim, Claire Y. J. | |
dc.contributor.author | Wu, Fuqing | |
dc.contributor.author | Xiao, Amy | |
dc.contributor.author | Moniz, Katya H | |
dc.contributor.author | Ghaeli, Newsha | |
dc.contributor.author | Matus, Mariana | |
dc.contributor.author | Thompson, Janelle | |
dc.contributor.author | Alm, Eric J. | |
dc.date.accessioned | 2021-08-05T20:10:25Z | |
dc.date.available | 2021-08-05T20:10:25Z | |
dc.date.issued | 2021-07 | |
dc.date.submitted | 2021-06 | |
dc.identifier.issn | 2328-8930 | |
dc.identifier.issn | 2328-8930 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/131141 | |
dc.description.abstract | 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. | en_US |
dc.publisher | American Chemical Society (ACS) | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1021/acs.estlett.1c00375 | en_US |
dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs License | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.source | ACS | en_US |
dc.title | Quantitative SARS-CoV-2 Alpha Variant B.1.1.7 Tracking in Wastewater by Allele-Specific RT-qPCR | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Lee, Wei Lin et al. "Quantitative SARS-CoV-2 Alpha Variant B.1.1.7 Tracking in Wastewater by Allele-Specific RT-qPCR." Environmental Science & Technology Letters (July 2021): dx.doi.org/10.1021/acs.estlett.1c00375. © 2021 The Authors | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biological Engineering | en_US |
dc.contributor.department | Singapore-MIT Alliance in Research and Technology (SMART) | en_US |
dc.relation.journal | Environmental Science & Technology Letters | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dspace.date.submission | 2021-08-05T12:35:51Z | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Complete | |