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Quantitative detection of SARS-CoV-2 B.1.1.7 variant in wastewater by allele-specific RT-qPCR
Wei Lin Lee PhD; Kyle A McElroy PhD; Federica Armas PhD; Maxim Imakaev PhD; Xiaoqiong Gu PhD; Claire Duvallet PhD; Franciscus Chandra; Hongjie Chen PhD; Mats Leifels PhD; Samuel Mendola; Roisin Floyd-O Sullivan; Morgan M Powell; Shane T Wilson; Fuqing Wu; Amy Xiao; Katya Moniz PhD; Mariana Matus PhD; Newsha Ghaeli; Janelle Thompson PhD; Eric J Alm PhD.
Affiliation
  • Wei Lin Lee PhD; Singapore-MIT Alliance for Research and Technology
  • Kyle A McElroy PhD; Biobot Analytics, Inc
  • Federica Armas PhD; Singapore-MIT Alliance for Research and Technology
  • Maxim Imakaev PhD; Biobot Analytics, Inc
  • Xiaoqiong Gu PhD; Singapore-MIT Alliance for Research and Technology
  • Claire Duvallet PhD; Biobot Analytics, Inc
  • Franciscus Chandra; Singapore-MIT Alliance for Research and Technology
  • Hongjie Chen PhD; Singapore-MIT Alliance for Research and Technology
  • Mats Leifels PhD; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
  • Samuel Mendola; Biobot Analytics, Inc
  • Roisin Floyd-O Sullivan; Biobot Analytics, Inc
  • Morgan M Powell; Biobot Analytics, Inc
  • Shane T Wilson; Biobot Analytics, Inc
  • Fuqing Wu; Massachusetts Institute of Technology
  • Amy Xiao; Massachusetts Institute of Technology
  • Katya Moniz PhD; Massachusetts Institute of Technology
  • Mariana Matus PhD; Biobot Analytics, Inc
  • Newsha Ghaeli; Biobot Analytics, Inc
  • Janelle Thompson PhD; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
  • Eric J Alm PhD; Massachusetts Institute of Technology
Preprint in English | medRxiv | ID: ppmedrxiv-21254404
ABSTRACT
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.
License
cc_by_nd
Full text: Available Collection: Preprints Database: medRxiv Type of study: Diagnostic study / Prognostic study / Rct Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Diagnostic study / Prognostic study / Rct Language: English Year: 2021 Document type: Preprint
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