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. 2022 Feb 10;807(Pt 2):150722.
doi: 10.1016/j.scitotenv.2021.150722. Epub 2021 Oct 3.

Comparison of five polyethylene glycol precipitation procedures for the RT-qPCR based recovery of murine hepatitis virus, bacteriophage phi6, and pepper mild mottle virus as a surrogate for SARS-CoV-2 from wastewater

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Comparison of five polyethylene glycol precipitation procedures for the RT-qPCR based recovery of murine hepatitis virus, bacteriophage phi6, and pepper mild mottle virus as a surrogate for SARS-CoV-2 from wastewater

Shotaro Torii et al. Sci Total Environ. .

Abstract

Polyethylene glycol (PEG) precipitation is one of the conventional methods for virus concentration. This technique has been used to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater. The procedures and seeded surrogate viruses were different among implementers; thus, the reported whole process recovery efficiencies considerably varied among studies. The present study compared five PEG precipitation procedures, with different operational parameters, for the RT-qPCR-based whole process recovery efficiency of murine hepatitis virus (MHV), bacteriophage phi6, and pepper mild mottle virus (PMMoV), and molecular process recovery efficiency of murine norovirus using 34 raw wastewater samples collected in Japan. The five procedures yielded significantly different whole process recovery efficiency of MHV (0.070%-2.6%) and phi6 (0.071%-0.51%). The observed concentration of indigenous PMMoV ranged from 8.9 to 9.7 log (8.2 × 108 to 5.6 × 109) copies/L. Interestingly, PEG precipitation with 2-h incubation outperformed that with overnight incubation partially due to the difference in molecular process recovery efficiency. The recovery load of MHV exhibited a positive correlation (r = 0.70) with that of PMMoV, suggesting that PMMoV is the potential indicator of the recovery efficiency of SARS-CoV-2. In addition, we reviewed 13 published studies and found considerable variability between different studies in the whole process recovery efficiency of enveloped viruses by PEG precipitation. This was due to the differences in operational parameters and surrogate viruses as well as the differences in wastewater quality and bias in the measurement of the seeded load of surrogate viruses, resulting from the use of different analytes and RNA extraction methods. Overall, the operational parameters (e.g., incubation time and pretreatment) should be optimized for PEG precipitation. Co-quantification of PMMoV may allow for the normalization of SARS-CoV-2 RNA concentration by correcting for the differences in whole process recovery efficiency and fecal load among samples.

Keywords: Polyethylene glycol precipitation; SARS-CoV-2; Surrogates; Virus concentration; Wastewater-based epidemiology.

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Conflict of interest statement

Declaration of competing interest The authors declare no conflicts of interest associated with this manuscript.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
Flow diagram of sample processing for the comparison of five PEG precipitation procedures. PEG precipitation, RNA extraction, and RT were performed in each laboratory. At the laboratory of the University of Tokyo, the spiked wastewater was also directly subjected to RNA extraction, RT, and qPCR for the measurement of the indigenous PMMoV concentration in the unconcentrated samples. All the runs of qPCR were performed at the laboratory of the University of Tokyo.
Fig. 2
Fig. 2
Log whole process recovery efficiency (log W) of MHV and phi6 and the observed concentrations of indigenous PMMoV with each PEG precipitation procedure (n = 34). Black circles represent the arithmetic mean of log W or the observed concentration of indigenous PMMoV, the error bars represent standard deviations, and the gray circles represent the individual data-points.
Fig. 3
Fig. 3
Log molecular process recovery efficiency (log M) of MNV with each PEG precipitation procedure (n = 34). Black circles represent the arithmetic means of log M, the error bars represent the standard deviations of log M, the gray circles represent the individual data-points.
Fig. 4
Fig. 4
Correlation between the recovery loads of each virus. Spearman's rank correlation coefficient is shown in the upper left of each panel.

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