Identification and quantification of bioactive compounds suppressing SARS-CoV-2 signals in wastewater-based epidemiology surveillance. (1st August 2022)

• Record Type: Journal Article
• Authors: Bayati, Mohamed; Hsieh, Hsin-Yeh; Hsu, Shu-Yu; Li, Chenhui; Rogers, Elizabeth; Belenchia, Anthony; Zemmer, Sally A.; Blanc, Todd; LePage, Cindy; Klutts, Jessica; Reynolds, Melissa; Semkiw, Elizabeth; Johnson, Hwei-Yiing; Foley, Trevor; Wieberg, Chris G.; Wenzel, Jeff; Lyddon, Terri; LePique, Mary; Rushford, Clayton; Salcedo, Braxton; Young, Kara; Graham, Madalyn; Suarez, Reinier; Ford, Anarose; Lei, Zhentian; Sumner, Lloyd; Mooney, Brian P.; Wei, Xing; Greenlief, C. Michael; Johnson, Marc C.; Lin, Chung-Ho;
• Is Part Of: Water research. Volume 221(2022)
• Publication Date: 2022-08-01
• Subjects: Metabolomics -- Detergents -- Surfactants -- Wastewater surveillance -- SARS-COV-2 suppression; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2022.118824
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: Wastewater facilities that exhibit SARS-CoV-2 suppression have been identified. Average SARS-CoV-2 load per reported case was calculated to be 5 × 10 11 . Metabolomic profiling was used to identify the compounds suppressing the SARS-CoV-2. Four compounds are positively correlated with SARS-CoV-2 signal suppression rates. Abstract: Recent SARS-CoV-2 wastewater-based epidemiology (WBE) surveillance have documented a positive correlation between the number of COVID-19 patients in a sewershed and the level of viral genetic material in the wastewater. Efforts have been made to use the wastewater SARS-CoV-2 viral load to predict the infected population within each sewershed using a multivariable regression approach. However, reported clear and sustained variability in SARS-CoV-2 viral load among treatment facilities receiving industrial wastewater have made clinical prediction challenging. Several classes of molecules released by regional industries and manufacturing facilities, particularly the food processing industry, can significantly suppress the SARS-CoV-2 signals in wastewater by breaking down the lipid-bilayer of the membranes. Therefore, a systematic ranking process in conjugation with metabolomic analysis was developed to identify the wastewater treatment facilities exhibiting SARS-CoV-2 suppression and identify and quantify the chemicals suppressing the SARS-COV-2 signals. By ranking the viral load per diagnosed case among the sewersheds, we successfully identified the wastewater treatment facilities in Missouri, USA that exhibit SARS-CoV-2 suppression (significantly lower than 5 × 10 11 gene copies/reported case) and determined their suppression rates. Through both untargeted global chemical profiling and targeted analysis of wastewater samples, 40 compounds were identified as candidates of SARS-CoV-2 signal suppressors. Among these compounds, 14 had higher concentrations in wastewater treatment facilities that exhibited SARS-CoV-2 signal suppression compared to the unsuppressed control facilities. Stepwise regression analyses indicated that 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethylene glycol dioleate are positively correlated with SARS-CoV-2 signal suppression rates. Suppression activities were further confirmed by incubation studies, and the suppression kinetics for each bioactive compound were determined. According to the results of these experiments, bioactive molecules in wastewater can significantly reduce the stability of SARS-CoV-2 genetic marker signals. Based on the concentrations of these chemical suppressors, a correction factor could be developed to achieve more reliable and unbiased surveillance results for wastewater treatment facilities that receive wastewater from similar industries. Graphical Abstract: Image, graphical abstract