Identification and quantification of bioactive compounds suppressing SARS-CoV-2 signals in wastewater-based epidemiology surveillance.
Identification and quantification of bioactive compounds suppressing SARS-CoV-2 signals in wastewater-based epidemiology surveillance.
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DOI：
10.1016/j.watres.2022.118824
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发表时间：
2022-08-01
期刊：
影响因子：
12.8
通讯作者：
Lin, Chung-Ho
中科院分区：
文献类型：
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作者：
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
最近基于SARS-CoV-2废水的流行病学（WBE）监测记录了污水中COVID-19患者数量与废水中病毒遗传物质水平之间的正相关性。人们已努力使用多变量回归方法，利用废水SARS-CoV-2病毒载量来预测每个下水道内的感染人群。然而，在接收工业废水的处理设施中，报告的SARS-CoV-2病毒载量的明显和持续变化使得临床预测具有挑战性。区域工业和制造设施释放的几类分子，特别是食品加工业，可以通过破坏膜的脂质双层来显著抑制废水中的SARS-CoV-2信号。因此，开发了结合代谢组学分析的系统排序过程，以识别表现出SARS-COV-2抑制的废水处理设施，并识别和量化抑制SARS-COV-2信号的化学物质。通过对污水处理厂中每个确诊病例的病毒载量进行排名，我们成功地确定了美国密苏里州显示出SARS-CoV-2抑制（显著低于5 × 1011基因拷贝/报告病例）的污水处理设施，并确定了它们的抑制率。通过非靶向的全球化学分析和对废水样品的靶向分析，确定了40种化合物作为SARS-CoV-2信号抑制剂的候选物。在这些化合物中，14种在显示SARS-CoV-2信号抑制的废水处理设施中的浓度高于未抑制的对照设施。逐步回归分析表明，4-壬基酚、棕榈油酸、油酸钠和聚乙二醇二油酸酯与SARS-CoV-2信号抑制率呈正相关。通过孵育研究进一步证实抑制活性，并测定每种生物活性化合物的抑制动力学。根据这些实验结果，废水中的生物活性分子可以显著降低SARS-CoV-2遗传标记信号的稳定性。根据这些化学抑制剂的浓度，可以开发一个校正因子，以获得更可靠和公正的监测结果，污水处理设施，接收来自类似行业的废水。
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 × 1011 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.
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影响因子：
29.4
作者：
Jiao L;Li H;Xu J;Yang M;Ma C;Li J;Zhao S;Wang H;Yang Y;Yu W;Wang J;Yang J;Long H;Gao J;Ding K;Wu D;Kuang D;Zhao Y;Liu J;Lu S;Liu H;Peng X
通讯作者：
Peng X
影响因子：
7.4
作者：
Gowda, Harsha;Ivanisevic, Julijana;Johnson, Caroline H.;Kurczy, Michael E.;Benton, H. Paul;Rinehart, Duane;Thomas Nguyen;Ray, Jayashree;Kuehl, Jennifer;Arevalo, Bernardo;Westenskow, Peter D.;Wang, Junhua;Arkin, Adam P.;Deutschbauer, Adam M.;Patti, Gary J.;Siuzdak, Gary
通讯作者：
Siuzdak, Gary
影响因子：
8.8
作者：
Jin, XL;Jiang, GB;Zhou, QF
通讯作者：
Zhou, QF
DOI：
10.1016/j.scitotenv.2021.149757
发表时间：
2021-12-20
期刊：
The Science of the total environment
影响因子：
--
作者：
Ai Y;Davis A;Jones D;Lemeshow S;Tu H;He F;Ru P;Pan X;Bohrerova Z;Lee J
通讯作者：
Lee J
DOI：
10.1093/infdis/jiu384
发表时间：
2014-11-01
期刊：
The Journal of infectious diseases
影响因子：
--
作者：
通讯作者：
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