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基于污水流行病学监测识别和定量抑制 SARS-CoV-2 信号的生物活性化合物。

Identification and quantification of bioactive compounds suppressing SARS-CoV-2 signals in wastewater-based epidemiology surveillance.

机构信息

School of Natural Resources, University of Missouri, Columbia, MO 65211, USA.

School of Natural Resources, University of Missouri, Columbia, MO 65211, USA; Center for Agroforestry, University of Missouri, Columbia, MO 65211, USA.

出版信息

Water Res. 2022 Aug 1;221:118824. doi: 10.1016/j.watres.2022.118824. Epub 2022 Jul 5.

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 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.

摘要

最近的 SARS-CoV-2 污水流行病学(WBE)监测记录表明,污水流域中 COVID-19 患者数量与污水中的病毒遗传物质水平呈正相关。已经努力使用污水中的 SARS-CoV-2 病毒载量,通过多元回归方法来预测每个污水流域内的感染人群。然而,报告表明,接收工业废水的处理设施中 SARS-CoV-2 病毒载量存在明显且持续的可变性,这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,通过破坏膜的脂质双层,可以显著抑制污水中的 SARS-CoV-2 信号。因此,开发了一种系统的排序过程,并结合代谢组学分析,以识别表现出 SARS-CoV-2 抑制作用的污水处理厂,并识别和量化抑制 SARS-COV-2 信号的化学物质。通过对污水流域中每例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表现出 SARS-CoV-2 抑制作用的污水处理厂(明显低于 5×10 基因拷贝/报告病例),并确定了它们的抑制率。通过非靶向性的全球化学特征分析和对污水样本的靶向分析,鉴定了 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,有 14 种在表现出 SARS-CoV-2 信号抑制作用的污水处理厂中的浓度高于未受抑制的对照设施。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了抑制活性,并确定了每种生物活性化合物的抑制动力学。根据这些实验的结果,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,以实现对接收类似工业废水的污水处理厂进行更可靠和无偏的监测结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5886/9253601/00beec7fe76f/ga1_lrg.jpg

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