Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain.
Environmental Virology and Food Safety Lab (VISAFELab), Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, Av. Agustín Escardino 7, 46980 Valencia, Spain.
Viruses. 2023 Jul 3;15(7):1499. doi: 10.3390/v15071499.
The COVID-19 pandemic has posed a significant global threat, leading to several initiatives for its control and management. One such initiative involves wastewater-based epidemiology, which has gained attention for its potential to provide early warning of virus outbreaks and real-time information on its spread. In this study, wastewater samples from two wastewater treatment plants (WWTPs) located in the southeast of Spain (region of Murcia), namely Murcia, and Cartagena, were analyzed using RT-qPCR and high-throughput sequencing techniques to describe the evolution of SARS-CoV-2 in the South-East of Spain. Additionally, phylogenetic analysis and machine learning approaches were applied to develop a pre-screening tool for the identification of differences among the variant composition of different wastewater samples. The results confirmed that the levels of SARS-CoV-2 in these wastewater samples changed concerning the number of SARS-CoV-2 cases detected in the population, and variant occurrences were in line with clinical reported data. The sequence analyses helped to describe how the different SARS-CoV-2 variants have been replaced over time. Additionally, the phylogenetic analysis showed that samples obtained at close sampling times exhibited a higher similarity than those obtained more distantly in time. A second analysis using a machine learning approach based on the mutations found in the SARS-CoV-2 spike protein was also conducted. Hierarchical clustering (HC) was used as an efficient unsupervised approach for data analysis. Results indicated that samples obtained in October 2022 in Murcia and Cartagena were significantly different, which corresponded well with the different virus variants circulating in the two locations. The proposed methods in this study are adequate for comparing consensus sequence types of the SARS-CoV-2 sequences as a preliminary evaluation of potential changes in the variants that are circulating in a given population at a specific time point.
新型冠状病毒肺炎疫情构成了重大的全球威胁,促使人们采取了多项控制和管理疫情的措施。其中一项措施涉及基于污水的流行病学,该方法因其能够对病毒爆发提供早期预警并实时了解其传播情况而受到关注。在这项研究中,使用 RT-qPCR 和高通量测序技术分析了来自西班牙东南部两个污水处理厂(位于穆尔西亚地区的穆尔西亚和卡塔赫纳)的污水样本,以描述 SARS-CoV-2 在西班牙东南部的演变。此外,还应用了系统发生分析和机器学习方法来开发一种用于识别不同污水样本中变异组成差异的预筛选工具。研究结果证实,这些污水样本中的 SARS-CoV-2 水平与人群中检测到的 SARS-CoV-2 病例数量有关,并且变异的发生与临床报告数据一致。序列分析有助于描述不同 SARS-CoV-2 变体随时间推移是如何被取代的。此外,系统发生分析表明,在接近采样时间获得的样本比在时间上相隔较远的样本具有更高的相似性。还使用基于 SARS-CoV-2 刺突蛋白中发现的突变的机器学习方法进行了第二次分析。层次聚类(HC)被用作数据分析的有效无监督方法。结果表明,2022 年 10 月在穆尔西亚和卡塔赫纳获得的样本存在显著差异,这与这两个地点流行的不同病毒变体很好地吻合。本研究中提出的方法可用于比较 SARS-CoV-2 序列的共识序列类型,作为对特定时间点特定人群中流行的变体潜在变化的初步评估。