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Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach

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 o...

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Autores principales: Férez, Jose A., Cuevas-Ferrando, Enric, Ayala-San Nicolás, María, Simón Andreu, Pedro J., López, Román, Truchado, Pilar, Sánchez, Gloria, Allende, Ana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386001/
https://www.ncbi.nlm.nih.gov/pubmed/37515186
http://dx.doi.org/10.3390/v15071499
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author Férez, Jose A.
Cuevas-Ferrando, Enric
Ayala-San Nicolás, María
Simón Andreu, Pedro J.
López, Román
Truchado, Pilar
Sánchez, Gloria
Allende, Ana
author_facet Férez, Jose A.
Cuevas-Ferrando, Enric
Ayala-San Nicolás, María
Simón Andreu, Pedro J.
López, Román
Truchado, Pilar
Sánchez, Gloria
Allende, Ana
author_sort Férez, Jose A.
collection PubMed
description 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.
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spelling pubmed-103860012023-07-30 Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach Férez, Jose A. Cuevas-Ferrando, Enric Ayala-San Nicolás, María Simón Andreu, Pedro J. López, Román Truchado, Pilar Sánchez, Gloria Allende, Ana Viruses Article 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. MDPI 2023-07-03 /pmc/articles/PMC10386001/ /pubmed/37515186 http://dx.doi.org/10.3390/v15071499 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Férez, Jose A.
Cuevas-Ferrando, Enric
Ayala-San Nicolás, María
Simón Andreu, Pedro J.
López, Román
Truchado, Pilar
Sánchez, Gloria
Allende, Ana
Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach
title Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach
title_full Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach
title_fullStr Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach
title_full_unstemmed Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach
title_short Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach
title_sort wastewater-based epidemiology to describe the evolution of sars-cov-2 in the south-east of spain, and application of phylogenetic analysis and a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386001/
https://www.ncbi.nlm.nih.gov/pubmed/37515186
http://dx.doi.org/10.3390/v15071499
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