Yuxing Hu, Kaiyang Jiang, Siqing Xia, Weixian Zhang, Jianhua Guo, Hong Wang
{"title":"各种消毒剂方案下全规模饮用水输水管网中阿米巴群落的动态和集结机制","authors":"Yuxing Hu, Kaiyang Jiang, Siqing Xia, Weixian Zhang, Jianhua Guo, Hong Wang","doi":"10.1016/j.watres.2024.122861","DOIUrl":null,"url":null,"abstract":"Free-living amoebae (FLA) are prevalent in drinking water distribution networks (DWDNs), yet our understanding of FLA community dynamics and assembly mechanisms in DWDNs remains limited. This study characterized the occurrence patterns of amoeba communities and identified key factors influencing their assembly across four full-scale DWDNs in three Chinese cities, each utilizing different disinfectants (chlorine, chloramine, and chlorine dioxide). High-throughput sequencing of full-length 18S rRNA genes revealed highly diverse FLA communities and an array of rare FLA species in DWDNs. Unique FLA community structures and higher gene copy numbers of three amoeba taxa of concern (<em>Vermamoeba vermiformis, Acanthamoeba</em>, and <em>Naegleria fowleri</em>) were observed in the chloraminated DWDN, highlighting the distinct impact of chloramine on shaping the amoeba community. The FLA communities in DWDNs were primarily driven by deterministic processes, with disinfectant and nitrogen compounds (nitrate, nitrite, and ammonia) identified as the main influencing factors. Machine learning models revealed high SHapley Additive exPlanations (SHAP) values of dominant amoeba genera (e.g., <em>Vannella</em> and <em>Vermamoeba</em>), indicating their critical ecological roles in shaping broader bacterial and eukaryotic communities. Correlation analyses between amoeba genera and bacterial taxa revealed that 82% of the bacterial taxa exhibiting a negative correlation with amoebae were gram-negative, suggesting the preferred predation of amoebae toward gram-negative bacteria. Network analysis revealed the presence of only one to two amoebae in distinct modules, suggesting that individual amoebae might be selective in grazing. These findings provide insight into the amoeba community dynamics, assembly mechanisms and ecological roles of amoebae in drinking water, which can aid in risk assessments and mitigation strategies within DWDNs.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"186 1","pages":""},"PeriodicalIF":11.4000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Amoeba Community Dynamics and Assembly Mechanisms in Full-scale Drinking Water Distribution Networks under Various Disinfectant Regimens\",\"authors\":\"Yuxing Hu, Kaiyang Jiang, Siqing Xia, Weixian Zhang, Jianhua Guo, Hong Wang\",\"doi\":\"10.1016/j.watres.2024.122861\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Free-living amoebae (FLA) are prevalent in drinking water distribution networks (DWDNs), yet our understanding of FLA community dynamics and assembly mechanisms in DWDNs remains limited. This study characterized the occurrence patterns of amoeba communities and identified key factors influencing their assembly across four full-scale DWDNs in three Chinese cities, each utilizing different disinfectants (chlorine, chloramine, and chlorine dioxide). High-throughput sequencing of full-length 18S rRNA genes revealed highly diverse FLA communities and an array of rare FLA species in DWDNs. Unique FLA community structures and higher gene copy numbers of three amoeba taxa of concern (<em>Vermamoeba vermiformis, Acanthamoeba</em>, and <em>Naegleria fowleri</em>) were observed in the chloraminated DWDN, highlighting the distinct impact of chloramine on shaping the amoeba community. The FLA communities in DWDNs were primarily driven by deterministic processes, with disinfectant and nitrogen compounds (nitrate, nitrite, and ammonia) identified as the main influencing factors. Machine learning models revealed high SHapley Additive exPlanations (SHAP) values of dominant amoeba genera (e.g., <em>Vannella</em> and <em>Vermamoeba</em>), indicating their critical ecological roles in shaping broader bacterial and eukaryotic communities. Correlation analyses between amoeba genera and bacterial taxa revealed that 82% of the bacterial taxa exhibiting a negative correlation with amoebae were gram-negative, suggesting the preferred predation of amoebae toward gram-negative bacteria. Network analysis revealed the presence of only one to two amoebae in distinct modules, suggesting that individual amoebae might be selective in grazing. 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Amoeba Community Dynamics and Assembly Mechanisms in Full-scale Drinking Water Distribution Networks under Various Disinfectant Regimens
Free-living amoebae (FLA) are prevalent in drinking water distribution networks (DWDNs), yet our understanding of FLA community dynamics and assembly mechanisms in DWDNs remains limited. This study characterized the occurrence patterns of amoeba communities and identified key factors influencing their assembly across four full-scale DWDNs in three Chinese cities, each utilizing different disinfectants (chlorine, chloramine, and chlorine dioxide). High-throughput sequencing of full-length 18S rRNA genes revealed highly diverse FLA communities and an array of rare FLA species in DWDNs. Unique FLA community structures and higher gene copy numbers of three amoeba taxa of concern (Vermamoeba vermiformis, Acanthamoeba, and Naegleria fowleri) were observed in the chloraminated DWDN, highlighting the distinct impact of chloramine on shaping the amoeba community. The FLA communities in DWDNs were primarily driven by deterministic processes, with disinfectant and nitrogen compounds (nitrate, nitrite, and ammonia) identified as the main influencing factors. Machine learning models revealed high SHapley Additive exPlanations (SHAP) values of dominant amoeba genera (e.g., Vannella and Vermamoeba), indicating their critical ecological roles in shaping broader bacterial and eukaryotic communities. Correlation analyses between amoeba genera and bacterial taxa revealed that 82% of the bacterial taxa exhibiting a negative correlation with amoebae were gram-negative, suggesting the preferred predation of amoebae toward gram-negative bacteria. Network analysis revealed the presence of only one to two amoebae in distinct modules, suggesting that individual amoebae might be selective in grazing. These findings provide insight into the amoeba community dynamics, assembly mechanisms and ecological roles of amoebae in drinking water, which can aid in risk assessments and mitigation strategies within DWDNs.
期刊介绍:
Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include:
•Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management;
•Urban hydrology including sewer systems, stormwater management, and green infrastructure;
•Drinking water treatment and distribution;
•Potable and non-potable water reuse;
•Sanitation, public health, and risk assessment;
•Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions;
•Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment;
•Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution;
•Environmental restoration, linked to surface water, groundwater and groundwater remediation;
•Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts;
•Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle;
•Socio-economic, policy, and regulations studies.