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Escherichia coli (E. coli) cells are present in fecal materials that can be the main source for disease‐causing agents in water. As a result, E. coli is recommended as a water quality indicator. We have developed an innovative platform to detect E. coli for monitoring water quality on-site by integrating paper-based sample preparation with nucleic acid isothermal amplification. The platform carries out bacterial lysis and DNA enrichment onto a paper pad through ball-based valves for fluid control, with no need of laboratory equipment, followed by loop-mediated isothermal amplification (LAMP) in a battery-operated coffee mug, and colorimetric detection. We have used the platform to detect E. coli in environmental water samples in about 1 h, with a limit of quantitation of 0.2 CFU/mL, and 3 copies per reaction. The platform was confirmed for detecting multiple E. coli strains, and for water samples of different salt concentrations. We validated the functions of the platform by analyzing recreational water samples collected near the Atlantic Ocean that contain different concentrations of salt and bacteria.
Escherichia coli (E. coli) cells are present in fecal materials that can be the main source for disease‐causing agents in water. As a result, E. coli is recommended as a water quality indicator. We have developed an innovative platform to detect E. coli for monitoring water quality on-site by integrating paper-based sample preparation with nucleic acid isothermal amplification. The platform carries out bacterial lysis and DNA enrichment onto a paper pad through ball-based valves for fluid control, with no need of laboratory equipment, followed by loop-mediated isothermal amplification (LAMP) in a battery-operated coffee mug, and colorimetric detection. We have used the platform to detect E. coli in environmental water samples in about 1 h, with a limit of quantitation of 0.2 CFU/mL, and 3 copies per reaction. The platform was confirmed for detecting multiple E. coli strains, and for water samples of different salt concentrations. We validated the functions of the platform by analyzing recreational water samples collected near the Atlantic Ocean that contain different concentrations of salt and bacteria.
Globally, water resources used for recreation and drinking water are threatened by fecal pollution. These pollutants can cause gastrointestinal illness and environmental degradation. Additionally, most sources of fecal pollution are non-point sources stemming from multiple species. Identifying these sources is vital to categorizing the exposure risk from contact and improving remediation efforts. A common technique to provide species-specific information for fecal source identification is microbial source tracking (MST). MST quantifies DNA of host or host-associated microorganisms through polymerase chain reaction (PCR) technologies such as quantitative PCR (qPCR) or droplet digital PCR (ddPCR). MST techniques have been implemented globally and are used for routine monitoring. In the United States (US), the US Environmental Protection Agency has provided several approved standard PCR methods for MST and other recreational water quality applications. These methods have specified quality controls including sample processing controls (SPC) and assessments for sample inhibition. A standard SPC used in EPA methods involves spiking samples with salmon testes DNA (nominally originating from Chum Salmon, Oncorhynchus keta and quantifying them using Sketa22, a genus specific TaqManTM assay). This quality control (QC) behaves similarly to the microbial species being monitored. MST testing in Fall 2022 indicated elevated Sketa22 recoveries and re-analysis of samples indicated the detection of external Salmonidae DNA on both qPCR and ddPCR platforms. Our research was designed to identify the cause of this interference. Results indicate that the primer probe set may react with wild Salmonidae DNA. Analyzing the Sketa22 sequence using BLAST indicated matches with many species of Salmonidae present in the sampled stream system. Consequently, further research is required to identify the effectiveness of Sketa22 as a QC when native and migratory Salmonidae are present. General recommendations are provided to account for excess ambient Salmonidae DNA.
Microbial water quality is an integral to water security and is directly linked to human health, food safety, and ecosystem services. However, specifically pathogen data and even faecal indicator data (e.g., E. coli), are sparse and scattered, and their availability in different water bodies (e.g., groundwater) and in different socio-economic contexts (e.g., low- and middle-income countries) are inequitable. There is an urgent need to assess and collate microbial data across the world to evaluate the global state of ambient water quality, water treatment, and health risk, as time is running out to meet Sustainable Development Goal (SDG) 6 by 2030. The overall goal of this paper is to illustrate the need and advocate for building a robust and useful microbial water quality database and consortium worldwide that will help achieve SDG 6. We summarize available data and existing databases on microbial water quality, discuss methods for producing new data on microbial water quality, and identify models and analytical tools that utilize microbial data to support decision making. This review identified global datasets (7 databases), and regional datasets for Africa (3 databases), Australia/New Zealand (6 databases), Asia (3 databases), Europe (7 databases), North America (12 databases) and South America (1 database). Data are missing for low- and middle-income countries. Increased laboratory capacity (due to COVID-19 pandemic) and molecular tools can identify potential pollution sources and monitor directly for pathogens. Models and analytical tools can support microbial water quality assessment by making geospatial and temporal inferences where data are lacking. A genomics, information technology (IT), and data revolution is upon us and presents unprecedented opportunities to develop software and devices for real-time logging, automated analysis, standardization, and modelling of microbial data to strengthen knowledge of global water quality. These opportunities should be leveraged for achieving SDG 6 around the world.
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