Next-generation sequencing reveals fecal contamination and potentially pathogenic bacteria in a major inflow river of Taihu Lake☆
Graphical abstract
Introduction
Waterborne diseases cause about 2.2 million deaths annually, with the majority occurring in children under the age of 5 years (WHO, 2015). In China, rapid urbanization, industrialization, and socio-economic development have led to a high degree of pollution in lakes and rivers, and this is regarded as a major challenging environmental issue (Jiang, 2009). Although surface water quality in China has improved rapidly in recent years, it has been reported that nearly 200 million people are still using unsafe water sources and approximately 60,000 people die every year due to water pollution related diseases (Han et al., 2016; Jiang, 2015). As diarrheal diseases are primarily caused by contamination of water sources with human or animal feces (WHO, 2017), monitoring of fecal contamination and pathogens in waters used for human consumption and recreational activities has become mandatory.
Most conventional fecal monitoring studies rely on the enumeration of fecal indicator bacteria (FIB), and this is considered as the “gold standard” to assess microbiological water quality and pathogen presence in environmental waters (Savichtcheva and Okabe, 2006). However, conventional FIB enumeration methods do not determine the origin of the fecal source, and previous reports have shown little or no correlation between FIB and the presence of pathogenic organisms (Shahryari et al., 2014). Therefore, several qualitative and quantitative “Microbial Source Tracking (MST)” methods have been developed to overcome this limitation, and MST methods have focused on determining the origin of the fecal sources (Green et al., 2014; Kildare et al., 2007; Mieszkin et al., 2009). In addition, several quantitative polymerase chain reaction (qPCR) methods have been widely used to determine the presence and abundance of pathogens (Ahmed et al., 2009; Oster et al., 2014). However, these methods can identify only the targeted pathogens that are specifically chosen for monitoring. As a result, it is a challenge to monitor a wide-range of pathogens in a watershed with culture-based or culture-independent qPCR-based methods. Next-generation sequencing (NGS) methods targeting the 16S rRNA gene have gained attention in order to explore the diversity of bacterial communities and their influence on microbial water quality (Ibekwe et al., 2013; Staley et al., 2013), potentially overcoming the limitations of culture and PCR-based detection protocols. Although community-based NGS methods (OTU comparison between fecal sources and environmental samples) have been proposed for microbial source tracking (Jeong et al., 2011; Unno et al., 2010), they are considered as a qualitative method for assessing fecal pollution since the OTU comparison results show discrepancies depending on the nature of the fecal OTU library applied (Boehm et al., 2013). However, a Bayesian algorithm based NGS method (SourceTracker) has been developed to predict the quantitative presence of fecal contamination (Knights et al., 2011). Recent reports indicated lower confidence in quantification results and also spatiotemporal limitations of the SourceTracker method indicating the necessity for optimization and validation prior to application in a new geographical area (Ahmed et al., 2015; Brown et al., 2017; Staley et al., 2018; Unno et al., 2018). The NGS based microbial community analysis approach is still considered as a valuable tool for preliminary investigation of water samples to assess public health risk associated with fecal contamination or pathogens (Tan et al., 2015), though such studies are very limited. The NGS method can evaluate bacterial diversity (including fecal and pathogenic bacteria) of water or other environmental samples and their relative abundance, providing valuable information to prioritize specific exposure assessment of suitable targets using quantitative (qPCR) methods (Tan et al., 2015). Most of the NGS studies on monitoring microbial communities in water samples have relied on targeting hypervariable regions of the 16S rRNA gene (Guo et al., 2013). Although 454-pyrosequencing was the commonly used sequencing platform in earlier studies, it has now been discontinued and is superseded by the sequencing of much larger libraries on the Illumina platform to yield community 16S rRNA based sequence datasets that are orders of magnitude larger and much more informative (Loman et al., 2012; Newton et al., 2015; Sinclair et al., 2015). The commonly used open source software packages for sequence analysis such as QIIME and MOTHUR have also been upgraded to analyze “pair-end” sequence data produced by Illumina sequencers, improving the performance (Caporaso et al., 2010; Kozich et al., 2013).
Taihu Lake is one of the top five largest freshwater lakes in China and serves as an important source of drinking water in addition to providing fisheries, transportation and flood protection (Chen et al., 2016). Currently, Taihu lake is connected to more than 200 rivers and tributaries, though the main inflow river is limited to thirteen rivers (Qin et al., 2007). Previous reports indicated that the inflow rivers were contributing to eutrophication, cyanobacterial blooms and fecal pollution, particularly Tiaoxi River (Hagedorn and Liang, 2011; Vadde et al., 2018). Although previous studies have addressed the microbial community composition in water and sediment samples of Taihu Lake (Cai et al., 2013; Paerl et al., 2011; Wilhelm et al., 2011; Zhao et al., 2017; Zheng et al., 2017), these have not focused on fecal bacteria and pathogens in the Tiaoxi River. Here, the bacterial community structure and relative abundance of fecal bacteria and pathogens in Tiaoxi River water are determined by the interrogation of large 16S rRNA gene sequence datasets generated by NGS on the Illumina platform. The specific objectives were to i) study the spatial and temporal variations in bacterial diversity in Tiaoxi River water, ii) determine the relative abundance of bacteria and potential pathogens of fecal origin and iii) assess the influence of environmental factors on bacterial diversity.
Section snippets
Collection of water and fecal samples
Twenty-five sampling locations were initially selected, covering domestic, agricultural and industrial areas of Tiaoxi river (Taihu watershed) (Fig. 1). Based on the detailed physico-chemical and microbiological characterization of the water, 15 locations were identified as pollution hotspots in our earlier study (Vadde et al., 2018) and water samples collected from those locations were used for bacterial community analysis. The details of land use type around the selected sampling locations
Assessment of physico-chemical and microbiological parameters
The detailed results of physico-chemical and microbiological parameters measured in various locations in Tiaoxi river were reported in our earlier study (Vadde et al., 2018). Among the tested parameters, pH and conductivity were found to be within the acceptable limits set by the Ministry of Environmental Protection (MEP), China (MEP, 2016), however TN was significantly higher than acceptable levels in all the locations and across the three seasons. TP, NH4–N, and NO2–N exceeded the acceptable
Discussion
Recent advances in NGS technologies coupled with reduced cost has enabled the application of microbial community analysis for monitoring of microbial quality and diversity in the aquatic environment and profiling the microbiota associated with fecal samples (Ley et al., 2008; Marti et al., 2017; Newton et al., 2011; Vierheilig et al., 2015). In this study, bacterial communities in water, fecal and wastewater samples were studied by Illumina sequencing by targeting the V4 hypervariable region of
Conclusions
A total of 20 different phyla were observed in most of the water samples of Tiaoxi River and wastewater samples, while only 16 phyla were detected in fecal samples. Hierarchical cluster analysis and PCoA performed for fecal, wastewater and water samples showed that fecal and wastewater samples clustered separately from water. Venn diagrams revealed that chicken fecal samples (9.8%) shared the highest number of OTUs with total water samples, followed by pig (7.1%), and human samples (4.5%)
Conflicts of interests
The authors declare no conflicts of interest.
Acknowledgments
The authors acknowledge the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20141211), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 13KJB180022) and Key Program Special Fund in Xi'an Jiaotong-Liverpool University (XJTLU; Grant No. KSF-E-20) for financial support. JW was supported by CAS Key Research Program of Frontier Sciences (QYZDB-SSW-DQC043), and National Natural Science Foundation of China (41871048, 91851117). We would
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2022, Journal of Hazardous Materials LettersCitation Excerpt :A high variation in the bacterial communities related to their taxonomic composition and spatial distribution was reported among or within different rivers across the world (Bai et al., 2014; Ibekwe et al., 2016; Staley et al., 2013; Wang et al., 2018a). However, studies carried out on the impact of raw sewage or effluents from WWTPs on receiving rivers and coastal water frequently reported a relatively high abundance of Cyanobacteria, Proteobacteria, Actinobacteria, and Bacteroidetes (He et al., 2017; Kalinowska et al., 2022; Vadde et al., 2019; Wang et al., 2018b). Except for G1 and G2 samples collected after three months of Hurricane Harvey, the order level taxonomic analysis revealed that Actinomycetales (14.6 %), Synechococcales (10.6 %), and Burkholderiales (10.5 %) were predominant in the water samples; although their relative abundance varied among three sampling events (Fig. S3).
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This paper has been recommended for acceptance by Dr. Sarah Harmon.