Integrating Metagenomic and Bayesian Analyses to Evaluate the Performance and Confidence of CrAssphage as an Indicator for Tracking Human Sewage Contamination in China.

Recently, crAssphage has been proposed as a human-specific marker for tracking fecal contamination. However, its performance has always been validated in a limited number of host samples, which may obscure our understanding of its utility. Furthermore, few studies have quantified confidence of fecal contamination when using crAssphage. Here, we evaluate the performance and confidence of crAssphage by analyzing a large panel of metagenomic data sets combined with Bayesian analyses. Results demonstrate that crAssphage exhibits superior performance with high host sensitivity and specificity, indicating its suitability for tracking human fecal sources. With the marker, a high confidence (>90%) can be obtained and particularly, multiple samples with positive results provide a near certainty of confidence. The application of crAssphage in the sediments of three Chinese urban rivers shows a high confidence of >97% of human fecal contamination, suggesting the serious challenge of sewage pollution in these environments. Additionally, significant correlation is observed between crAssphage and antibiotic resistance genes (ARGs), expanding the utilization of crAssphage for pollution management of ARGs. This study highlights the benefit of using metagenomic-based analysis for evaluating the performance and confidence of microbial source tracking markers in the coming era of big data with increasing resources in available metagenomic data.

Effects on microbiomes and resistomes and the source-specific ecological risks of heavy metals in the sediments of an urban river.

This study aims to better understand the effects of heavy metal enrichment on microbiomes and resistomes and the source-specific ecological risks of metals in the sediments of an urban river. Geo-accumulation index and enrichment factor suggested the river sediments were contaminated by Cd, Cu, Pb, and Zn in varying degrees. High-throughput sequencing-based metagenomics analysis identified 430 types of antibiotic resistance genes (ARGs), dominated by the multidrug, MLS, bacitracin, quinolone, and aminoglycoside ARGs, and 52 metal resistance genes (MRGs) mainly conferring resistance to zinc, copper, cadmium, lead, mercury and multiple metals. Spearman correlation analysis and Mantel test showed the heavy metal enrichment exerted significant effects on the microbial community, ARGs and MRGs. Source apportionment using positive matrix factorization revealed that natural source (42.8%) was the largest contributor of metals in the river sediments, followed by urban activities (35.4%) and a mixed source (21.7%). However, when incorporating the apportionment results into a modified risk model to evaluate the source-specific ecological risks, results showed human activities dominated the risks of metals. Comparatively, the urban activities majorly caused moderate- and considerable- ecological risks, while the mixed source with respect to agricultural and industrial activities contributed higher percentages on high- and extremely high- ecological risks.

Source apportionment and source-oriented risk assessment of heavy metals in the sediments of an urban river-lake system.

Heavy metal pollution in lakes has attracted concerns worldwide since long retention times in lakes allow metals to accumulate and may pose significant threat to ecosystem health. For designing targeted risk mitigation strategies, it is necessary to identify the source-specific risks of heavy metals in the environment. Although previous studies have addressed either risk assessment or source identification of heavy metals in the environment, few have attempted to establish a link between them. In the study, we perform a combination of source apportionment and risk assessment for characterizing the pollution sources and source-specific risks of heavy metals in the sediments of an urban river-lake system. To this end, positive matrix factorization (PMF) was employed to apportion the potential sources of heavy metals, combined with a support vector machine classifier and the referential source fingerprints of metals in the study area. Then, the apportionment results were incorporated into the environmental risk models to evaluate the ecological and human health risks posed by heavy metals from the identified pollution sources. Results showed the river-lake system was contaminated by Cd, Cu, Zn and other metals in varying degree. Particularly, the element of Cd presented moderate to heavy pollution level. In relative, the industrial activities were identified as the largest contributor (48.0%) of heavy metals in the river-lake sediments, mainly associating with electroplating and paper making, followed by the agricultural activities (27.3%) and mix source (24.7%). Overall, the non-carcinogenic and carcinogenic risks posed by the heavy metals were acceptable, however, the element of Cd showed moderate ecological effect. Further, source-oriented risk evaluation suggested industrial processes made higher contributions to the ecological risk of heavy metals in the river-lake system. The study will provide regulators help to update the information by adding apportionment analysis in the context of risk assessment to facilitate subsequent mitigation strategies.

[Environmental Characteristics and Source Apportionment of Heavy Metals in the Sediments of a River-Lake System].

In this study, the geochemical characteristics and ecological risks of heavy metals in the sediments of a river-lake system were comprehensively identified and the spatial distribution was analyzed. Meanwhile, the potential sources of heavy metals in the sediments were apportioned using the positive matrix factorization model (PMF). The elements Cd, Cu, and Zn were identified as the main pollutants in the sediments of the river-lake system; in particular, Cd indicated an ecological risk. Compared with receiving lakes, the river is more polluted and poses a more significant risk. Spatial analysis of the lake suggested that the entrance of rivers had a relatively higher pollution degree and risk, indicating that rivers may be an important channel to transfer pollutants into the lake. PMF modelling showed that the heavy metals in the sediments were mainly associated with human activities (55.7%), including industrial emissions (20.3%), fertilizer application (19.5%), and aquaculture (15.9%).

Characterization and source identification of antibiotic resistance genes in the sediments of an interconnected river-lake system.

Antimicrobial resistance has been a global public health concern. The river-lake systems are one of the tightly connected terrestrial ecosystems and, appear to be reservoirs of antibiotic resistant genes (ARGs) and dispersal routes of resistant pathogens because they are easily impacted by human activities. Currently, systematic knowledge on the prevalence, transfer risk and source of ARGs in river-lake systems is largely lacking. In this study, we focused on the high-throughput profiling and source-sink relationship disentangling of ARGs in the sediments of an interconnected river-lake system (Fuhe River and its receiving Lake Baiyang in northern China). To this end, 40 surface sediments were collected for metagenomic shotgun sequencing. The profile and co-occurrence of ARGs in the sediments of the river-lake system were comprehensively characterized, as well as the mobile genetic elements (MGEs) carrying ARGs and their potential resistome dissemination risk. CrAssphage, a recently-discovered bacteriophage, was used to track human fecal pollution on the prevalence of ARGs. Meanwhile, a novel fast expectation-maximization microbial source tracking (FEAST) method was combined with linear discriminant analysis effect size method (LEfSe) for quantitatively apportioning the contribution of river sediment to the presence of ARGs in the receiving lake. Results showed abundant and diverse ARGs (24 types consisting of 510 subtypes) were detected in the sediments of the river-lake system, including some emerging ARGs such as mcr-1, tetX and carbapenemases types. Network analysis suggested non-random co-occurrence patterns of ARGs within the same type and among different types. Importantly, a number of MGE-carrying contigs were identified with jointly containing one or more ARGs, resulting in higher resistome risk potential in Lake Baiyang than many worldwide lakes. Source tracking indicated the prevalence of ARGs in the sediments of the river-lake system might be largely explained by the extent of human fecal contamination, and apportionment estimates the load transport from Fuhe River contributed more than 80% of ARGs to the receiving Lake Baiyang.

Source identification of antibiotic resistance genes in a peri-urban river using novel crAssphage marker genes and metagenomic signatures.

Antimicrobial resistance is a growing public health concern, and environment is regarded as an important reservoir and dissemination route for antibiotic resistance genes (ARGs). To prevent and control ARG pollution, it is essential to correctly disentangle source-sink relationship of ARGs in the environment. However, accurately apportioning sources of ARGs is still a big challenge due to the complex interaction of multiple sources and contaminants in the environment with changing dynamics. In this study, we addressed this problem and focused on identifying the potential sources of ARGs in a peri-urban river by jointly utilizing two novel microbial source tracking methods. To attain the objective, sediment/water samples were collected from the peri-urban river and four ARG-associated ecotypes including effluents of sewage treatment plants (STPs), STP influent, chicken manures and pig manures. The high-throughput profilings of ARGs and microbial taxa in the river sediments and the four ecotypes were comprehensively characterized in combination of shotgun sequencing and metagenomic assembly analysis. CrAssphage, a recently-discovered DNA bacteriophage, was employed to track the impact of human fecal pollution on ARGs in the river sediments. Further, SourceTracker, a machine-learning classification tool, was used for quantifying the contributions of potential sources to ARGs in the river sediments based on the metagenomic signatures of ARGs and microbial taxa. In total, 888 ARG subtypes belonging to 29 ARG types were detected across all samples, including mcr-1 and a range of carbapenemases types. Statistical analyses suggested different ecotypes generally had distinct profiles of both ARGs and microbial taxa, while the ARG compositions were significantly correlated with the microbial community. Source tracking with crAssphage showed the presence of ARGs in the river sediments might be largely impacted by the extent of human fecal pollution, which was also confirmed by the analyses of SourceTracker that the discharge from STPs was the largest contributor of ARGs (81.6-92.1%) and microbes (49.3-68.1%) in the river sediments. Results of the study can help us to better understand the characterization of ARGs in the peri-urban ecosystem and to design effective prevention and control strategies for reducing ARG dissemination.

Characterization and source-tracking of antibiotic resistomes in the sediments of a peri-urban river.

The peri-urban rivers are one of the critical interfaces between urban-rural symbiotic ecosystems and appear to be a reservoir of antibiotic resistance genes (ARGs) in the environment. To prevent the transmission risks of ARGs between peri-urban river and human, it is essential to explore the prevalence and source of ARGs in the environment for designing potential mitigation strategies. In this study, we focused on the characterization and source-tracking of ARGs in the sediments of a typical peri-urban river in Beijing, Chaobai River. Twenty-seven ARGs frequently reported in the environment, and two integrons (intI1 and intI2) were detected using high-throughput quantitative PCR. The profile of bacterial community was determined by performing 16S rRNA gene sequencing. Meanwhile, crAssphage, a novel recently-discovered DNA bacteriophage, was employed for tracking the contribution of human fecal pollution to the prevalence of ARGs. Results showed that the targeted ARGs were detected widely in the sediments of Chaobai River. Relatively, the abundances of ARGs in downstream were higher than those in the upstream, likely suggesting a gradient impact of anthropogenic activities along the river. Remarkably, the int1 gene was correlated significantly with most of the ARGs and might be the key factor influencing the shaping of ARGs in the river sediments. However, no significant correlations were observed between the ARGs and selective pressure factors, including antibiotics and metals. Of the identified 1039 genera, Escherichia-Shigella, Bacteroides, Arcobacter, Dechloromonas and Pseudomonas were the top most abundant organisms. Microbial source tracking based on the crAssphage annotation suggested that human sewage might be one of the potential sources of resistance bacteria in the river sediments. The study can advance our knowledge about ARGs in the peri-urban river and provides a management reference for ARG pollution control.