Urban coasts receive watershed drainage from ecosystems that include highly developed lands with sewer and stormwater infrastructure. In these complex ecosystems, coastal waters are often contaminated with fecal pollution, where multiple delivery mechanisms that often contain multiple fecal sources make it difficult to mitigate the pollution. Here, we exploit bacterial community sequencing of the V6 and V6V4 hypervariable regions of the bacterial 16S rRNA gene to identify bacterial distributions that signal the presence of sewer, fecal, and human fecal pollution. The sequences classified to three sewer infrastructure-associated bacterial genera, Acinetobacter, Arcobacter, and Trichococcus, and five fecal-associated bacterial families, Bacteroidaceae, Porphyromonadaceae, Clostridiaceae, Lachnospiraceae, and Ruminococcaceae, served as signatures of sewer and fecal contamination, respectively. The human fecal signature was determined with the Bayesian source estimation program SourceTracker, which we applied to a set of 40 sewage influent samples collected in Milwaukee, WI, USA to identify operational taxonomic units (≥97 % identity) that were most likely of human fecal origin. During periods of dry weather, the magnitudes of all three signatures were relatively low in Milwaukee's urban rivers and harbor and nearly zero in Lake Michigan. However, the relative contribution of the sewer and fecal signature frequently increased to >2 % of the measured surface water communities following sewer overflows. Also during combined sewer overflows, the ratio of the human fecal pollution signature to the fecal pollution signature in surface waters was generally close to that of sewage, but this ratio decreased dramatically during dry weather and rain events, suggesting that nonhuman fecal pollution was the dominant source during these weather-driven scenarios. The qPCR detection of two human fecal indicators, human Bacteroides and Lachno2, confirmed the urban fecal footprint in this ecosystem extends to at least 8 km offshore.
" et al . , 2011 ) . Analysis of profiles of microbial SSU rRNA genes ( V4 / V6 regions ) with a source estimation program employing Bayesian statistics ( SourceTracker ) allowed identification of human fecal and sewage signatures which correlated to the distribution of human - source markers Lachno2 and HF183 along the coastline of Lake Michigan ( Newton et al . , 2013 ) . Use of bacterial taxonomic groups identified through NGS - based surveys as alternative indicators may be site - specific as several studies have shown that the microbial composition in different sewer systems can differ according to several instances : origin of the waste materials ( Ye and Zhang , 2013 ) , climatic variations due "
[Show abstract][Hide abstract] ABSTRACT: Water quality is an emergent property of a complex system comprised of interacting microbial populations and introduced microbial and chemical contaminants. Studies leveraging next-generation sequencing (NGS) technologies are providing new insights into the ecology of microbially mediated processes that influence fresh water quality such as algal blooms, contaminant biodegradation, and pathogen dissemination. In addition, sequencing methods targeting small subunit (SSU) rRNA hypervariable regions have allowed identification of signature microbial species that serve as bioindicators for sewage contamination in these environments. Beyond amplicon sequencing, metagenomic and metatranscriptomic analyses of microbial communities in fresh water environments reveal the genetic capabilities and interplay of waterborne microorganisms, shedding light on the mechanisms for production and biodegradation of toxins and other contaminants. This review discusses the challenges and benefits of applying NGS-based methods to water quality research and assessment. We will consider the suitability and biases inherent in the application of NGS as a screening tool for assessment of biological risks and discuss the potential and limitations for direct quantitative interpretation of NGS data. Secondly, we will examine case studies from recent literature where NGS based methods have been applied to topics in water quality assessment, including development of bioindicators for sewage pollution and microbial source tracking, characterizing the distribution of toxin and antibiotic resistance genes in water samples, and investigating mechanisms of biodegradation of harmful pollutants that threaten water quality. Finally, we provide a short review of emerging NGS platforms and their potential applications to the next generation of water quality assessment tools.
Frontiers in Microbiology 10/2015; 6(1027). DOI:10.3389/fmicb.2015.01027 · 3.99 Impact Factor
"However, stark differences in the bacterial communities of the water versus sediment samples, as was seen here using a high-throughput sequencing approach, have been well-established in the literature (Gløckner et al., 2000; Zwart et al., 2002). Nevertheless, this study offers novel information regarding the contribution of the sediment community to that in the water column using an OTU-based analysis that was previously employed to detect and quantify sources of fecal contamination (Knights et al., 2011; Newton et al., 2013). Interestingly , the Minnesota River was found to have N50% of sequence reads attributed to sediment, and this watershed has been reported to contribute as much as 90% of sediment to Lake Pepin (Engstrom et al., 2009). "
[Show abstract][Hide abstract] ABSTRACT: Bacterial community structure (BCS) in freshwater ecosystems varies seasonally and due to physicochemical gradients, but metacommunity structure of a major river remains understudied. Here we characterize the BCS along the Mississippi River and contributing rivers in Minnesota over three years using Illumina next-generation sequencing, to determine how changes in environmental conditions as well as inputs from surrounding land and confluences impacted community structure. Contributions of sediment to water microbial diversity were also evaluated. Long-term variation in community membership was observed, and significant shifts in relative abundances of major freshwater taxa, including alpha-Proteobacteria, Burkholderiales, and Actinomycetales, were observed due to temporal and spatial variations. Environmental parameters (e.g. temperature, rainfall, and nutrient concentrations) primarily contributed to differences in phyla abundances (88% of variance), with minimal influence from spatial distance alone (<1% of variance). Furthermore, an annually-recurrent BCS was observed in late summer, further suggesting that seasonal dynamics strongly influence community composition. Sediment communities differed from those in the water, but contributed up to 50% to community composition in the water column. Among water sampling sites, 34% showed significant variability in BCS of replicate samples indicating variability among riverine communities due to heterogeneity in the water column. Results of this study highlight the need for a better understanding of spatial and temporal variations in riverine bacterial diversity associated with physicochemical gradients and reveal how communities in sediments, and potentially other environmental reservoirs, impact waterborne BCS. Techniques used in this study may prove useful to determine sources of microbes from sediments and soils to waterways, which will facilitate best management practices and total maximum daily load determinations.
Science of The Total Environment 02/2015; 505. DOI:10.1016/j.scitotenv.2014.10.012 · 4.10 Impact Factor
"Rivers and streams also have a large number of potential inputs, each adding new microbial communities to the mix. For example, although at any point in a fluvial system the majority of the water, and therefore microbial community, is likely to have originated from upstream, additional inputs can arrive from groundwater (Sorensen et al., 2013), soil and surface runoff (Crump et al., 2007), atmospheric and precipitation inputs (Christner et al., 2008; DeLeon-Rodriguez et al., 2013) and anthropogenic point sources such as sewage outlets (Newton et al., 2013). Moreover, many fluvial networks worldwide are highly modified by land use changes and the connection of man-made waterways such as canals and reservoirs, causing significant changes in water chemistry, flow velocities and water residence times (Whitehead et al., 2013). "
[Show abstract][Hide abstract] ABSTRACT: Lotic ecosystems such as rivers and streams are unique in that they represent a continuum of both space and time during the transition from headwaters to the river mouth. As microbes have very different controls over their ecology, distribution and dispersion compared with macrobiota, we wished to explore biogeographical patterns within a river catchment and uncover the major drivers structuring bacterioplankton communities. Water samples collected across the River Thames Basin, UK, covering the transition from headwater tributaries to the lower reaches of the main river channel were characterised using 16S rRNA gene pyrosequencing. This approach revealed an ecological succession in the bacterial community composition along the river continuum, moving from a community dominated by Bacteroidetes in the headwaters to Actinobacteria-dominated downstream. Location of the sampling point in the river network (measured as the cumulative water channel distance upstream) was found to be the most predictive spatial feature; inferring that ecological processes pertaining to temporal community succession are of prime importance in driving the assemblages of riverine bacterioplankton communities. A decrease in bacterial activity rates and an increase in the abundance of low nucleic acid bacteria relative to high nucleic acid bacteria were found to correspond with these downstream changes in community structure, suggesting corresponding functional changes. Our findings show that bacterial communities across the Thames basin exhibit an ecological succession along the river continuum, and that this is primarily driven by water residence time rather than the physico-chemical status of the river.
The ISME Journal 09/2014; 9(2). DOI:10.1038/ismej.2014.166 · 9.30 Impact Factor
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