Integrated online system for a pyrosequencing-based microbial source tracking method that targets Bacteroidetes 16S rDNA

Next Generation High Throughput Sequencing to Assess Microbial Communities: An Application Based on Water Quality

Traditional techniques to identify different contaminants (biological or chemical) in the waters are slow, laborious, and can require specialized expertise. Hence, the rapid determination of water quality using more sensitive and reliable metagenomic based approaches attains special importance. Metagenomics deals with the study of genetic material that is recovered from microbial communities present in environmental samples. In traditional techniques cultivation-based methodologies were used to describe the diversity of microorganisms in environmental samples. It has failed to function as a robust marker because of limited taxonomic and phylogenetic implications. In this backdrop, high-throughput DNA sequencing approaches have proven very powerful in microbial source tracking because of investigating the full variety of genome-based analysis such as microbial genetic diversity and population structure played by them. Next generation sequencing technologies can reveal a greater proportion of microbial communities that have not been reported earlier by traditional techniques. The present review highlights the shift from traditional techniques for the basic study of community composition to next-generation sequencing (NGS) platforms and their potential applications to the biomonitoring of water quality in relation to human health.

Identification the source of fecal contamination for geographically unassociated samples with a statistical classification model based on support vector machine

The bacterial diversity and corresponding biological significance revealed by high-throughput sequencing contribute massive information to source tracking of fecal contamination. The performances of classification models on predicting the fecal source of geographical local and foreign samples were examined herein, by applying support vector machine (SVM) algorithm. Random forest (RF) and Adaboost were applied for comparison as well. Discriminatory sequences were selected from Clostridiale, Bacteroidales, or Lactobacillales bacterial groups using extremely randomized trees (ExtraTrees). 1.51-12.64% of the unique sequences in the original library composed the representative markers, and they contributed 70% of the discrepancies between source microbiomes. The overall accuracy of the SVM model and the RF model on local samples was 96.08% and 98.04%, respectively, higher than that of the Adaboost (90.20%). As for the non-local samples, the SVM assigned most of the fecal samples into the correct category while several false-positive judgments occurred in closely related groups. The results in this paper suggested that the SVM was a time-saving and accurate method for fecal source tracking in contaminated water body with the potential capability of executing tasks based on geographically unassociated samples, and underlined the necessity of qPCR analysis for accurate detection of human source pollution.

Fecal pollution: new trends and challenges in microbial source tracking using next-generation sequencing

In this minireview, we expand upon traditional microbial source tracking (MST) methods by discussing two recently developed, next-generation-sequencing (NGS)-based MST approaches to identify sources of fecal pollution in recreational waters. One method defines operational taxonomic units (OTUs) that are specific to a fecal source, e.g., humans and animals or shared among multiple fecal sources to determine the magnitude and likely source association of fecal pollution. The other method uses SourceTracker, a program using a Bayesian algorithm, to determine which OTUs have contributed to an environmental community based on the composition of microbial communities in multiple fecal sources. Contemporary NGS-based MST tools offer a promising avenue to rapidly characterize fecal source contributions for water monitoring and remediation efforts at a broader and more efficient scale than previous molecular MST methods. However, both NGS methods require optimized sequence processing methodologies (e.g. quality filtering and clustering algorithms) and are influenced by primer selection for amplicon sequencing. Therefore, care must be taken when extrapolating data or combining datasets. Furthermore, traditional limitations of library-dependent MST methods, including differential decay of source material in environmental waters and spatiotemporal variation in source communities, remain to be fully understood. Nevertheless, increasing use of these methods, as well as expanding fecal taxon libraries representative of source communities, will help improve the accuracy of these methods and provide promising tools for future MST investigations.

Application of SourceTracker for Accurate Identification of Fecal Pollution in Recreational Freshwater: A Double-Blinded Study

The efficacy of SourceTracker software to attribute contamination from a variety of fecal sources spiked into ambient freshwater samples was investigated. Double-blinded samples spiked with ≤5 different sources (0.025-10% vol/vol) were evaluated against fecal taxon libraries characterized by next-generation amplicon sequencing. Three libraries, including an initial library (17 nonlocal sources), a blinded source library (5 local sources), and a composite library (local and nonlocal sources), were used with SourceTracker. SourceTracker's predictions of fecal compositions in samples were made, in part, based on distributions of taxa within abundant genera identified as discriminatory by discriminant analyses but also using a large percentage of low abundance taxa. The initial library showed poor ability to characterize blinded samples, but, using local sources, SourceTracker showed 91% accuracy (31/34) at identifying the presence of source contamination, with two false positives for sewage and one for horse. Furthermore, sink predictions of source contamination were positively correlated (Spearman's ρ ≥ 0.88, P < 0.001) with spiked source volumes. Using the composite library did not significantly affect sink predictions ( P > 0.79) compared to those made using the local sources alone. Results of this study indicate that geographically associated fecal samples are required for SourceTracker to assign host sources accurately.

Changes in bacterial and eukaryotic communities during sewage decomposition in Mississippi river water

Microbial decay processes are one of the mechanisms whereby sewage contamination is reduced in the environment. This decomposition process involves a highly complex array of bacterial and eukaryotic communities from both sewage and ambient waters. However, relatively little is known about how these communities change due to mixing and subsequent decomposition of the sewage contaminant. We investigated decay of sewage in upper Mississippi River using Illumina sequencing of 16S and 18S rRNA gene hypervariable regions and qPCR for human-associated and general fecal Bacteroidales indicators. Mixtures of primary treated sewage and river water were placed in dialysis bags and incubated in situ under ambient conditions for seven days. We assessed changes in microbial community composition under two treatments in a replicated factorial design: sunlight exposure versus shaded and presence versus absence of native river microbiota. Initial diversity was higher in sewage compared to river water for 16S sequences, but the reverse was observed for 18S sequences. Both treatments significantly shifted community composition for eukaryotes and bacteria (P < 0.05). Data indicated that the presence of native river microbiota, rather than exposure to sunlight, accounted for the majority of variation between treatments for both 16S (R = 0.50; P > 0.001) and 18S (R = 0.91; P = 0.001) communities. A comparison of 16S sequence data and fecal indicator qPCR measurements indicated that the latter was a good predictor of overall bacterial community change over time (rho: 0.804-0.814, P = 0.001). These findings suggest that biotic interactions, such as predation by bacterivorous protozoa, can be critical factors in the decomposition of sewage in freshwater habitats and support the use of Bacteroidales genetic markers as indicators of fecal pollution.

Potential applications of next generation DNA sequencing of 16S rRNA gene amplicons in microbial water quality monitoring

The applicability of next generation DNA sequencing (NGS) methods for water quality assessment has so far not been broadly investigated. This study set out to evaluate the potential of an NGS-based approach in a complex catchment with importance for drinking water abstraction. In this multi-compartment investigation, total bacterial communities in water, faeces, soil, and sediment samples were investigated by 454 pyrosequencing of bacterial 16S rRNA gene amplicons to assess the capabilities of this NGS method for (i) the development and evaluation of environmental molecular diagnostics, (ii) direct screening of the bulk bacterial communities, and (iii) the detection of faecal pollution in water. Results indicate that NGS methods can highlight potential target populations for diagnostics and will prove useful for the evaluation of existing and the development of novel DNA-based detection methods in the field of water microbiology. The used approach allowed unveiling of dominant bacterial populations but failed to detect populations with low abundances such as faecal indicators in surface waters. In combination with metadata, NGS data will also allow the identification of drivers of bacterial community composition during water treatment and distribution, highlighting the power of this approach for monitoring of bacterial regrowth and contamination in technical systems.

Bacterial community structure is indicative of chemical inputs in the Upper Mississippi River

Local and regional associations between bacterial communities and nutrient and chemical concentrations were assessed in the Upper Mississippi River in Minnesota to determine if community structure was associated with discrete types of chemical inputs associated with different land cover. Bacterial communities were characterized by Illumina sequencing of the V6 region of 16S rDNA and compared to >40 chemical and nutrient concentrations. Local bacterial community structure was shaped primarily by associations among bacterial orders. However, order abundances were correlated regionally with nutrient and chemical concentrations, and were also related to major land coverage types. Total organic carbon and total dissolved solids were among the primary abiotic factors associated with local community composition and co-varied with land cover. Escherichia coli concentration was poorly related to community composition or nutrient concentrations. Abundances of 14 bacterial orders were related to land coverage type, and seven showed significant differences in abundance (P ≤ 0.046) between forested or anthropogenically-impacted sites. This study identifies specific bacterial orders that were associated with chemicals and nutrients derived from specific land cover types and may be useful in assessing water quality. Results of this study reveal the need to investigate community dynamics at both the local and regional scales and to identify shifts in taxonomic community structure that may be useful in determining sources of pollution in the Upper Mississippi River.

Fe-oxide grain coatings support bacterial Fe-reducing metabolisms in 1.7-2.0 km-deep subsurface quartz arenite sandstone reservoirs of the Illinois Basin (USA)

The Cambrian-age Mt. Simon Sandstone, deeply buried within the Illinois Basin of the midcontinent of North America, contains quartz sand grains ubiquitously encrusted with iron-oxide cements and dissolved ferrous iron in pore-water. Although microbial iron reduction has previously been documented in the deep terrestrial subsurface, the potential for diagenetic mineral cementation to drive microbial activity has not been well studied. In this study, two subsurface formation water samples were collected at 1.72 and 2.02 km, respectively, from the Mt. Simon Sandstone in Decatur, Illinois. Low-diversity microbial communities were detected from both horizons and were dominated by Halanaerobiales of Phylum Firmicutes. Iron-reducing enrichment cultures fed with ferric citrate were successfully established using the formation water. Phylogenetic classification identified the enriched species to be related to Vulcanibacillus from the 1.72 km depth sample, while Orenia dominated the communities at 2.02 km of burial depth. Species-specific quantitative analyses of the enriched organisms in the microbial communities suggest that they are indigenous to the Mt. Simon Sandstone. Optimal iron reduction by the 1.72 km enrichment culture occurred at a temperature of 40°C (range 20-60°C) and a salinity of 25 parts per thousand (range 25-75 ppt). This culture also mediated fermentation and nitrate reduction. In contrast, the 2.02 km enrichment culture exclusively utilized hydrogen and pyruvate as the electron donors for iron reduction, tolerated a wider range of salinities (25-200 ppt), and exhibited only minimal nitrate- and sulfate-reduction. In addition, the 2.02 km depth community actively reduces the more crystalline ferric iron minerals goethite and hematite. The results suggest evolutionary adaptation of the autochthonous microbial communities to the Mt. Simon Sandstone and carries potentially important implications for future utilization of this reservoir for CO2 injection.

Discovering new indicators of fecal pollution

Fecal pollution indicators are essential to identify and remediate contamination sources and protect public health. Historically, easily cultured facultative anaerobes such as fecal coliforms, Escherichia coli, or enterococci have been used but these indicators generally provide no information as to their source. More recently, molecular methods have targeted fecal anaerobes, which are much more abundant in humans and other mammals, and some strains appear to be associated with particular host sources. Next-generation sequencing and microbiome studies have created an unprecedented inventory of microbial communities associated with fecal sources, allowing reexamination of which taxonomic groups are best suited as informative indicators. The use of new computational methods, such as oligotyping coupled with well-established machine learning approaches, is providing new insights into patterns of host association. In this review we examine the basis for host-specificity and the rationale for using 16S rRNA gene targets for alternative indicators and highlight two taxonomic groups, Bacteroidales and Lachnospiraceae, which are rich in host-specific bacterial organisms. Finally, we discuss considerations for using alternative indicators for water quality assessments with a particular focus on detecting human sewage sources of contamination.

Bacteroidales terminal restriction fragment length polymorphism (TRFLP) for fecal source differentiation in comparison to and in combination with universal bacteria TRFLP

Terminal restriction fragment length polymorphism (TRFLP) is an attractive community analysis method for microbial source tracking (MST) because it is accessible, relatively inexpensive, and can discern multiple fecal sources simultaneously. A new Bacteroidales TRFLP (Bac-TRFLP) method was developed and its source identification performance was evaluated by itself, in comparison to, and in combination with an existing universal bacterial TRFLP method in two laboratories. Sixty-four blind samples from 12 fecal sources (sewage, septage, human, dog, horse, cow, deer, pig, chicken, goose, pigeon, and gull) were used for evaluation. Bac- and Univ-TRFLP exhibited similarly high overall correct identification (>88% and >89%, respectively), excellent specificity regardless of fecal sources, variable sensitivity depending on the source, and stable performance across two laboratories. Compared to Univ-TRFLP, Bac-TRFLP had better sensitivity and specificity with horse, cow, and pig fecal sources but was not suited for certain avian sources such as goose, gull, and pigeon. Combining the general and more targeted TRFLP methods (Univ&Bac-TRFLP) achieved higher overall correct identification (>92%), higher sensitivity and specificity metrics, and higher reproducibility between laboratories. Our results suggest that the Bac-TRFLP and Univ&Bac-TRFLP methods are promising additions to the MST toolbox and warrant further evaluation and utilization in field MST applications.

Evaluation of molecular community analysis methods for discerning fecal sources and human waste

Molecular microbial community analyses provide information on thousands of microorganisms simultaneously, and integrate biotic and abiotic perturbations caused by fecal contamination entering water bodies. A few studies have explored community methods as emerging approaches for microbial source tracking (MST), however, an evaluation of the current state of this approach is lacking. Here, we utilized three types of community-based methods with 64 blind, single- or dual-source, challenge samples generated from 12 sources, including: humans (feces), sewage, septage, dogs, pigs, deer, horses, cows, chickens, gulls, pigeons, and geese. Each source was a composite from multiple donors from four representative geographical regions in California. Methods evaluated included terminal restriction fragment polymorphism (TRFLP), phylogenetic microarray (PhyloChip), and next generation (Illumina) sequencing. These methods correctly identified dominant (or sole) sources in over 90% of the challenge samples, and exhibited excellent specificity regardless of source, rarely detecting a source that was not present in the challenge sample. Sensitivity, however, varied with source and community analysis method. All three methods distinguished septage from human feces and sewage, and identified deer and horse with 100% sensitivity and 100% specificity. Method performance improved if the composition of blind dual-source reference samples were defined by DNA contribution of each single source within the mixture, instead of by Enterococcus colony forming units. Data analysis approach also influenced method performance, indicating the need to standardize data interpretation. Overall, results of this study indicate that community analysis methods hold great promise as they may be used to identify any source, and they are particularly useful for sources that currently do not have, and may never have, a source-specific single marker gene.

Correlative assessment of fecal indicators using human mitochondrial DNA as a direct marker

Identifying the source of surface water fecal contamination is paramount to mitigating pollution and risk to human health. Fecal bacteria such as E. coli have been staple indicator organisms for over a century, however there remains uncertainty with E. coli-based metrics since these bacteria are abundant in the environment. The relationships between the presence of direct indicator of human waste (human mitochondrial DNA), human-specific Bacteroidales, and E. coli were studied for water samples taken from an urban creek system (Duck Creek Watershed, Cincinnati, OH) impacted by combined sewer overflows. Logistic regression analysis shows that human-specific Bacteroidales correlates much more closely to human mitochondrial DNA (R = 0.62) relative to E. coli (R = 0.33). We also examine the speciation of Bacteroidales within the Duck Creek Watershed using next-generation sequencing technology (Ion Torrent) and show the most numerous populations to be associated with sewage. Here we demonstrate that human-specific Bacteroidales closely follow the dynamics of human mitochondrial DNA concentration changes, indicating that these obligate anaerobes are more accurate than E. coli for fecal source tracking, lending further support to risk overestimation using coliforms, especially fecal coliforms and E. coli.

Application of Illumina next-generation sequencing to characterize the bacterial community of the Upper Mississippi River

AIMS

A next-generation, Illumina-based sequencing approach was used to characterize the bacterial community at ten sites along the Upper Mississippi River to evaluate shifts in the community potentially resulting from upstream inputs and land use changes. Furthermore, methodological parameters including filter size, sample volume and sample reproducibility were evaluated to determine the best sampling practices for community characterization.

METHODS AND RESULTS

Community structure and diversity in the river was determined using Illumina next-generation sequencing technology and the V6 hypervariable region of 16S rDNA. A total of 16,400 operational taxonomic units (OTUs) were observed (4594 ± 824 OTUs per sample). Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria and Verrucomicrobia accounted for 93.6 ± 1.3% of all sequence reads, and 90.5 ± 2.5% belonged to OTUs shared among all sites (n = 552). Among nonshared sequence reads at each site, 33-49% were associated with potentially anthropogenic impacts upstream of the second sampling site. Alpha diversity decreased with distance from the pristine headwaters, while rainfall and pH were positively correlated with diversity. Replication and smaller filter pore sizes minimally influenced the characterization of community structure.

CONCLUSIONS

Shifts in community structure are related to changes in the relative abundance, rather than presence/absence of OTUs, suggesting a 'core bacterial community' is present throughout the Upper Mississippi River.

SIGNIFICANCE AND IMPACT OF THE STUDY

This study is among the first to characterize a large riverine bacterial community using a next-generation-sequencing approach and demonstrates that upstream influences and potentially anthropogenic impacts can influence the presence and relative abundance of OTUs downstream resulting in significant variation in community structure.

Microbial structures, functions, and metabolic pathways in wastewater treatment bioreactors revealed using high-throughput sequencing

The objective of this study was to explore microbial community structures, functional profiles, and metabolic pathways in a lab-scale and a full-scale wastewater treatment bioreactors. In order to do this, over 12 gigabases of metagenomic sequence data and 600,000 paired-end sequences of bacterial 16S rRNA gene were generated with the Illumina HiSeq 2000 platform, using DNA extracted from activated sludge in the two bioreactors. Three kinds of sequences (16S rRNA gene amplicons, 16S rRNA gene sequences obtained from metagenomic sequencing, and predicted proteins) were used to conduct taxonomic assignments. Specially, relative abundances of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) were analyzed. Compared with quantitative real-time PCR (qPCR), metagenomic sequencing was demonstrated to be a better approach to quantify AOA and AOB in activated sludge samples. It was found that AOB were more abundant than AOA in both reactors. Furthermore, the analysis of the metabolic profiles indicated that the overall patterns of metabolic pathways in the two reactors were quite similar (73.3% of functions shared). However, for some pathways (such as carbohydrate metabolism and membrane transport), the two reactors differed in the number of pathway-specific genes.