Genome Sequencing Reveals the Environmental Origin of Enterococci and Potential Biomarkers for Water Quality Monitoring

Enterococci are common members of the gut microbiome and their ease of culturing has facilitated worldwide use as indicators of fecal pollution of waters. However, enterococci were recently shown to persist in environmental habitats, often in the absence of fecal input, potentially confounding water quality assays. Toward resolving this issue and providing a more complete picture of natural enterococci diversity, 11 isolates of <i>Enterococcus faecalis</i> recovered from freshwater watersheds (environmental) were sequenced and compared to 59 available enteric genomes. Phenotypically and phylogenetically the environmental <i>E. faecalis</i> were indistinguishable from their enteric counterparts. However, distinct environmental- and enteric-associated gene signatures, encoding mostly accessory nutrient utilization pathways, were detected among the variable genes. Specifically, a nickel uptake operon was over-represented in environmental genomes, while genes for utilization of sugars thought to be abundant in the gut such as xylose were over-represented in enteric genomes. The distribution and phylogeny of these identified signatures suggest that ancestors of <i>E. faecalis</i> resided in extra-enteric habitats, challenging the prevailing commensal view of enterococci ecology. Thus, habitat-associated gene content changes faster than core genome phylogeny and may include biomarkers for reliably detecting fecal contaminants for improved microbial water quality monitoring

New Perspectives on Microbial Community Distortion after Whole-Genome Amplification

<div><p>Whole-genome amplification (WGA) has become an important tool to explore the genomic information of microorganisms in an environmental sample with limited biomass, however potential selective biases during the amplification processes are poorly understood. Here, we describe the effects of WGA on 31 different microbial communities from five biotopes that also included low-biomass samples from drinking water and groundwater. Our findings provide evidence that microbiome segregation by biotope was possible despite WGA treatment. Nevertheless, samples from different biotopes revealed different levels of distortion, with genomic GC content significantly correlated with WGA perturbation. Certain phylogenetic clades revealed a homogenous trend across various sample types, for instance Alpha- and Betaproteobacteria showed a decrease in their abundance after WGA treatment. On the other hand, <i>Enterobacteriaceae</i>, an important biomarker group for fecal contamination in groundwater and drinking water, were strongly affected by WGA treatment without a predictable pattern. These novel results describe the impact of WGA on low-biomass samples and may highlight issues to be aware of when designing future metagenomic studies that necessitate preceding WGA treatment.</p></div

Individual analysis of samples from biotope nitrogen biofilm and sludge: NMDS (left panel) and differentiating taxa (right panel).

<p>N (nitrogen biofilm). NMDS analysis shows a separation of samples with high WGA treatment along NMDS1 axis (stress: 0.0628). Bargraph depicts the number of different taxa passing certain statistical tests. All test were corrected for false positives using the Benjamini-Hochberg correction. <i>all</i>: the total number of eOTUs considered for this analysis (called present in at least one of the N samples); <i>pT_PvsL</i>: Number of taxa that were significantly different between pre-WGA and low-WGA treatment using a paired t-test; <i>pT_PvsH</i>: Number of taxa that were significantly different between pre-WGA and high-WGA treatment using a paired t-test; <i>pT_LvsH</i>: Number of taxa that were significantly different between low-WGA and high-WGA treatment using a paired t-test; <i>cor_P</i>.<i>L</i>.<i>H</i>: Number of taxa that showed a significant correlation with the samples in the order pre-low-high using a Pearson correlation; <i>cor_P</i>.<i>H</i>.<i>L</i>: Number of taxa that showed a significant correlation with the samples in the order pre-high-low using a Pearson correlation. S (sludge). NMDS analysis shows a separation of samples with high WGA treatment along NMDS1 axis (stress: 0.0785). Bargraph depicts the number of different taxa passing certain statistical tests. All test were corrected for false positives using the Benjamini-Hochberg correction. Bargraph labels are according to N (see above).</p

Determining Hot Spots of Fecal Contamination in a Tropical Watershed by Combining Land-Use Information and Meteorological Data with Source-Specific Assays

The objective of this study was to combine knowledge of environmental, topographical, meteorological, and anthropologic factors in the Río Grande de Arecibo (RGA) watershed in Puerto Rico with information provided by microbial source tracking (MST) to map hot spots (i.e., likely sources) of fecal contamination. Water samples were tested for the presence of human and bovine fecal contamination in addition to fecal indicator bacteria and correlated against several land uses and the density of septic tanks, sewers, and latrines. Specifically, human sources were positively correlated with developed (<i>r</i> = 0.68), barren land uses (<i>r</i> = 0.84), density of septic tanks (<i>r</i> = 0.78), slope (<i>r</i> = 0.63), and the proximity to wastewater treatment plants (WWTPs) (<i>r</i> = 0.82). Agricultural land, the number of upstream National Pollution Discharge Elimination System (NPDES) facilities, and density of latrines were positively associated with the bovine marker (<i>r</i> = 0.71; <i>r</i> = 0.74; and <i>r</i> = 0.68, respectively). Using this information, we provided a hot spot map, which shows areas that should be closely monitored for fecal contamination in the RGA watershed. The results indicated that additional bovine assays are needed in tropical regions. We concluded that meteorological, topographical, anthropogenic, and land cover data are needed to evaluate and verify the performance of MST assays and, therefore, to identify important sources of fecal contamination in environmental waters

Taxonomic distribution of abundance shifts.

<p>Phylogenetic tree of families with significant abundances changes in pre versus low, pre versus high or low versus high WGA treatment samples (paired student’s t-test). Please note that sample type T (treated biosolid) did not reveal any families with significant changes. Dark blue indicates a significantly negative delta, while dark red indicates a significantly positive delta. The amount of significant t-tests per family is displayed in the ring “score”, whereas dark grey indicates 7 out of 15 tests were significant, while white indicates only 1 difference (families without significant changes are not displayed). The inner rings show barplots for 16S rRNA gene GC content and genomic GC content.</p

Various effects of WGA treatment of samples from five biotopes investigated by ordination analysis.

<p>NMDS analysis of whole community profile of 31 different samples from 5 different biotopes. All samples were either without treatment (“pre”), treated with “low” WGA (0.4 ng/μl) or with “high” WGA (4.0 ng/μl) resulting in 91 samples in total colored by biotope. For groundwater (C), sludge (S), and treated biosolid (T), the different WGA treatments of each individual sample are connected by grey lines (from “pre” to “low” to “high”). When a separation of samples based on the different WGA treatments was observed [biotopes drinking water (D) and nitrogen biofilms (N)], samples were not connected. Stress: 0.0983, number of OTUs: 1491.</p

Correlation analysis between OTU abundance shift and genomic properties on family level.

<p>A: Heatmap of correlation coefficients (Spearman’s rho) between the OTU abundance shift and the mean per family of four genomic properties. Each row contains the correlation coefficients for OTU abundance shifts between two WGA treatments (high vs. pre, high vs. low or low vs. pre) for one sample. Each column shows the correlation coefficients for one genomic property (Primer score, Genome size, 16S GC and Genomic GC). Dark turquoise color indicates a negative correlation, dark red color a positive correlation. Significant p-values are given as numbers in each cell after applying Benjamini-Hochberg correction. B: As an example of the correlation of genomic GC and abundance changes, significantly different families for sample type N (pre vs. hi, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124158#pone.0124158.g002" target="_blank">Fig 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124158#pone.0124158.s002" target="_blank">S2 Fig</a>) were used. The plot shows a significant negative correlation, as do all other analyses of the samples for significantly different families.</p

Detection of Fecal Bacteria and Source Tracking Identifiers in Environmental Waters Using rRNA-Based RT-qPCR and rDNA-Based qPCR Assays

In this study, we evaluated the use of RT-qPCR assays targeting rRNA gene sequences for the detection of fecal bacteria in water samples. We challenged the RT-qPCR assays against RNA extracted from sewage effluent (<i>n</i> = 14), surface water (<i>n</i> = 30), and treated source water (<i>n</i> = 15) samples. Additionally, we applied the same assays using DNA as the qPCR template. The targeted fecal bacteria were present in most of the samples tested, although in several cases, the detection frequency increased when RNA was used as the template. For example, the majority of samples that tested positive for <i>E. coli</i> and <i>Campylobacter</i> spp. in surface waters, and for human-specific <i>Bacteroidales</i>, <i>E. coli</i>, and <i>Enterococcus</i> spp. in treated source waters were only detected when rRNA was used as the original template. The difference in detection frequency using rRNA or rDNA (rRNA gene) was sample- and assay-dependent, suggesting that the abundance of active and nonactive populations differed between samples. Statistical analyses for each population exhibiting multiple quantifiable results showed that the rRNA copy numbers were significantly higher than the rDNA counterparts (<i>p</i> < 0.05). Moreover, the detection frequency of rRNA-based assays were in better agreement with the culture-based results of <i>E. coli</i>, intestinal enterococci, and thermotolerant <i>Campylobacter</i> spp. in surface waters than that of rDNA-based assays, suggesting that rRNA signals were associated to active bacterial populations. Our data show that using rRNA-based approaches significantly increases detection sensitivity for common fecal bacteria in environmental waters. These findings have important implications for microbial water quality monitoring and public health risk assessments

Detection of Fecal Bacteria and Source Tracking Identifiers in Environmental Waters Using rRNA-Based RT-qPCR and rDNA-Based qPCR Assays

In this study, we evaluated the use of RT-qPCR assays targeting rRNA gene sequences for the detection of fecal bacteria in water samples. We challenged the RT-qPCR assays against RNA extracted from sewage effluent (<i>n</i> = 14), surface water (<i>n</i> = 30), and treated source water (<i>n</i> = 15) samples. Additionally, we applied the same assays using DNA as the qPCR template. The targeted fecal bacteria were present in most of the samples tested, although in several cases, the detection frequency increased when RNA was used as the template. For example, the majority of samples that tested positive for <i>E. coli</i> and <i>Campylobacter</i> spp. in surface waters, and for human-specific <i>Bacteroidales</i>, <i>E. coli</i>, and <i>Enterococcus</i> spp. in treated source waters were only detected when rRNA was used as the original template. The difference in detection frequency using rRNA or rDNA (rRNA gene) was sample- and assay-dependent, suggesting that the abundance of active and nonactive populations differed between samples. Statistical analyses for each population exhibiting multiple quantifiable results showed that the rRNA copy numbers were significantly higher than the rDNA counterparts (<i>p</i> < 0.05). Moreover, the detection frequency of rRNA-based assays were in better agreement with the culture-based results of <i>E. coli</i>, intestinal enterococci, and thermotolerant <i>Campylobacter</i> spp. in surface waters than that of rDNA-based assays, suggesting that rRNA signals were associated to active bacterial populations. Our data show that using rRNA-based approaches significantly increases detection sensitivity for common fecal bacteria in environmental waters. These findings have important implications for microbial water quality monitoring and public health risk assessments

Detection of Fecal Bacteria and Source Tracking Identifiers in Environmental Waters Using rRNA-Based RT-qPCR and rDNA-Based qPCR Assays

In this study, we evaluated the use of RT-qPCR assays targeting rRNA gene sequences for the detection of fecal bacteria in water samples. We challenged the RT-qPCR assays against RNA extracted from sewage effluent (<i>n</i> = 14), surface water (<i>n</i> = 30), and treated source water (<i>n</i> = 15) samples. Additionally, we applied the same assays using DNA as the qPCR template. The targeted fecal bacteria were present in most of the samples tested, although in several cases, the detection frequency increased when RNA was used as the template. For example, the majority of samples that tested positive for <i>E. coli</i> and <i>Campylobacter</i> spp. in surface waters, and for human-specific <i>Bacteroidales</i>, <i>E. coli</i>, and <i>Enterococcus</i> spp. in treated source waters were only detected when rRNA was used as the original template. The difference in detection frequency using rRNA or rDNA (rRNA gene) was sample- and assay-dependent, suggesting that the abundance of active and nonactive populations differed between samples. Statistical analyses for each population exhibiting multiple quantifiable results showed that the rRNA copy numbers were significantly higher than the rDNA counterparts (<i>p</i> < 0.05). Moreover, the detection frequency of rRNA-based assays were in better agreement with the culture-based results of <i>E. coli</i>, intestinal enterococci, and thermotolerant <i>Campylobacter</i> spp. in surface waters than that of rDNA-based assays, suggesting that rRNA signals were associated to active bacterial populations. Our data show that using rRNA-based approaches significantly increases detection sensitivity for common fecal bacteria in environmental waters. These findings have important implications for microbial water quality monitoring and public health risk assessments