The effect of Paenibacillus on IDEXX Enterolert results from freshwater stream environments.

Enterolert, a fluorogenic substrate test, is used as a quantitative method for determining freshwater concentrations of Enterococcus for water quality indicators. However, there is some evidence from recent studies suggesting that Enterolert may not suppress false positives due to pollution sources in waterbodies. In this study, we evaluated this method by analyzing field water and sediment samples from four freshwater streams. We also performed a laboratory microcosm study from two of the stream sediments. The Enterolert method was investigated by phenotypic and genomic analyses for accuracy of isolating and quantifying Enterococcus and/or Streptococcus. Additionally, we tested isolates from Enterolert panels for antibiotic resistance. Results from the field and microcosm studies from initial to final time points indicated that false positives were predominantly Paenibacillus spp. and other non-fecal indicator bacteria. Furthermore, the microcosm study indicated shifts from lactic acid to non-lactic acid bacteria between initial to final time points, but Enterococcus concentrations from Enterolert panels remained stable for the duration of the study for both stream sediments. Antibiotic resistance indicated no distinct pattern of resistance or susceptibility to a suite of antibiotics. However, all isolates tested were resistant to bacitracin and nalidixic acid. In conclusion, we found that Enterolert was not exclusively selective for Enterococcus from freshwater environments and that sediment and polluted waterbodies have the potential to skew the presumed concentrations. More research is needed to evaluate the effectiveness and selectivity of the medium used for the fluorogenic substrate test for Enterococcus enumeration.

QT-AMP: Sequencing PCR amplicons from Quanti-Tray wells to analyze enterococci communities.

Enterococcus is ubiquitous in human feces and has been adopted as a useful indicator of human fecal pollution in water. Although regular enterococci monitoring only examines their numbers, identifying human-specific Enterococcus species or genotypes could help discriminate human fecal contamination from other environmental sources. We documented a new approach to characterize enterococci using a high-throughput 16S rRNA gene amplicon sequencing platform from Quanti Trays after following the counting of the most probable numbers of enterococci. We named this method QT-AMP (Quanti-Tray-based amplicon sequencing). We tested surface water samples collected from three rivers in southwest Florida. We detected 11 Enterococcus species from 45 samples in 1.1 million sequence reads. The method detected three rare species and eight cosmopolitan species (Enterococcus faecalis, E. faecium, E. casseliflavus, E. hirae, E. mundtii, E. gallinarum, E. avium, and E. durans) which have been commonly documented in previous studies. The approximate detection level of QT-AMP was four orders of magnitude higher than regular 16S rRNA gene amplicon sequencing. The current Enterolert MPN method only provides quantitative information but now we can look into the relative abundance of Enterococci species composition by accompanying Illumina sequencing. This QT-AMP could be a useful tool to streamline the quantification and identification of enterococci and could be used in various water management projects and human health risk assessment.

False-positive enterococci counts in seawater with the IDEXX Enterolert-E most probable number technique caused by Bacillus licheniformis.

Enterolert-E is an easy-to-use method for the enumeration of enterococci in water samples as an indicator of fecal pollution. This most probable number technique replaced the laborious and more time-consuming MEA-BEA plating method, and it is used extensively in ballast water testing and monitoring. In spring 2018, the Control Union Water ballast water test facility measured high enterococci concentrations in Wadden Sea water without any correlation with polluted freshwater input. By isolating bacteria from samples incubated in Enterolert-E culture medium, followed by analyses of colony morphology and DNA, it is shown that these erroneously high concentrations were caused by Bacillus licheniformis, a gram-positive rod-shaped chlorine-resistant bacterium. It is concluded that control analyses or the MEA-BEA method or dilution to reduce salinity must be performed when high enterococci concentrations are measured in water samples that are not suspected to be polluted.

Citizen science-based water quality monitoring: Constructing a large database to characterize the impacts of combined sewer overflow in New York City.

To protect recreational water users from waterborne pathogen exposure, it is crucial that waterways are monitored for the presence of harmful bacteria. In NYC, a citizen science campaign is monitoring waterways impacted by inputs of storm water and untreated sewage during periods of rainfall. However, the spatial and temporal scales over which the monitoring program can sample are constrained by cost and time, thus hindering the construction of databases that benefit both scientists and citizens. In this study, we first illustrate the scientific value of a citizen scientist monitoring campaign by using the data collected through the campaign to characterize the seasonal variability of sampled bacterial concentration as well as its response to antecedent rainfall. Second, we examine the efficacy of the HyServe Compact Dry ETC method, a lower cost and time-efficient alternative to the EPA-approved IDEXX Enterolert method for fecal indicator monitoring, through a paired sample comparison of IDEXX and HyServe (total of 424 paired samples). The HyServe and IDEXX methods return the same result for over 80% of the samples with regard to whether a water sample is above or below the EPA's recreational water quality criteria for a single sample of 110 enterococci per 100mL. The HyServe method classified as unsafe 90% of the 119 water samples that were classified as having unsafe enterococci concentrations by the more established IDEXX method. This study seeks to encourage other scientists to engage with citizen scientist communities and to also pursue the development of cost- and time-efficient methodologies to sample environmental variables that are not easily collected or analyzed in an automated manner.

Factors affecting the relationship between quantitative polymerase chain reaction (qPCR) and culture-based enumeration of Enterococcus in environmental waters.

AIMS: To determine the extent to which discrepancies between qPCR and culture-based results in beach water quality monitoring can be attributed to: (i) within-method variability, (ii) between-method difference within each method class (qPCR or culture) and (iii) between-class difference. METHODS AND RESULTS: We analysed 306 samples using two culture-based (EPA1600 and Enterolert) and two qPCR (Taqman and Scorpion) methods, each in duplicate. Both qPCR methods correlated with EPA1600, but regression analyses indicated approximately 0·8 log10 unit overestimation by qPCR compared to culture methods. Differences between methods within a class were less than half of this and were minimal for between-replicate within a method. Using the 104 Enterococcus per 100 ml management decision threshold, Taqman qPCR indicated the same decisions as EPA1600 for 87% of the samples, but indicated beach posting for unhealthful water when EPA1600 did not for 12% of the samples. After accounting for within-method and within-class variability, 8% of the samples exhibited true between-class discrepancy where both qPCR methods indicated beach posting while both culture methods did not. CONCLUSION: Measurement target difference (DNA vs growth) accounted for the majority of the qPCR-vs-culture discrepancy, but its influence on monitoring application is outweighed by frequent incorrect posting with culture methods due to incubation time delay. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first study to quantify the frequency with which culture-vs-qPCR discrepancies can be attributed to target difference - vs - method variability.