Global Distribution of Human-Associated Fecal Genetic Markers in Reference Samples from Six Continents

Numerous bacterial genetic markers are available for the molecular detection of human sources of fecal pollution in environmental waters. However, widespread application is hindered by a lack of knowledge regarding geographical stability, limiting implementation to a small number of well-characterized regions. This study investigates the geographic distribution of five human-associated genetic markers (HF183/BFDrev, HF183/BacR287, BacHum-UCD, BacH, and Lachno2) in municipal wastewaters (raw and treated) from 29 urban and rural wastewater treatment plants (750–4 400 000 population equivalents) from 13 countries spanning six continents. In addition, genetic markers were tested against 280 human and nonhuman fecal samples from domesticated, agricultural and wild animal sources. Findings revealed that all genetic markers are present in consistently high concentrations in raw (median log10 7.2–8.0 marker equivalents (ME) 100 mL–1) and biologically treated wastewater samples (median log10 4.6–6.0 ME 100 mL–1) regardless of location and population. The false positive rates of the various markers in nonhuman fecal samples ranged from 5% to 47%. Results suggest that several genetic markers have considerable potential for measuring human-associated contamination in polluted environmental waters. This will be helpful in water quality monitoring, pollution modeling and health risk assessment (as demonstrated by QMRAcatch) to guide target-oriented water safety management across the globe.


Collection and processing of fecal DNA samples from Reischer et al. 2013
The fecal DNA samples used in the investigation of assay specificity and sensitivity were collected in 16 countries: Argentina, Australia, Austria, Ethiopia, Germany, Hungary, Korea, Nepal, Nether-lands, Romania, Spain, Sweden, Tanzania, Uganda, United Kingdom, and the United States of America and extracted in the course of a previous study 1  DNA concentration and quality were subsequently determined using a NanoDrop ND 1000 UV spectrophotometer (Thermo Fisher Scientific Inc., Vienna, Austria). DNA extracts with concentrations of > 30 ng µL −1 were diluted 10-fold, in order to bring the DNA concentration in all extracts to between 3 and 30 ng µL −1 for further analysis. The majority of the DNA concentrations in the purified extracts were between 5 and 12 ng µL -1 1

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In this study, the AllBac marker through qPCR assay was used to assess the quality of DNA extracts and to estimate total Bacteroidetes concentrations in the samples. PCR inhibition was assessed by determining the AllBac marker concentrations in the 1:4 and 1:16 sample dilutions. Extracts with matching concentrations in the two dilutions (2 cycles difference in threshold cycle value in qPCR) were judged free of PCR inhibitory substances in the 1:4 dilution. All qPCR runs in this study had a PCR efficiency of between 92 and 105%, and the no-template controls were consistently negative 1 .
All sample DNA extracts were diluted 4-fold and measured in duplicate with the tested source-associated assays. Results were reported as marker copy numbers per reaction volume in this dilution. Results <1 copy per reaction were set to 0. All the results in this study are reported directly as qPCR copy numbers in the 4-fold dilution, and analysis was focused on comparisons between distributions of concentrations in groups of samples 1 .

Modelling demonstration for the MST marker data set using QMRAcatch
For the demonstration, we used the novel open access computational tool QMRAcatch (http://www.waterandhealth.at/index.php?id=82&L=1), which combines catchment-based microbial water quality modelling with quantitative microbial risk assessment 2 .
The QMRAcatch model domain encompasses various human/animal contamination sources (domestic wastewater, faecal pollution from animals, birds and humans). Best available data on faecal indicator bacteria (FIB), genetic microbial source tracking (MST) marker, and reference pathogens can be combined to support source-targeted calibration and verification of the model. Additional reference pathogens can be selected (based on assumed source concentrations from literature) to support cross-comparison of pathogen risks. The importance of critical factors and/or future changes (e.g. hydrological/climatic situation, animal/human pollution sources, epidemiology) can be considered by scenario analysis 3 . Results inform about sustainable catchment protection measures and required pathogen reduction targets (log-reductions of pathogens during treatment/disinfection) to meet defined health-based targets (based on a maximum tolerable infection risk). In addition to drinking water safety management, infection risks associated during recreation/bathing activities can also be considered by the software tool for the investigated environment 3 .
QMRAcatch was previously applied at the River Danube for simulating entero-and norovirus concentrations from human-associated fecal pollution sources and predicting the required virus log-reduction for safe drinking water production during river bank filtration and final disinfection 3 . The pooled HF183/BacR287 MST marker concentrations in raw and treated wastewater (n=17) at two rural WWTP were used as input values for the model simulations.
These data were analysed from 24-hour volume proportional composite samples by 4 . The model calibration procedure was followed as described by Derx et al. (2016) 3 . The same S7 procedure was then followed using the HF183/BacR287 MST marker concentrations in raw and treated wastewater from rural areas (data set from this paper, n=18). After model calibration, the observed and simulated mean concentrations of raw and treated wastewater, and in the Danube river (at sampling site 2017 3 ) highly agreed with the original simulations based on the Austrian WWTP data set ( Figure S8). The maximum biases of the mean simulated concentrations were smaller than 0.3 log 10 [ME*L -1 ] for both data sets. The adjusted parameters during model calibration are shown in Table S6.   Abbreviations: DNQ, samples that do not meet the quality criteria and were not used. Abbreviations: DNQ, samples that do not meet the quality criteria and were not used. S11