Identification of reliable marker genes for the detection of canine fecal contamination in sub-tropical Australia

Journal Pre-proof Identification of reliable marker genes to identify canine fecal contamination in sub-tropical Australia

Pradip Gyawali, Kerry Hamilton, Sayalee Joshi, David Aster, Warish Ahmed PII:

S0048-9697(20)30756-7

DOI:

https://doi.org/10.1016/j.scitotenv.2020.137246

Reference:

STOTEN 137246

To appear in:

Science of the Total Environment

Received date:

11 January 2020

Revised date:

9 February 2020

Accepted date:

9 February 2020

Please cite this article as: P. Gyawali, K. Hamilton, S. Joshi, et al., Identification of reliable marker genes to identify canine fecal contamination in sub-tropical Australia, Science of the Total Environment (2020), https://doi.org/10.1016/j.scitotenv.2020.137246

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© 2020 Published by Elsevier.

Journal Pre-proof

Identification of reliable marker genes to identify canine fecal contamination in sub-tropical Australia

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Pradip Gyawalia, Kerry Hamiltonb,c, Sayalee Joshib,c , David Asterd, Warish Ahmede

Institute of Environmental Science and Research Ltd (ESR), Porirua, 5240, New

The School of Sustainable Engineering and the Built Environment, Arizona State

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b

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Zealand

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University, 660S College Ave, Tempe, AZ 85281, USA c

The Biodesign Institute Center for Environmental Health Engineering, Arizona State

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University, 1001 S McAlister Ave, Tempe, AZ 85281, USA Department of Agriculture and Fisheries, Ecosciences Precinct, 41 Boggo Road,

CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD

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Dutton Park, QLD 4102, Australia

4102, Australia

Running title: Canine fecal contamination in water

Corresponding author. Warish Ahmed. Mailing address: Ecosciences Precinct, 41 Boggo Road, Dutton Park 4102, QLD, Australia. Tel.: +617 3833 5582; E-mail address: [email protected]

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Journal Pre-proof Abstract Animal fecal contamination in aquatic environments is a major source of zoonotic diseases in humans. While concerns are focused on livestock, companion animals such as dogs can also be a source of a wide range of zoonotic pathogens. Therefore, identification of dog or canine fecal contamination in aquatic environments is important for mitigating fecal contamination risks. In this study, host-sensitivity and specificity of four canine fecal-associated marker genes were evaluated by analyzing 30 canine and 240 non-canine fecal samples. The application of these markers was also tested in water from an urban river under dry weather

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conditions. The host sensitivity values of the Bacteroides BacCan-UCD, DogBact, DF113 and DF418 were 1.00, 0.90, 0.83, and 0.90, respectively. The host specificity value of the BacCan-UCD, DogBact, DF113 and DF418 were 0.87, 0.98, 0.83, and 0.59, respectively. The

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mean concentrations of DF418 were highest (7.82 ± 1.13 log10 GC/g of feces) followed by

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BacCan-UCD (7.61 ± 1.06 log10 GC/g) and DogBact (7.15 ± 0.92 log10 GC/g). The mean concentration of DF113 (5.80 ± 1.25 log10 GC/g) was 1.5 to 2.5 orders of magnitude lower

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than the other marker genes. The DogBact marker gene was not detected in any other animal feces other than a small number of untreated sewage samples. The BacCan-UCD marker

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gene cross-reacted with cat, chicken, and pig fecal samples, while the DF113 marker gene cross-reacted with cat, chicken, cattle fecal and untreated sewage samples. The DF418

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marker gene was detected in all sewage and animal feces and deemed not suitable for

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canine fecal contamination tracking in sub-tropical Australia. Canine fecal contamination was infrequently detected in environmental water samples. Based on the results obtained in this study, we recommend that at least two canine feces-associated marker genes should be used in field studies.

Keywords: Microbial source tracking; fecal contamination; marker gene; host specificity; host sensitivity; zoonotic pathogens; urban water

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Journal Pre-proof Introduction Population growth, urbanization, and climate change have contributed to increasing sources of nonpoint fecal contamination due to larger volumes of waste production contributing to increased runoff into aquatic environments. Human health risks can occur when fecal materials from animals and humans are not sufficiently managed and there is a potential for exposure to pathogens. Animal feces constitute a greater amount of fecal material globally than human fecal waste, and exposure to animal feces has been identified as a key route of contamination in the environment (Penakalapati et al., 2017). Animal feces can be a source of zoonotic pathogens and fecal indicator bacteria (FIB). While

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concerns are typically devoted to agricultural animals, companion animals such as dogs and cats can also be a source of FIB and zoonotic pathogens in the environment. Approximately 38% of Australian

rates of pet ownership globally (RSPCA, 2019).

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households own a dog, the most commonly owned pet, and overall Australia has one of the highest

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Canine feces have been shown to contain pathogens such as Campylobacter jejuni (Damborg et al., 2004), Salmonella spp., (Hackett and Lappin, 2003), pathogenic E. coli (Johnson et al., 2001),

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antibiotic-resistant bacteria (ARB) (LaLonde-Paul et al., 2018), Cryptosporidium parvum (Hackett and Lappin, 2003), Ancylostoma caninum (Hackett and Lappin, 2003), and other human parasites such

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as Toxoplasma spp. and helminths (Salb et al., 2008; Penakalapati et al. 2017). Outbreaks have

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been associated with exposure to puppies (Campagnolo et al., 2016); a multidrug-resistant C. jejuni outbreak was linked to exposure to puppies among 118 people in the USA from 2016-2018

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(Montgomery et al., 2018), and cases of human brucellosis have been associated with exposure to canine feces (Johnson et al., 2018; Hensel et al., 2018). Although a specific exposure pathway was not identified in these cases, the US Centers for Disease Control (CDC) and Prevention recommends that pet owners maintain hygienic practices including proper disposal and handwashing when handling canine urine, feces, or other wastes, keeping home surfaces clean, and avoiding child handto-mouth contacts after contact with animals (CDC, 2019). Contamination of water sources, soil, food, human hands, transfer via vectors, and contamination of fomites from animal feces have all been identified as potential exposure routes (Penakalapati et al., 2017). Identifying sources of fecal contamination in the environment is crucial to implementing risk management approaches. Comparatively lesser attention has been devoted to quantifying the impacts of companion animals as sources of fecal contamination compared to sewage and livestock.

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Journal Pre-proof Human health risks differ depending on the source of fecal material introduced into the environment, as different fecal sources are associated with different pathogens (Soller et al., 2015). However, monitoring exclusively for FIB will not provide information about the source of contamination in the environment, limiting the ability to target risk mitigation efforts, for example implementing controls at a farm or disallowing pets at beaches. Identifying major contamination source(s) for a given watershed can allow for more targeted protections for local waterways that reduce human health risks. Quantitative polymerase chain reaction (qPCR) based microbial source tracking (MST) marker genes are frequently used to determine the sources of fecal contamination in aquatic

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environments (Kildare et al., 2007; Green et al., 2014; Stachler et al., 2017; Feng et al., 2018). However, before field application, the assay requires validation to determine its precision and accuracy by determining performance characteristics such as host sensitivity,

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host specificity and their abundance in the target animal species (Harwood et al., 2014;

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Ahmed et al., 2019). This is because performance characteristics of a marker gene may vary geographically and should always be verified before field testing in a new geographical area

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(Boehm et al., 2013; Ahmed et al., 2019).

MST can help to manage canine fecal pollution by identifying impacted urban areas or

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recreational beaches. There are a limited number of MST canine marker genes currently available including Bacteroides BacCan-UCD (Kildare et al., 2007), Bacteroides DogBact

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(Dick et al., 2005; Sinigalliano et al., 2010), canine eukaryotic mitochondrial DNA (mtDNA)

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(Caldwell and Levine, 2009), DF113 and DF418 (Hussein et al., 2014) and Bacteroides DG3, Lachnospiraceae DG37 and Bacteroides DG72 (Green et al., 2014). The host specificity and sensitivity of these canine maker genes except BacCan-UCD have not been evaluated thoroughly outside USA or beyond the laboratories that developed each assay. The objective of this study was to compare the host specificity, sensitivity, and concentration of four canine feces-associated marker genes across a large number of animal fecal samples in a subtropical region of Australia. We also collected environmental water samples during three dry weather events and determined the concentrations of selected canine feces-associated markers along with E. coli to determine the general contamination level and potential relative impacts of canine-feces on water quality. This study will provide valuable information to water quality managers and regulators for

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Journal Pre-proof choosing appropriate canine feces-associated marker genes for identifying canine fecal contamination in catchment and recreational waters.

Materials and methods Fecal sample collection To determine the host sensitivity and host specificity of the canine feces-associated marker genes, 30 canine (dog) and 240 non-canine fecal DNA samples were used from a recent study (Ahmed et al., 2019). Individual cat fecal samples from the veterinary hospital located at

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the University of Queensland, Gatton Campus and six different households were obtained. Individual chicken fecal samples were collected from the backyard of six different households and a chicken processing farm in Brisbane, whereas individual cow fecal samples were

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collected from five farms located on the outskirts of Brisbane. Individual deer fecal samples

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were sourced from a deer Sanctuary in Mount Samson, Brisbane on two separate occasions. Individual canine fecal samples from three dog parks and a veterinary hospital were also

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obtained. Individual emu fecal samples were collected from an emu farm on two separate occasions. Individual horse fecal samples were collected from two different horse

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racecourses. Individual kangaroo fecal samples were collected from a sanctuary and from the wild. Individual pig fecal samples were collected from two abattoirs. Individual sheep fecal

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samples were collected from the veterinary hospital located at University of Queensland,

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Gatton Campus and three farms located in Toowoomba region. Thirty composite untreated sewage samples representing human hosts were collected from the influent of three sewage treatment plants (STPs) from Brisbane, Perth, and Tasmania. Background information on fecal samples are presented in Table 1.

Environmental water sampling Water samples were collected from Brisbane River (BR) and Oxley Creek (OC) in Brisbane, Australia (Fig. 1). Oxley Creek is a tributary of BR and is tidally influenced. Thirteen sampling sites (BR1-BR13) were chosen along the entire length (i.e., 344 km) of the BR. In addition, one sampling site (OC1) was chosen in OC. Sampling sites (BR1-BR5) are sparsely populated with forested hills and grazing land. The middle and lower catchments (sites BR6-

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Journal Pre-proof BR13) are highly populated and characterized by industrial, residential and highly urban th

areas. From each site, samples were collected on three separate occasions between 15-26 May, 2019. All water samples were collected during dry weather events. The study area did

not receive any precipitation ten days prior water sampling. A total of 42 water samples were collected for monitoring E. coli and canine feces-associated marker genes. Water samples were collected in 500 mL sterilized PET bottles at approximately 30 cm below the water surface and transported on ice to the laboratory and tested within 16 h.

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Concentration of environmental water samples For qPCR analysis of E. coli 23S rRNA and canine feces-associated marker genes, 500 ml of each water sample was filtered through 0.45-μm pore size HAWP membrane (Millipore,

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Tokyo, Japan). In the case of membrane clogging during filtration, multiple membranes were

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DNA extraction

DNA was extracted from an aliquot of 250 mL of untreated sewage sample using the MO Bio

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PowerSoil DNA isolation kit (Mo Bio Laboratories, Carlsbad, CA) with minor modifications as described elsewhere (Ahmed et al., 2015). DNeasy PowerSoil kit was used to extract DNA

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from individual fecal samples. DNA concentrations were measured with a spectrophotometer

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(NanoDrop ND-1000, Thermo Scientific, Willmington, DE, USA). All DNA samples were stored at −80°C until further analysis.

PCR inhibition An experiment was conducted to determine the presence of PCR inhibitors in animal fecal and untreated sewage DNA samples using a Sketa22 qPCR assay (Haugland et al., 2005). DNA samples with a 2-quantification cycle (Cq) delay were considered to have potential PCR inhibitors (Ahmed et al., 2019). Samples with PCR inhibitors were subjected to a 10-fold dilution with TE buffer and reanalysed with the Sketa22 assay. PCR-uninhibited and 10-fold diluted (inhibition relieved) samples were used for qPCR analysis.

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Journal Pre-proof qPCR assays Previously published qPCR assays were used for the analysis of the E. coli 23S rRNA and canine feces associated marker genes (Dick et al., 2005; Kildare et al., 2007; Sinigalliano et al., 2010; Chern et al., 2011; Hussein et al., 2014). The primers and probes for each assay are shown in Supplementary Table S1 along with qPCR cycling parameters. gBlocks gene 6

fragments were used as to prepare qPCR standards, ranging from 10 to 1 GC/µL of DNA (Integrated DNA Technology, Coralville, IA). All qPCR amplifications were performed in 20 µL reaction mixtures using SsoAdvanced Universal Probes Supermix or SsoAdvanced Universal

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SYBR Green Supermix (Bio-Rad Laboratories, Richmond, CA). qPCR mixtures contained 10 µL of SsoAdvanced Universal Probes Supermix, 800 nM of forward primer, 800 nM of reverse primer, 80 nM probe (for E. coli 23S rRNA), 400 nM of forward primer, 400 nM of each

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reverse primer, 80 nM probe (for BacCan-UCD), 500 nM of forward primer, 500 nM of reverse

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primer, 200 nM probe (for DogBact) and 10 µL of SsoAdvanced Universal SYBR Green Supermix, 300 nM of forward primer and 300 nM of reverse primer (for DF113 and DF418)

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and 3 µL of template DNA. Melt curve analysis was performed for SYBR Green based assays. During melting curve analysis, the temperature was increased from 60°C to 95°C at

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approximately 2°C/min. Samples were considered as positive when the melting points were matched with the melting point of the standard curve amplification within a tolerance of 0.5ºC

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(Nutz et al., 2011). The qPCR assays were performed using a Bio-Rad CFX96 thermal cycler.

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All qPCR reactions were performed in triplicate. For each qPCR run, a series of standards (3 × 10 to 3 GC per reaction) and no template controls (n = 3) were included.

qPCR assays performance characteristics, lower limit of detection and quantification The qPCR standards were analysed to determine the amplification efficiencies (E) and the 2

correlation coefficient (R ) for each assay. The qPCR assay’s lower limit of detection (ALLOD) and quantification (ALLOQ) values were determined from Cq values of the standards. The qPCR ALLOD was defined as the number of copies that could be detected in 2 out of 3 qPCR assays, while the ALLOQ) was the number of copies that could be quantified in 2 out of 3 qPCR assays. Five separate standard curves were generated for each qPCR assay. The optimal reaction conditions were determined based on the following criteria: a slope factor of -

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Journal Pre-proof 3.09 to -3.59, corresponding to a PCR efficiency of 90 to 110%; a correlation coefficient > 0.98 (Bustin et al., 2009).

Quality control A reagent blank was included for each batch of DNA samples to ensure no carryover contamination occurred from DNA extraction reagents. No carryover contamination was observed in extracted DNA samples. To minimize qPCR contamination, DNA extraction and

Data analysis

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qPCR setup were performed in separate laboratories.

The host specificity and sensitivity values of the studied marker genes were determined as and host specificity =

, where TP is true positive (human

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follows: host sensitivity =

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fecal and untreated sewage samples were positive for sewage-associated marker genes), FN is false negative (human and untreated sewage samples were negative for the tested marker

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genes), TN is true negative (non-human fecal samples were negative for sewage-associated marker genes), and FP is false positive (non-human fecal samples were positive for the

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sewage-associated marker genes) (Stoeckel and Harwood 2007). Samples were considered

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quantifiable when the canine marker gene levels were above the qPCR ALLOQ. Samples that were below the ALLOQ and above the ALLOD levels and generated PCR amplification were

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considered as positive but not quantifiable. Samples below the ALLOD were considered as negative. Marker gene concentrations data were log10 transformed for statistical analysis One-way ANOVA with Tukey’s post-hoc test was conducted to determine the difference in gene copy numbers between the marker genes.

Results qPCR performance characteristics Four qPCR standard curves were analyzed to determine the qPCR performance characteristics for E. coli 23S rRNA, BacCan-UCD, DogBact, DF113 and DF418. The 6

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standard curves had a linear range of quantification from 3 × 10 to 3 × 10 GC/reaction. The range of qPCR efficiency, linearity, slope, and Y-intercept are shown in Supplementary Table

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Journal Pre-proof S2. These parameters were within the optimal recommended values as per the MIQE guidelines (Bustin et al., 2009). The mean coefficient of variation (CV) values for all assays were <6%. The qPCR ALLOD and ALLOQ values were determined to range from 3 to 30 GC/reaction for all qPCR assays. No carryover contamination was observed in method and reagent blank samples. No template controls did not show any amplification.

Host sensitivity and concentration of canine marker genes in canine fecal samples Host-sensitivity of four canine feces-associated marker genes (BacCan-UCD, DogBact,

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DF418 and DF113) were assessed using 30 individual canine fecal samples. The prevalence of BacCan-UCD was the highest (100%) and DF118 was the lowest (83%) among the marker genes tested. The host sensitivity values of the BacCan-UCD, DogBact, DF113 and DF418

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were 1.00, 0.90, 0.83 and 0.90, respectively (Table 2). The mean concentrations of DF418

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was the highest (7.82 ± 1.13 log10 GC/g of feces) followed by BacCan-UCD (7.61 ± 1.06 log10 GC/g) and DogBact (7.15 ± 0.92 log10 GC/g). The mean concentration of DF113 (5.80 ± 1.25

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log10 GC/g) was 1.5 to 2.5 orders of magnitude lower than the other marker genes (Table 2). One-way ANOVA with a Tukey’s post-hoc test indicated that the concentrations of DF418 in

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canine fecal samples were significantly (p >0.05) lower than BacCan-UCD, DogBact, and DF113. The concentration of all four marker genes were highly variable within the individual

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canine fecal samples. The concentrations of marker genes in canine fecal samples ranged

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between 4.68-9.20 log10 GC/g (BacCan-UCD), 5.26-8.67 log10 GC/g (DogBact), 3.66-8.23 log10 GC/g (DF113) and 4.13-9.48 log10 GC/g (DF448), respectively (Table 3). The highest level of sample to sample marker gene variation was observed in DF113 (5.35 log10 GC/g), followed by DF418 (5.25 log10 GC/g) and BacCan-UCD (4.52 log10 GC/g). The least variation was observed in the DogBact marker gene (3.41 log10 GC/g).

Host specificity and concentration of marker genes in non-canine fecal samples Of the 240 non-canine fecal samples tested, BacCan-UCD was detected and quantifiable in five cat, eight chicken, 12 pig and six untreated sewage samples. The concentrations of BacCan-UCD in cat, chicken and pig fecal samples ranged from 5.90 to 8.80 log10 GC/g (Fig. 2). Similarly, the concentrations of the BacCan-UCD in untreated sewage samples ranged

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Journal Pre-proof from 5.67 to 7.55 log10 GC/L. DF418 was the most frequently detected marker gene among non-canine fecal samples. Of the 240 non-canine fecal sample tested, one cat fecal and four sewage samples were positive for the DogBact marker gene. The concentration of DogBact in one of 10 cat fecal samples was below the ALLOQ. In all, four of 30 untreated sewage samples were positive and quantifiable for the DogBact marker gene. The concentrations of DogBact in these samples ranged from 4.65 to 5.77 log10 GC/L. The DF113 marker gene was detected in six cat, one chicken, 18 cow and 14 sewage samples, of which five cat, one chicken, 13 cow and seven sewage samples were

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quantifiable. The concentrations of DF113 in cat, chicken and cow fecal samples ranged from 4.14 to 4.72 log10 GC/g. Similarly, the concentrations of DF113 in untreated sewage samples ranged from 4.67 to 5.12 log10 GC/L. DF418 was the most frequently detected marker gene

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among non-canine fecal samples. This marker gene was detected in 142 non-canine fecal

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samples, of which 141 samples were quantifiable. These samples belonged to cat (n = 8), chicken (n = 8), cattle (n = 18), deer (n = 19), emu (n = 2), horse (n = 6), kangaroo (n = 28),

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pig (n = 9), sewage (n = 30) and sheep (n = 29). The concentrations of DF418 in animal fecal samples ranged from 5.12 to 10.7 log10 GC/g and sewage samples ranged from 7.54 to 10.2

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log10 GC/L. The host specificity values of the tested markers from the highest to the lowest were DogBact (0.98) > BacCan-UCD (0.87), DF113 (0.83) and DF418 (0.59) (Table 2).

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The mean concentrations of all four marker genes in pooled canine and non-canine

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fecal/untreated sewage samples are shown in Table 4. The mean concentrations of BacCanUCD in canine fecal samples were approximately one log10 higher than non-canine fecal samples. The mean concentrations of DogBact and DF113 in canine fecal samples were approximately two and one log10 greater than non-canine fecal samples, respectively. However, the concentrations of DF418 in canine fecal samples were approximately 1 log10 lower than non-canine fecal samples.

Prevalence and concentration of E. coli 23S rRNA and canine marker genes in river water samples As a proof-of-concept, dry weather water samples from Brisbane River and one of its tributaries were analysed quantitatively for the presence of E. coli and canine-feces

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Journal Pre-proof associated marker genes. The DF418 marker gene was omitted from the analysis considering its low host sensitivity and specificity values. The concentration of E. coli in water samples ranged from 2.51 to 4.89 log10 GC/100 mL with mean of 3.52 ± 0.55 GC/100 mL (Table 5). The concentrations of E. coli in each site were consistent throughout the study period. The concentrations of E. coli were greater in sites OC1 and BR8 compared to other sites. The prevalence of BacCan-UCD, DogBact and DF113 were low in BR water samples. Among the 42 water samples tested from 14 sites, 3 (7.14%) samples were positive for the canine marker genes. Water samples from site BR3 and BR8 collected on event 1 were both positive

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for the BacCan-UCD and DogBact marker genes. However, marker genes were not quantifiable suggesting very low level of dog fecal contamination was present in these samples. Sample from site BR3 collected on event 2 was positive for DF113 marker gene but

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not BacCan-UCD or DogBact.

Discussion

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In this study, we rigorously evaluated the host specificity and sensitivity of four canine fecesassociated marker genes by testing a large number of canine and non-canine fecal samples

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collected from a sub-tropical region of Australia. Previously, the performance characteristics of BacCan-UCD marker gene have been evaluated in Australia; however, DogBact, DF113

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developed.

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and DF418 have not been evaluated beyond the laboratory where the assays were

The host specificity values of the DogBact marker gene was 0.98 (a maximum value of 1.00) which is excellent (USEPA, 2005; Ahmed et al., 2016). Little is known regarding the host specificity of DogBact marker gene except for the Source Identification Protocol Project (SIPP) in California (Schriewer et al., 2013). In the SIPP study, the authors compared DogBact and BacCan-UCD marker genes in a multi-laboratory comparison study using DNA from fecal slurries originating from either one animal source or two combined sources (Boehm et al., 2013). When data from multiple laboratories were standardized by considering DNQ samples as non-detect for all singletons and doubletons, DogBact specificity value was 0.81, and when only singletons were considered, this value was 0.83. Several individual laboratories indicated the host specificity of DogBact can be as high as 0.94 (for singletons)

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Journal Pre-proof and 0.95 (for doubletons samples). The host specificity value obtained for DogBact in this study was similar to the SIPP study. The fundamental difference was that in this study, data were not normalized because it is a single laboratory study, whereas, SIPP study required data normalization due to highly variable results from multiple laboratories. DogBact marker gene was detected in one of ten cat and four of 30 untreated sewage samples. The concentration of DogBact in untreated sewage samples was two orders of magnitude lower than in canine fecal samples. This comparison needs to be interpreted with care as the units of measurement (g for feces vs. L for sewage) are different. The presence of

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DogBact marker genes in untreated sewage suggests resident flushing canine droppings in the toilet although it is not recommended nor is it an ideal method of disposal for canine feces in Australia. Since DogBact showed limited cross-reactivity, results obtained in the field study

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should be interpreted cautiously. In some cases, the application of multiple markers (Ahmed

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et al. 2019) or further confirmation that sewage is not the source of canine marker genes by testing sewage-associated marker genes may be needed. This will increase the confidence

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level that the sources of contamination have been correctly identified. A sanitary survey may also be useful to rule out the presence of contamination sources that may contribute canine

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marker gene to aquatic environments.

The host specificity value of the BacCan-UCD marker gene was 0.87, which can be

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considered quite useful for field studies (Ahmed et al., 2019). The BacCan-UCD marker gene

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was detected in cat, chicken, pig, and untreated sewage samples. Ahmed et al. (2008) reported the presence of BacCan-UCD in sewage, chicken, and pig fecal samples in subtropical Australia. Similarly, Silkie and Nelson (2009) reported the presence of BacCanUCD in cow, Canada goose, and horse feces (Silkie and Nelson, 2009). Kildare et al. (2007), the investigators who developed the assay, also noted the presence of BacCan-UCD in human and cat fecal samples (Kildare et al., 2007). The specificity value (0.87) of the BacCan-UCD obtained in this study was slightly greater than the original study (0.84) that developed this assay. Ahmed et al. (2008) reported high host specificity (0.96) of the BacCanUCD, whereas, Tambalo et al. (2012) reported very low specificity (0.31). Host specificity values from Ahmed et al. (2008) and Tambalo et al. (2012) cannot be compared to the current study as these two studies modified the BacCan-UCD assay, either by using only

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Journal Pre-proof conventional PCR and removing the second reverse primer (Ahmed et al., 2008), or by choosing a different annealing temperature and quencher (Tambalo et al., 2012). In this study, the host-specificity values of the DF113 and DF418 marker genes were 0.83 and 0.59, respectively. These values are much lower than the values (DF113 specificity 1.00 and DF418 specificity 0.92) reported by Hussein and colleagues (2014) in the UK. DF113 marker gene was detected in cat, cow, chicken and untreated sewage samples, whereas, DF418 was detected in almost all non-canine animal species tested in our study. Based on our results, we conclude that the DF418 marker gene may not be suitable for canine fecal

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pollution tracking in subtropical Australia. During qPCR analysis, we noticed significant primer dimers and non-specific products for both the SYBR green assays. It is recommended that the DF113 assay should be upgraded to a TaqMan qPCR assay for increased specificity and

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improved quantification, however, this was not within the scope of this study.

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The presence of canine-associated marker genes in non-canine fecal samples may not be an issue as long as the concentrations are low or non-quantifiable. This is because in the

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event of a canine fecal contamination event in aquatic environments, such low levels may be difficult to detect due to loss of the marker gene through water sample concentration and

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DNA extraction processes. Also, the dilution factor due to precipitation or tide will likely mask their detection (Zhang and Ishii, 2008). However, in this study, the concentrations of BacCan-

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UCD marker gene were quite high in non-canine fecal samples (i.e., cat, chicken, pig, and

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untreated sewage) suggesting possible colonization of this marker in the gut of certain animals (Shanks et al., 2008). It has been suggested that markers with host-sensitivity value of >0.20 may be useful (Senkbeil et al., 2019). Among the four marker genes tested, BacCan-UCD showed absolute sensitivity (1.00), while the sensitivity values of the remaining three markers were >0.80, suggesting high prevalence of the genetic markers in canine fecal samples. We also noticed that the marker gene concentration in individual canine fecal samples may vary up to four orders of magnitude and some canine fecal samples were PCR negative for the marker gene. The reasons for complete absence or an uneven distribution of canine marker genes in fecal samples are not clearly understood and require further investigation along with the collection of metadata such as diet, host age, animal health, and living conditions. All these factors may

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Journal Pre-proof influence the host sensitivity and stability of a marker gene within a host (Shanks et al., 2008; Feng et al., 2019). Since we did not collect any metadata, it is not possible to determine which factors may have contributed to the lower levels or negative detection of marker genes in canine feces. In this study, we also collected environmental water samples from an urban river (i.e., the Brisbane River) and quantified the concentrations of E. coli and canine feces-associated marker genes. Environmental water samples were collected during dry weather events when the fecal pollution load is supposed to be low. The concentrations of E. coli 23S rRNA genes

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were similar in water samples collected during three events within the same site. This is somewhat expected as the study sites did not receive any rainfall or stormwater runoff. The concentrations of E. coli in site OC1 were 1-2 orders of magnitude greater than other sites.

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This site is located downstream from a sewage treatment plant and during sampling, we

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noticed water quality signs stating poor microbial water quality. The concentrations of E. coli were also greater in site BR8, which is located near a storm drain. Storm drains are known to

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contribute elevated levels of FIB and fecal contamination into receiving waters (Ahmed et al., 2018). The detection frequency of canine feces-associated marker genes in water samples

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were low. Among the 14 sites, only two sites BR3 and BR8 were PCR positive for the canine marker, suggesting very low level of canine fecal contamination in these two sites. During

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sampling in site BR3, we noticed dogs near the sampling sites, which may have contributed

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to the canine marker in water samples. Site BR8 is located near a stormwater drain, and it is possible that people throw canine feces in such drains. Also, the occurrence of canine marker genes from sewage cannot be ruled out. Nonetheless, the results suggest that canine fecal contamination was not occurring in the studied river during the dry weather events. Such findings are important to water quality managers for prioritizing their efforts in improving water quality.

Conclusions 

The BacCan-UCD marker gene showed absolute host sensitivity (1.00) among the four markers tested. The sensitivity values (0.83 to 0.90) of the remaining markere were quite acceptable.

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Journal Pre-proof 

The DogBact marker gene had the highest host-specificity value (0.98 out of 1.00), followed by BacCan-UCD and DF113. This marker appears to be the most suitable for accurately identifying canine fecal contamination in subtropical Australia. Since the DogBact marker gene was detected in untreated sewage samples, this marker should be paired with BacCan-UCD marker gene in field studies to minimize false positive detection.

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DF418 had the lowest host-specificity and sensitivity value among all tested canine marker genes and deemed not suitable for tracking canine fecal contamination in

Canine fecal contamination was infrequently detected in dry weather water samples collected from Brisbane River, suggesting canine fecal contamination may not be an

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issue impacting the water quality of the studied river during dry weather periods.

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subtropical Australia.

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Ahmed, W., Powell, D., Goonetilleke, A., Gardner, T., 2008. Detection and source identification of faecal pollution in non-sewered catchment by means of host-specific molecular markers. Water Sci. Technol. 58 (3), 579-586. Ahmed, W., Sidhu, J.P.S., Smith, K., Beale, D.J., Gyawali, P., Toze, S., 2015.Distributions of fecal markers in wastewater from different climatic zones for human fecal pollution tracking in Australian surface waters. Appl. Environ. Microbiol. 82 (4), 1316-1323. Ahmed, W., Hughes, B., Harwood, V.J., 2016. Current status of marker genes of Bacteroides and related taxa for identifying sewage pollution in environmental waters. Water. 8, 231. Ahmed, W., Zhang, Q., Lobos, A., Senkbeil, J., Sadowsky, M.J., Harwood, V.J., Saeidi, N., Marinoni, O., Ishii, S., 2018. Precipitation influences pathogenic bacteria and antibiotic resistance gene abundance in storm drain outfalls in coastal sub-tropical waters. Environ Int. 116, 308-318. Ahmed, W., Gyawali, P., Feng S, McLellan, S.L., 2019. Host specificity and sensitivity of established and novel sewage-associated marker genes in human and nonhuman fecal samples. Appl. Environ. Microbiol. 85 (14), e00641-19. Boehm, A.B., Van De Werfhorst, L.C., Griffith, J.F., Holden, P.A., Jay, J.A., Shanks, O.C., Wang, D., Weisberg, S.B., 2013. Performance of forty‐one microbial source tracking methods: a twenty‐seven lab evaluation study. Water Res. 47, 6812-6828. Bustin, S.A., Benes, V., Garson, J.A., Hellemans, J., Huggett, J., Kubista, M., Mueller, R., Nolan, T., Pfaffl, M.W., Shipley, G.L., Vandesompele, J., Wittwer, C.T., 2009. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 55 (4), 611-622. Caldwell, J.M., Levine, J.F., 2009. Domestic wastewater influent profiling using mitochondrial real-time PCR for source tracking animal contamination. J. Microbiol. Methods. 77 (1), 1722. Campagnolo, E.R., Philipp, L.M., Long, J.M. and Hanshaw, N.L., 2018. Pet‐associated Campylobacteriosis: A persisting public health concern. Zoonoses Public Health, 65 (3), 304-311. Centers for Disease Control and Prevention, 2019. “About Pets & People.” Available at https://www.cdc.gov/healthypets/healthbenefits/index.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Ffeatures%2Fh ealthypets%2Findex.html. Accessed 3 October 2019. Chern, E.C., Siefring, S., Paar J Doolittle, M., Haugland, R.A., 2011. Comparison of quantitative PCR assays for Escherichia coli targeting ribosomal RNA and single copy genes. Lett. Appl. Microbiol. 52 (3), 298−306. Damborg, P., Olsen, K.E.P., Moller Nielsen, E., Guardabassi, L., 2004. Occurrence of Campylobacter jejuni in pets living with human patients infected with C. jejuni. J. Clin. Microbiol. 42 (3), 1363-1364. Dick, L.K., Bernhard A.E., Brodeur, T.J., Domingo, J.S., Simpson, J.M., Walters, S.P., Field, K.G., 2005. Host distributions of uncultivated fecal Bacteroidales bacteria reveal genetic markers for fecal source identification. Appl. Environ. Microbiol. 71 (6), 3184–3191. Feng, S., Bootsma, M., McLellan, S.L., 2018. Novel human-associated Lachnospiraceae genetic markers improve detection of fecal pollution sources in urban waters. Appl. Environ. Microbiol. 84 (14), e00309-18. Feng, S., Ahmed, W., McLellan, S.L., 2019. Ecological and technical mechanisms for cross reaction of human fecal indicators with animal hosts. Appl. Environ. Microbiol. pii: AEM.02319-19. Green, H.C., White, K.M., Kelty, C.A., Shanks, O.C., 2014. Development of rapid canine fecal source identification PCR-based assays. Environ Sci Technol. 48 (19), 11453-11461. Hackett, T., and Lappin, M.R., 2003. Prevalence of enteric pathogens in dogs of north-central Colorado. J Am Anim Hosp Assoc, 39 (1), 52-56. Harwood, V.J., Staley, C., Badgley, B.D., Borges, K. and Korajkic, A., 2014. Microbial source tracking markers for detection of fecal contamination in environmental waters: relationships between pathogens and human health outcomes. FEMS Microbiol. Rev. 38 (1), 1-40. Haugland, R.A., Siefring, S.C., Wymer, L.J., Brenner, K.P., Dufour, A.P., 2005. Comparison of Enterococcus measurements in freshwater at two recreational beaches by quantitative polymerase chain reaction and membrane filter culture analysis. Water Res. 39, 559-568.

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TABLE 1 Background information on fecal samples

Hosts

Cat Chicken Cow Deer Dog Emu Horse Kangaroo Pig Sheep Untreated sewage

Total number of samples

Sample types

Sources of samples

10 30 20 20 30 10 30 30 30 30 30

Individual Individual Individual Individual Individual Individual Individual Individual Individual Individual Composite

Veterinary hospital Various households Various farms Deer sanctuary Dog beach Farm Horse racecourse Wild Abattoir Various farms Primary influent of a WWTP

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Wet weight (mg) or volume (mL) used for DNA extraction 200-220 mg 180-200 mg 200-220 mg 200-220 mg 200-220 mg 200-220 mg 200-220 mg 100 mg 200-220 mg 200-220 mg 10 mL

Range (ng) DNA per µL of extract 17-83.2 11.1-70.2 10.2-108 20.1-80.3 14-230 14-220 8.2-78.0 16.9-75.2 10-70.1 3-15 10.3-23.4

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TABLE 2 Host specificity, sensitivity and mean and SD of log10 concentrations of four canine-feces associated MST marker genes in canine and non-canine fecal samples Hosts Cat Chicken Cow Deer Emu Horse Kangaroo Pig Sheep Untreated sewage TN FP Host specificity Dog TP FN Host sensitivity

Number of samples tested 10 30 20 20 10 30 30 30 30 30

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No. of positive samples/No. of quantifiable samples (log10 (± SD) concentrations/g or L of feces)) BacCan-UCD 5/5 (6.61 ± 1.62) 8/8 (6.45 ± 1.35) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 12/12 (7.19 ± 0.49) 0/0 (NA) 6/6 (8.08 ± 1.22) 209 31 0.87 30/30 (7.61 ± 1.06) 30 0 1.00

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DogBact 1/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 4/4 (5.23 ± 0.59) 235 5 0.98 27/27 (7.15 ± 0.92) 27 3 0.90

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DF113 6/5 (4.51 ± 0.26) 1/1 (4.36) 18/13 (4.44 ± 0.25) 1/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 0/0 (NA) 14/7 (4.91 ± 0.15) 200 40 0.83 25/25 (5.80 ± 1.25) 25 5 0.83

DF418 8/8 (8.71 ± 0.31) 8/8 (8.62 ± 0.95) 19/18 (8.63 ± 0.85) 19/19 (9.40 ± 0.73) 2/2 (6.67 ± 0.62) 6/6 (8.66 ± 0.70) 20/20 (8.76 ± 0.80) 1/1 (8.02) 29/29 (9.90 ± 0.46) 30/30 (9.02 ± 0.76) 142 98 0.59 27/27 (7.82 ± 1.13) 27 3 0.90

NA: Not applicable; TN: True negative; FP: False positive; TP: True positive; FN: False negative.

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Journal Pre-proof TABLE 3 Concentration of canine feces-associated marker genes in individual canine fecal samples

Canine fecal sample ID

Mean ± SD log10 GC/g DF113 ND 3.66 ± 0.24 7.06 ± 0.18 5.11 ± 0.22 ND 5.19 ± 0.12 ND ND 6.51 ± 0.10 ND 5.92 ± 0.11 6.43 ± 0.50 6.16 ± 0.12 7.78 ± 0.08 6.31 ± 0.23 6.10 ± 0.33 5.81 ± 0.05 8.23 ± 0.07 3.71 ± 1.23 6.54 ± 0.25 3.96 ± 0.13 4.80 ± 1.14 7.14 ± 0.11 6.26 ± 0.21 3.99 ± 0.67 5.03 ± 0.45 5.37 ± 0.23 4.42 ± 1.16 6.82 ± 0.16 6.66 ± 0.12

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BacCan DogBact Dog 1 7.36 ± 0.11 ND Dog 2 6.77 ± 0.21 ND Dog 3 8.73 ± 0.13 7.76 ± 0.10 Dog 4 8.25 ± 0.06 8.38 ± 0.25 Dog 5 6.98 ± 0.09 8.67 ± 0.21 Dog 6 7.08 ± 0.13 5.46 ± 0.18 Dog 7 7.72 ± 0.05 6.48 ± 0.45 Dog 8 8.30 ± 0.07 7.08 ± 0.12 Dog 9 8.71 ± 0.10 8.63 ± 0.19 Dog 10 6.44 ± 0.21 8.00 ± 0.07 Dog 11 9.20 ± 0.14 7.05 ± 0.14 Dog 12 7.33 ± 0.05 8.25 ± 0.08 Dog 13 9.00 ± 0.12 7.14 ± 0.19 Dog 14 4.68 ± 1.12 ND Dog 15 9.13 ± 0.03 7.27 ± 0.13 Dog 16 5.29 ± 0.13 6.30 ±0.05 Dog 17 6.54 ± 0.10 6.76 ± 23 Dog 18 7.21 ± 0.11 8.48 ± 0.11 Dog 19 7.42 ± 0.14 6.89 ± 0.14 Dog 20 7.10 ± 0.25 7.57 ± 0.41 Dog 21 8.21 ± 0.19 6.69 ± 0.32 Dog 22 7.21 ± 0.12 7.61 ± 0.10 Dog 23 8.49 ± 0.05 7.54 ± 0.11 Dog 24 8.81 ± 0.18 7.32 ± 0.19 Dog 25 8.15 ± 0.07 6.07 ± 0.21 Dog 26 7.66 ± 0.08 6.53 ± 0.28 Dog 27 7.12 ± 0.21 7.48 ± 0.13 Dog 28 7.23 ± 0.33 6.52 ± 0.21 Dog 29 8.29 ± 0.07 5.98 ± 0.12 Dog 30 8.15 ± 0.03 5.26 ± 0.45 ND: Not detected; SD: Standard deviation

DF418 7.35 ± 0.19 7.52 ± 0.12 8.27 ± 0.07 8.95 ± 0.03 9.48 ± 0.05 8.18 ± 0.05 8.27 ± 0.08 7.74 ± 0.12 8.85 ± 0.12 8.22 ± 0.21 ND 8.41 ± 0.11 7.64 ± 0.14 8.57 ± 0.03 7.54 ± 0.19 ND 7.62 ± 0.21 8.67 ± 0.04 4.13 ± 0.18 7.99 ± 0.02 7.60 ± 0.05 5.76 ± 0.24 8.12 ± 0.25 7.58 ± 0.14 5.76 ± 0.10 ND 7.07 ± 0.11 8.16 ± 0.12 8.23 ± 0.14 9.34 ± 0.13

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Journal Pre-proof TABLE 4: Mean log10 concentrations of canine-feces associated marker genes in canine and non-canine fecal and sewage samples tested in this study. Marker genes Canine

Animals 6.80 ± 1.11 4.46 ± 0.24 8.77 ± 1.24

Untreated sewage 6.54 ± 0.71 5.23 ± 0.59 4.91 ± 0.51 9.03 ± 0.76

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7.61 ± 1.06 7.15 ± 0.92 5.80 ± 1.24 7.82 ± 1.13

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BacCan-UCD DogBact DF113 DF418

Mean log10 (± SD) GC/g or L Non-canine

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TABLE 5: Concentrations of E. coli 23S rRNA, DogBact and DF113 marker genes in water samples collected from Brisbane River during base flow. Sampling sites are shown in Fig. 1. Sampling sites*

EC 23S rRNA GC/100 mL Event 1

BR1 BR2 BR3 BR4 BR5 OC1 BR6 BR7 BR8 BR9 BR10 BR11 BR12 BR13 a

3.23 3.43 3.12 4.21 3.98 4.89 3.78 3.98 4.12 3.09 2.93 3.43 2.98 3.21

a

Event 2 3.43 3.21 2.93 3.93 3.78 4.81 3.54 4.01 4.09 3.25 2.85 3.54 2.51 3.54

b

Event 3

BacCan-UCD GC/100 mL b

Event 1

2.98 3.47 2.81 3.24 3.81 4.23 3.67 4.12 4.31 3.48 3.12 3.21 2.63 3.12

ND ND + ND ND ND ND ND + ND ND ND ND ND

a

Event 2 ND ND ND ND ND ND ND ND ND ND ND ND ND ND

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Event 3 ND ND ND ND ND ND ND ND ND ND ND ND ND ND

DogBact (GC/100 mL) b

Event 1 ND ND + ND ND ND ND ND + ND ND ND ND ND

Event 2

b

Event 3

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ND ND ND ND ND ND ND ND ND ND ND ND ND ND

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DF113 (GC/100 mL) b

ND ND ND ND ND ND ND ND ND ND ND ND ND ND

Event 1 ND ND ND ND ND ND ND ND ND ND ND ND ND ND

a

Event 2 ND ND + ND ND ND ND ND ND ND ND ND ND ND

b

Event 3

b

ND ND ND ND ND ND ND ND ND ND ND ND ND ND

= 15th May 2019; b = 19th May 2019; c = 26th May 2019; + = Sample positive for the marker but not quantifiable.

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FIG 1: Map of the study river

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FIG 2: Concentrations of canine feces-associated marker genes in non-canine fecal and sewage samples

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Credit author statement Pradip Gyawali: Sampling, data analysis, writing Kerry Hamilton: Writing

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Sayalee Joshi Writing and preparation of graphical abstract David Aster: Sampling Warish Ahmed: Design study, writing, editing,

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Conflict of intereset The authors declare no conflict of intereset

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Graphical abstract

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Highlights 

BacCan-UCD, DogBact, DF113 and DF418 were highly prevalent in canine feces.

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DogBact marker gene was highly specific to canine feces.

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Dog marker was occasionally detected in river water samples.

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The DF418 marker gene was not suitable for dog fecal pollution tracking in Australia.

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