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Widespread detection of antibiotic resistant bacteria from natural aquatic environments in southern Ontario

Journal: Canadian Journal of Microbiology

Manuscript ID cjm-2018-0286.R2

Manuscript Type: Article

Date Submitted by the Author: 12-Nov-2018

Complete List of Authors: Pashang, Rosha; Ryerson University Faculty of Science, Chemistry and BiologyYusuf, Farhan; Ryerson University Faculty of Science, Chemistry and BiologyZhao, Simon; Ryerson Univeristy Faculty of Science, Chemistry and BiologyDeljoomanesh, Shadi; Ryerson University, Faculty of Science, Chemistry and BiologyGilbride, Kimberley; Ryerson University Faculty of Science, Chemistry and Biology

Keyword: antibiotic resistance, bacterial community, aquatic environments, tetracycline

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Issue? :Not applicable (regular submission)

https://mc06.manuscriptcentral.com/cjm-pubs

Canadian Journal of Microbiology

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3 Widespread detection of antibiotic resistant bacteria from natural aquatic environments in

4 southern Ontario

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7 Rosha Pashanga*, Farhan Yusufa*, Simon Zhaoa, Shadi Deljoomanesha and Kimberley A. Gilbridea,b**

8 aDepartment of Chemistry and Biology and bRyerson Urban Water, Ryerson University

9 350 Victoria Street, Toronto, Ontario, M5B 2K3

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11 * these two authors contributed equally to this work

12 ** corresponding author, email: [email protected], phone:416-979-5000, ex 6354

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15 Acknowledgements: The authors would like to thank the numerous undergraduate students

16 that helped to collect and plate the water samples including Aditi Patel, Amanda Marple, Eddie

17 H. Truong, Hossam Abdel Rahman, Jaerok Kim, Ramsey Smith, Sharmay Cu, Tung Nguyen,

18 Umair Munawar and Zohreh Kianfard. The authors would also like to thank Eric Harley for his

19 advice on the Weka software.

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21 Funding: K.A.G. was funded by a NSERC Discovery Grant, (RGPIN227565) and a Ryerson

22 Health Research Grant.

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23 Abstract

24 To elucidate how widespread antibiotic resistance is in the surface water environment, we

25 studied the prevalence of antibiotic resistance bacteria (ARB) at four locations in southern

26 Ontario. We found that the percentage of bacteria resistant to the antibiotic tetracycline was

27 higher at the river site, which flows through agricultural land, and lower at the lake sites. A

28 total of 225 colonies were selected for further antibiotic disc susceptibility testing to 8

29 different antibiotics to calculate the multiple antibiotic resistance (MAR) and the antibiotic

30 resistance index (ARI) for each site. Although the isolates from the lake site outside the city

31 displayed resistance to fewer antibiotics, their MAR scores were not significantly different

32 from the lake sites adjacent to urban beaches showing that multiple antibiotic resistance was

33 widespread in the natural water environments tested. Isolation of colonies under selection

34 pressure to tetracycline was found to have a significant effect on the likelihood that the

35 isolates would contain multiple resistance traits for other antibiotics. Identification of isolates

36 selected on tetracycline were compared with isolates that were sensitive to tetracycline and

37 the community composition was found to be distinctly different although isolates from the

38 genera Chryseobacterium, Pseudomonas and Stenotrophomonas were found in both

39 communities.

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42 Key words

43 Antibiotic resistance, bacterial community, aquatic environments, tetracycline

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44 Introduction

45 The continued discovery, development and use of antibiotics, is challenged despite

46 growing clinical needs because of the increased emergence of antibiotic resistant bacteria

47 (ARB) (Leung et al. 2011). Although ARB can be found in a wide variety of environments the

48 negative impact of human activities, including travel and the import and export of goods, on

49 the dissemination and abundance of ARB and the resistance determinants that they carry, has

50 become a concern not only for public health but is also viewed as an accelerant of the

51 evolution of resistance gene patterns and distributions (Finley et al. 2013; Wellington et al.

52 2013; Marti et al. 2014; Berendonk et al. 2015). Furthermore, the increasing number of

53 antibiotic resistance genes (ARG) carried by bacterial pathogens affects our capability to

54 combat infectious diseases and has become a global issue (de Kraker et al. 2011; Public Health

55 Agency of Canada 2017).

56 Resistance genes appear to be ubiquitous in nature with many examples coming from

57 environmental reservoirs such as soil and water, however, the dissemination patterns of the

58 resistance determinants in the natural environment is poorly understood (Baquero et al.

59 2008; Finley et al. 2013; Wellington et al. 2013; Marti et al. 2014; Berendonk et al. 2015; Roca

60 et al. 2015). A critical factor that favours dissemination and increased abundance of ARG in

61 man-made and natural environments (Allen et al. 2010) is the ability of bacteria to acquire

62 resistance to antibiotics in numerous ways including mutation, transposition within genomes,

63 and horizontal gene transfer (HGT). Transmission by HGT includes the development of

64 antibiotic resistant bacteria via conjugation, transformation, or transduction. These processes

65 can contribute to genome plasiticity, adaptation, and evolution of many bacteria lineages and

66 can further influence biological networks that extend beyond the limits of a single bacterium

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67 (Heuer and Smalla 2007; Smillie et al. 2011). Horizontal gene transfer can also promote the

68 acquisition of resistance genes by pathogens from bacteria in the environment (Baquero et al.

69 2008; Allen et al. 2010; Finley et al. 2013). Since the interaction between the pathogen and

70 environmental reservoir is a crucial link for clinical isolates to acquire new ARGs, the

71 investigation of the origin, distribution, and presistence of ARGs in the natural environments is

72 important for understanding the dissemination patterns of ARGs. The main challenge for

73 investigating the spread of resistant bacteria is the lack of available surveillance data and

74 scientific literature on the resistome in whole bacterial communities in the natural

75 environment (Berendonk et al. 2015; Public Health Agency of Canada 2017). While more

76 recent studies have investigated the link between the environmental reservoirs of ARGs and

77 pathogenic bacteria, there is a need to collect data on both ARG carrying and ARG non-

78 carrying, non-pathogenic bacteria to better understand the ecology of resistance within

79 microbial communities.

80 Aquatic environments including benthic sediments have been considered ideal settings

81 for the acquisition and dissemination of ARGs and ARBs (Kümmerer 2009; Marti et al. 2014)

82 Metagenomic analysis has identified more than 600 ARG subtypes from environmental

83 samples (Li et al. 2015) with more than 140 ARGs from bacteria from wastewater

84 (Szczepanowski et al. 2009). Although metagenomic analysis can identify ARGs and predict the

85 co-occurrence of resistance traits and the possible hosts, culturing methods are still needed to

86 confirm the associations.

87 This study investigated bacterial communities at four different sample aquatic sites, (1

88 recreational lake site, 1 river site impacted by agricultural land and 2 lakes sites adjacent to a

89 large urban city), to determine the prevalence and abundance of tetracycline resistance

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90 bacteria, the prevalence of eight different antibiotic resistance genes (ARGs) in that population

91 and the identity of the bacteria that carry these genes. In order to provide an integrated

92 approach that includes prevalence data, antibiotic profiles as well as identities of the isolated

93 strains we chose to use culture methods to be able to match the identity of the bacteria with

94 their resistance profile including identifying isolates that do not carry resistance. The

95 information gathered from this research will help to better understand the prevalence of ARBs

96 and ARGs in aquatic environments in southern Ontario together with the relationship between

97 ARG-carrying and ARG non-carrying bacteria, and the potential for the spread of ARGs within

98 the bacterial community.

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100 Materials and Methods

101 Description of study sample sites and collection

102 Samples (1L) were collected from each of four sites on at least 4 separate occasions that

103 spanned spring to late fall from both water column and sediment. The first site was Buckhorn

104 Lake (44° 29' N / 78° 22' W) that serves as a recreational water source for cottages and was

105 sampled in August 2014, September 2014, October 2014, June 2015 and July 2017. The second

106 site was the Humber River (43° 52' N / 79° 44' W) adjacent to a township of Caledon of

107 approximately 60,000. The river is not used as a source of drinking water as that area relies on

108 ground water for its drinking water. The river, however, does pass through regions of

109 agriculture and therefore may receive non-point source run-off from farmland. This site was

110 sampled in April 2014, November 2014, May 2015 and April 2016. The third and fourth sites

111 were from Lake Ontario. Specifically, the third site (43° 39' N / 79° 18' W) was at Ashbridges

112 Bay Beach on the east side of Toronto and was samples in September 2014, December 2014,

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113 April 2015, May 2015, and July 2015. The fourth site (43° 38' N / 79° 28' W) was at the mouth

114 of the Humber River on the west side of Toronto and was sampled in October 2014, April

115 2015, May 2015 and June 2016. The lake receives final effluent from all four of Toronto’s

116 wastewater treatment plants (WWTPs) with the Humber WWTP and the Ashbridges Bay

117 WWTP final effluent pipes within 2 kilometers of the west and east lake sampling sites

118 respectively.

119 Samples were collected in sterile 1 liter glass bottles. The water column samples were

120 collected by submerging the bottles 30 cm below the water surface approximately 1 meter

121 from the shore, filling and screwing on the lids before removing. The surface sediment samples

122 were collected by disturbing the surface of the sediment to a depth of 1-2 cm and holding the

123 bottles to the bottom to fill. All samples were transported to the lab within 30 minutes and

124 processed. The water column samples were mixed prior to plating samples on media while the

125 sediment samples were allowed to settle before samples from the bottom of the bottles were

126 pipetted for plating on media.

127 Of the 225 isolates that were selected to further characterization, 42 isolates were from

128 Buckhorn lake – Site 1 (S1), 85 were from Humber River-site 2 (S2), 60 were from Lake

129 Ontario – east side – Site 3 (S3) and 40 were from Lake Ontario – west side –site 4 (S4).

130

131 Culturable heterotrophic counts and isolation of bacteria

132 The enumeration of culturable heterotrophic counts were carried out by preparation of

133 serial dilution using sterile 0.9% NaCl followed by spread plating onto Reasoner’s 2A agar

134 (R2A) with and without tetracycline (16 mg/L) in quadruplicate and incubated at room

135 temperature for 2-5 days. The average colony forming units (CFU) from the quadruplicate for

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136 culturable aerobic plates counts were used to determine the percentage of tet resistant

137 bacteria at each site. From the plates, bacteria isolates were selected based on morphotypes

138 and re-grown on R2A with and without tetracycline (16 mg/L) accordingly for further

139 antibiotic resistance testing and identification.

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141 Antibiotic resistance screening of isolates

142 One hundred and sixty isolates were tested for antibiotic susceptibility to eight

143 antibiotics using the standard Kirby–Bauer Disk Diffusion method (Bauer et al. 1966) and the

144 protocol provided by BBL™ Sensi-Disc™ antimicrobial susceptibility test discs (Becton,

145 Dickson and Company, NJ, USA) text manual except that isolates were tested on R2A agar

146 instead of Mueller-Hinton. Isolates were grown in broth to achieve the density of a 0.5

147 McFarland standard and then used to swab plates used to test 4 antibiotics per plate. Lab

148 strains of Escherichia coli (DH5α) and Pseudomonas putida (ATCC 12633) were used as

149 standards for the methods. The zone diameters were measured and the isolates classified as

150 sensitive, intermediate or resistant using the diameters set out for heterotrophic bacteria. The

151 eight antibiotic discs used were ciprofloxacin (5µg), tetracycline (30 µg), gentamicin (10 µg)

152 streptomycin (10 µg), chloramphenicol (30 µg), kanamycin 30 µg, ampicillin (10 µg),

153 sulfamethoxazole-trimethoprim (23.75 ug/1.25 ug).

154

155 Multiple antibiotic resistance and antibacterial resistance index

156 The percentage of multiple antibiotic resistant bacteria (MAR) at each location was

157 determined. An isolate was considered to be MAR if it was found to be resistant to three or

158 more antibiotics (Krumperman, 1983).

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159 Antibacterial resistance index (ARI) was calculated using the following formula:

160 ARI = A/NY

161 where A is the total number of resistant determinates recorded in the population, N is the

162 number of isolates in the population and Y is the total number of antibiotics tested (Mohanta

163 and Goel 2014).

164 DNA Extraction

165 The DNA was extracted from isolates using the MoBio UltraClean Soil DNA Extraction

166 Kit (MoBio Laboratories Inc., CA, USA), following the manufacturer’s protocol. DNA was stored

167 at -20º C until needed for polymerase chain reaction (PCR) amplification and sequencing.

168

169 Multiplex PCR for identification of tetracycline resistance determinants (tetR)

170 Primer pairs targeting eight different tetR genes were used in single (tet (A), (G), (Q),

171 (X)) and duplex (tet (B) and (C); tet (M) and (W)) PCR assays (Table 1). The PCR was

172 conducted using S1000™ Thermal Cycler (Bio-Rad Life Science Group, Canada) with the blocks

173 preheated to PCR conditions described by Yeung et al., (2011). Primer pairs targeting eight

174 different tetR genes were used in 25 µL reaction containing 50 ng of genomic DNA, 10 pmol of

175 each target primer, 0.3 µL BSA (New England BioLabs, ON, USA), 10 pmol of each

176 oligonucleotide primer 100 µmolL-1, 2.5 µL Taq buffer (10 mM Tris-HCl pH 9.0, 50 mM KCl, 1.5

177 mM MgCl2) (New England BioLabs, ON, Canada), and 1.25 µL Taq polymerase (New England

178 BioLabs, ON, Canada). PCR product aliquots of 5 µL were analyzed by 1% agarose gel with

179 SYBR® Safe DNA Stain (Thermo Fisher Scientific, ON, Canada), using a 100 bp ladder (New

180 England BioLabs, ON, Canada). To ensure reproducibility, the PCR amplifications were carried

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181 out in duplicates with positive and negative controls. The sources of positive controls along

182 with the target regions for the tetR primers are summarized in Table 1.

183

184 DNA sequencing and phylogenetic analysis

185 The bacteria-specific primers used for the 16S rRNA gene in the PCR reactions were

186 forward primer U341 F (5’-CCTACGGGAGGCAGCAG-3’) (Muyzer et al. 1993) and reverse

187 primer U758 R (5’-CTACCAGGGTATCTAATCC-3’) (Baker et al. 2003). This primer pair

188 amplifies an approximate 418 base pair fragment. DNA sequencing of the PCR products were

189 performed at the ACGT (ON, Canada) with an Applied Biosystems SOLiD 3.0 system. A single

190 consensus sequence was generated from the forward and the reverse nucleotide sequences

191 using BioEdit Sequence Alignment Editor (Version 7.0.9.0; Hall, 1999). The resultant DNA

192 sequences were then analyzed by the Basic Local Alignment Search Tool at the National Center

193 for Biotechnology Information website (NCBI, http://www.ncbi.nlm.nih.gov/Blast.cgi).

194

195 Co-occurrence of antibiotic resistances

196 Antibiotic resistance profiles were analyzed using Weka software (Witten et al. 2011;

197 Frank et al. 2016) to determine the probability that resistances were co-present in isolate

198 profiles. Briefly, the data was imported as a .csv file into Weka and converted using the Weka

199 software to an ARFF file with the eight antibiotic as attributes and their possible values as

200 resistant or sensitive which generated 161 rows of data. We ran the Apriori association rule

201 algorithm in Weka to obtain the top 10 rules with a confidence of at least 90%. In this paper

202 we are reporting only the rules with at least 95% confidence, which have the support of at

203 least 32 rows of data.

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204 Results and Discussion

205 Antibiotic resistance has developed rapidly in clinical isolates because of the use and

206 misuse of antibiotics for the treatment of bacterial infections (Leung et al. 2011). However

207 there is much evidence to suggest that antibiotic resistance is ubiquitous in nature (Forsberg

208 et al. 2012) and the presence of sub-inhibitory concentrations of antimicrobials in the

209 environment may select for these traits and cause their prevalence to increase (Finley et al.

210 2013). New high-throughput sequencing has shown that an “intrinsic resistome” exists and

211 includes many sequences that belong to the bacterial metabolic network. These traits appear

212 to contribute to resistance if subjected to an environment with high levels of antibiotics (Galán

213 et al. 2013). However, there is still a lack of data to link the bacterial hosts in the natural

214 environment to their pool of ARGs to better understand the evolution and dissemination of

215 resistance genes (Marti et al. 2014). In this study we tested natural water sources from four

216 southern Ontario aquatic sites for the presence of ARBs and ARGs using culture dependent

217 methods. Isolates were collected and tested for resistance to eight ARGs and for the prevalence

218 of specific tetracycline genes and identified by sequencing their 16S rRNA amplicons.

219

220 Antibiotic resistance screening of isolates

221 A total of 36 samples from the water column and benthic sediment of 4 different sites

222 were collected and plated on media containing and not containing tetracycline. The average

223 overall culturable bacterial counts were higher in the sediment samples than in the water

224 column samples for all sites with detectable tetracycline resistance at all locations (Table 2).

225 Overall, the frequency of resistance to tetracycline was between 0.06% and 0.79% with the

226 river site (S2) having the highest prevalence of tetracycline resistant bacteria (Figure 1).

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227 Although the use of tetracycline has waned in human medicine it is still used extensively in

228 agricultural husbandry (Daghrir and Drogui 2013). Since the river site flows through farmland

229 upstream of the sample site, it is possible that run off from the farmland contained tetracycline

230 residues that promoted higher levels of bacterial resistance (Uyaguari-Diaz et al. 2017). The

231 frequency of tetracycline resistance overall, however, was lower in these natural aquatic

232 environments than previously seen in Toronto wastewater treatment system samples, where

233 frequencies have been reported to vary from 0.13% to 7.18% (Tehrani and Gilbride 2018).

234 The lower frequencies associated with the natural environment may be expected since the

235 natural environment is assumed to be less impacted by anthropogenic activities. Molecular

236 approaches have also shown that soil and surface water samples appear to contain 1-3

237 magnitudes lower ARGs than sewage treatment plant or fecal samples (Li et al. 2015). Of all

238 the isolates collected, a total of 225 isolates were selected to represent the phenotypic

239 diversity observed on the plates and maintained for further analysis.

240 The investigation of the diversity and abundance of ARGs in these environments, was

241 conducted on 100 tetracycline resistant and 60 tetracycline sensitive isolates to the following

242 antibiotics: ampicillin (10 µg); chloramphenicol (30 µg); ciprofloxacin (5 µg); gentamicin (10

243 µg); kanamycin (30 µg); sulfamethoxazole-trimethoprim (23.75 µg /1.25 µg); and tetracycline

244 (30 µg). The percentage of isolates with resistance to each of the eight antibiotics is shown in

245 Table 3. Regardless of the sample site, many of the isolates showed resistance to many of the

246 antibiotics tested which confirms the notion that antibiotic resistance is ubiquitous (Finley et

247 al. 2013). Most isolates showed resistance to multiple resistances (Table 4) and it was rare to

248 find an isolate (less than 5%) that did not display at least one antibiotic resistance which

249 supports the perception that the environment contains a pool of antibiotic resistance genes

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250 (LaPara and Burch 2011; Zhang et al. 2011). Interestingly, we did not isolate any bacteria from

251 Buckhorn Lake that were simultaneously resistant to 6 or more of the antibiotics tested unlike

252 the other sites, which might imply that resistance to antibiotics is somewhat reflective of the

253 intrinsic environmental conditions at each location. However, caution should be exercised not

254 to assume that there were fewer resistance genes at this site since further investigation of our

255 isolates for additional antibiotic resistance genes may reveal additional traits not yet tested

256 for.

257 In this study, all the sites had similar MAR scores (Table 4). However, when we grouped

258 isolates into two groups - isolates that had been selected for resistance to tetracycline and

259 isolates that had been isolated from non-selective plates, it was found that the MAR scores

260 were significantly different. Many of the isolates selected on tetracycline were found to contain

261 at least 3 more antibiotic resistance traits (83.3%) compare to only 43.4 % of the isolates

262 collected under no selection pressure. This suggests that isolates that carry a resistance gene

263 to one antibiotic are more likely to carry multiple resistance genes. Previous studies have

264 shown that exposure to one antibiotic can not only increase the selection for that resistance

265 but can also increase resistance to other antibiotics as well as heavy metals (Barr et al. 1986;

266 Aminov 2009; Blázquez et al. 2012). Furthermore, we might be able to infer that the presence

267 of MAR isolates at all locations in the natural aquatic environment suggests that multiple

268 resistance may be more common than previously recognized. Interestingly the MAR scores for

269 the tetracycline sensitive population in the natural aquatic environments in this study was

270 found to be higher than the MAR scores reported for tetracycline sensitive isolates from

271 Toronto WWTPs (43.4% vs 13.6 %) (Tehrani and Gilbride 2018).

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272 The antibiotic resistance index (ARI) was calculated and the ARI score for the

273 tetracycline resistance population was calculated to be 0.37 while the tetracycline sensitive

274 population had an ARI score of 0.11 (Table 4). Since an ARI score of 0.2 or above suggests that

275 selection pressure accounts for the enrichment of ARGs in a population, the ARI score of the

276 population selected on tetracycline confirms that the selection pressure (plating on

277 tetracycline) contributed to increased ARG isolation among the isolates collected. This is a

278 concerning thought since it implies that the contamination of water sources with a single

279 antibiotic may result in a bacterial community carrying resistance to multiple antibiotics

280 (Alonso et al. 1999). The significance of these results would be quite staggering in the clinical

281 setting as it suggests that bacterial pathogens exposed to one antibiotic will be more likely to

282 carry additional resistances. This would additionally augment the difficulty of treating patients

283 that have contracted an antibiotic resistant bacterial infection. To test the association of one

284 resistance to another, we analyzed the antibiotic resistance profiles of the isolates using Weka

285 software (Witten et al. 2011; Frank et al. 2016) to determine the probability that resistance to

286 one antibiotic is carried concomitantly with resistance to another. Four associations with

287 confidence levels of 95% or more were found. They were; 1) if isolates were resistant to

288 kanamycin and streptomycin then they were most likely also resistant to gentamicin, 2) if

289 isolates were resistant to chloramphenicol and ciprofloxacin then they were most likely also

290 resistant to tetracycline, 3) if isolates were resistant to ciprofloxacin and kanamycin they were

291 most likely also resistant to gentamicin, and 4) if isolates were resistant to tetracycline,

292 chloramphenicol and streptomycin then they were most likely also resistant to gentamicin.

293 Similar co-occurrence associations have been seen in metagenomic and network analysis (Li et

294 al. 2015).

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295

296 Taxonomic identification of isolates

297 To evaluate the composition of the populations, one hundred and forty-two of the

298 isolates were identified (Table 5). Overall, we identified 37 different genera, 7 genera

299 containing pathogenic species, 6 genera containing some pathogenic species or species that

300 could be opportunistic, and 23 genera containing species that were not known to be human

301 pathogens. Overall 31 % of the isolates were considered potentially pathogenic, 32.4 % were

302 opportunistic pathogens and 36.6 % were not considered pathogenic to humans but contained

303 species known to be plant pathogens. The pathogenic isolates were isolated from all four sites

304 regardless of their location. Eighty-four percent of all the isolates were Gram-negative and 16

305 % were Gram-positive. The dominance of Gram-negative isolates in culturable populations is

306 common. Other studies that have looked at culturable resistant bacterial isolates and have

307 identified at least 49 different genera (Low et al. 2016) including Acinetobacter spp.,

308 Aeromonas spp., Chryseobacterium spp., Escherichia coli, Pseudomonas spp., and Serratia spp., all

309 of which are Gram-negative and most likely to carry tetracycline resistance (Sullivan et al.

310 2013). We also found these genera although our two Aeromonas isolates were tetracycline

311 sensitive. Overall, many genera were found in multiple sites with Chryseobacterium spp.,

312 Pseudomona spp. and Stenotrophomonas ssp. being the most common at our sites. The

313 tetracycline resistant population and the tetracycline sensitive population shared more than

314 half of their genera in common however the composition of the two populations was distinctly

315 different. This implies that some genera may be more likely to carry resistance genes than

316 others however the basis of that difference has not yet been elucidated.

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317 The populations were also compared to wastewater populations in the Toronto area

318 and we saw that only ten of the genera (27%) isolated from the surface water were also

319 identified from wastewater treatment systems (Tehrani and Gilbride 2018). Furthermore,

320 only tetracycline sensitive isolates of Chryseobacterium were isolated from the natural

321 environment sites while only tetracycline resistant isolates had been isolated from the

322 wastewater. Conversely, isolates of Flavobacterium isolated from the natural environment

323 were found to be tetracycline resistant while those from the wastewater samples were

324 tetracycline sensitive. Although not all species were monitored in this study, the frequency of

325 carrying an ARG and genera identity may be important for understanding the influence of one

326 environment on the other and the movement of ARGs between environments. Moreover, it is

327 also important to document the identification of bacteria that are not carrying resistance traits

328 to determine if genera identity impacts HGT events so that the pathways of dissemination can

329 be elucidated. Metagenomic data has also been used to predict the typical genera carrying

330 antibiotic resistance genes and has predicted that isolates from the genera Blautia,

331 Clostridium, Enterococcus, Bacteroides and Escherichia would be likely carriers (Forslund et al.

332 2013; Li et al. 2015). Thus, our data suggests that the ARG carrying consortium is much more

333 diverse and contains genera that may not be well represented in shotgun library constructs.

334 Bioinformatic analyses, to predict co-occurrence relationships between ARGs and their

335 possible hosts in complex environmental samples using metagenomic data, however, can be

336 supported with culture dependent studies like this one.

337

338 Tetracycline resistance gene distribution

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339 To evaluate the diversity of tetracycline genes in tetracycline resistant isolates, sixty-

340 four of the tetracycline resistance isolates were further tested for the presence of eight

341 tetracycline resistance genes (tet (A), tet (B), tet (C), tet(G), tet (M), tet (Q), tet (X), and tet (W))

342 that have previous been found widespread in environmental or wastewater samples (Sullivan

343 et al. 2013; Mao et al. 2015; Li et al. 2015; Waseem et al. 2017). Only 10 of the isolates

344 contained at least one of the selected genes, which might imply that the non-culturable portion

345 of the population probably contains resistant genes not seen in the culturable portion

346 (Popowska et al. 2012). Previous studies with whole community DNA have shown that tet (C),

347 tet (Q) and tet (X) were found in Toronto’s wastewater environment (Tehrani and Gilbride

348 2018) yet we found only one isolate from Lake Ontario - east side that contained a tet (C) gene,

349 five isolates from the river site (S2) and Lake Ontario – west side (S4) that contained a tet (Q)

350 and no isolates that contained a tet (X). Furthermore, we found four isolates that carried a tet

351 (B) gene, a determinant that had not been previously detected in the wastewater (Tehrani and

352 Gilbride 2018) and two isolates that carried multiple tetracycline resistance genes - a

353 Chryseobacterium that carried a tet (B) and tet (G) and a Varivorax that carried tet (B), tet (G),

354 tet (M) and tet (Q). Overall, the ten isolates spanned four phyla, which confirms the

355 widespread occurrence of tetracycline genes. There is not enough data available to identify the

356 most common tetracycline resistance gene in these environments. Additional studies that

357 compare the ARG profiles and the associated genera from natural aquatic and clinical

358 environments in the same urban location may be able to provide a better understanding of the

359 origin, and dispersal of ARGs between natural and man-made environments such as

360 wastewater or hospitals.

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361 The contribution of the widespread occurrence of ARGs in the urban aquatic

362 environment towards the rise in new variants of ARBs is still not fully understood. The

363 findings from this study highlight the need for developing monitoring protocols for ARG

364 carrying non-pathogenic bacteria, as well as, pathogenic bacteria in the environment to better

365 mitigate the risks associated with antibiotic resistance gene dissemination.

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366 References

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486

487

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488 Figure 1. Average number of culturable a) heterotrophic bacteria and b) tetracycline resistant

489 bacteria in the water column (WC) and sediment (SED) at each of the 4 sites. S1=Buckhorn

490 Lake, S2=Humber River, S3=Lake Ontario-east side, S4=Lake Ontario-west side.

491

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Table 1. Tetracycline-resistance PCR primers used.

Sequences (5’ to 3’)Target

Tetracycline Resistance

Gene

AmpliconSize (bp)

Resistance mechanism

Positive Control

Source

GCT ACA TCC TGC TTG CCT TC

CAT AGA TCG CCG TGA AGA GG

tet (A) 210 Efflux pump E. coli

(RP1)

M. Roberts

TTG GTT AGG GGC AAG TTT TG

GTA ATG GGC CAA TAA CAC CG

tet (B) 659 Efflux pump E. coli HB101

(pRT11)

M. Roberts

CTT GAG AGC CTT CAA CCC AG

ATG GTC GTC ATC TAC CTG CC

tet (C) 418 Efflux pump E. coli DO-7

(pBR322)

M. Roberts

GCT CGG TGG TAT CTC TGC TC

AGC AAC AGA ATC GGG AAC AC

tet (G) 468 Efflux pump E. coli

(pUC119G)

M. Roberts

GTG GAC AAA GGT ACA ACG AG

CGG TAA AGT TCG TCA CAC AC

tet (M) 406 Ribosomal

binding protein

E. coli DH1

(pACYC177)

M. Roberts

TTA TAC TTC CTC CGG CAT CG

ATC GGT TCG AGA ATG TCC AC

tet (Q) 904 Ribosomal

binding protein

Plasmid DNA

(pBT-1)

M. Roberts

GAG AGC CTG CTA TAT GCC AGC

GGG CGT ATC CAC AAT GTA AAC

tet (W) 168 Ribosomal

binding protein

Plasmid DNA

(pGEM-TW)

M. Roberts

CAA TAA TTG GTG GTG GAC CC

TTC TTA CCT TGG ACA TCC CG

tet (X) 468 Enzymatic

modification

DNA

(tetX gene)

G. J. Vora

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Table 2. Number of culturable tetracycline resistant bacterial colonies in the water column and sediment at

the various locations.

Site Location Sample Total average (CFU/mL)

Total average tetR

(CFU/mL)Water column 3.3 x 104 ± 1 x 104 1.9 x 101 ± 12Site 1 Buckhorn lake Sediment 3.9 x 104 ± 1.3 x 104 2.5 x 101 ± 8Water column 4.6 x 104 ± 5.2 x 103 3.6 x 102 ± 54Site 2 Humber River Sediment 1.7 x 105 ± 5.2 x 104 4.2 x 102 ± 120Water column 2.6 x 104 ± 1.1 x 104 2.5 x 101 ± 6Site 3 Lake Ontario – east side

of city Sediment 1.1 x 105 ± 1.3 x 104 1.0 x 102 ± 40Water column 2.8 x 104 ± 9.8 x 103 4.0 x 101 ± 17Site 4 Lake Ontario – west side

of city Sediment 4.6 x 104 ± 4.5 x 103 4.4 x 101 ± 8

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Table 3. The antibiotic profiles (expressed as percentages) of all the isolates for the eight antibiotics from the water column and sediment of each sitea.

a WC = water column, S = sediment; b tet=tetracycline, amp=ampicillin, chl=chloramphenicol, cip=ciprofloxacin, gm=gentamicin, km=kanamycin, str=streptomycin, SxT=sulfamethoxazole and trimethylprim

Antibioticsb (percent resistant)Sourceatet amp chl cip gm km str SxT

Site 1-WC 41.2 28.6 28.6 0 85.7 50.0 0 0Site 1-S 64.7 41.2 23.5 47.0 88.2 64.7 29.4 29.4

Site 2-WC 59.3 38.5 53.8 55.5 66.6 40.7 25 33.3Site 2-S 66.6 66.6 75.0 15.0 79.2 45.8 52.2 58.3

Site 3-WC 51.7 44.8 41.3 26.3 37.9 27.6 44.8 44.8Site 3-S 33.3 60 40.0 30.8 73.3 46.7 53.3 60.0

Site 4-WC 57.1 42.9 52.4 66.7 71.4 33.3 33.3 42.9Site 4-S 50.0 75.0 60.0 45.0 100 80.0 55.0 ND

Total average 53.0 49.7 46.8 35.8 75.3 48.6 36.6 38.4

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Table 4: The distribution of the isolates that were resistant to none (0) to all (8) of the antibiotics tested from each of the sites including the percentage of total number of isolates that had been isolated (100-TetR) or not isolated (60-TetS) on tetracycline containing media.

Isolates found resistant to multiple different antibiotics (%)Source ARI score 0 1 2 3 (MAR score) 4 5 6 7 8Site 1-WC 0.04 0 100 100 57.1 14.3 14.3 0 0 0

Site 1-S 0.07 5.9 94.1 88.2 76.5 58.8 5.9 0 0 0

Site 2 - WC 0.17 4.1 95.8 95.8 91.7 62.5 45.8 16.7 4.1 0Site 2-S 0.18 0 100 83.3 83.3 75.0 54.2 50.0 29.2 8.3

Site 3-WC 0.12 3.7 96.3 55.5 51.9 40.7 33.3 18.5 18.5 7.4Site 3-S 0.11 13.3 86.7 80.0 73.3 53.3 40.0 40.0 13.3 13.3

Site 4-WC 0.11 9.5 90.5 85.7 66.7 47.6 38.1 33.3 23.8 19.0Site 4-S 0.14 0 100 100 85.0 65.0 50.0 45.0 20.0 nd*

Total TetR 0.37 0 100 96 83.3 68 53 34 21 9Total TetS 0.11 11.7 88.3 61.7 43.3 26.7 8.3 3.3 0 0

* nd = not determined

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Table 5. a) Identification and number of isolates of each bacterial genera at the different sites and their tetracycline profiles. b) distribution of the bacterial phylum and their tetracycline profilesa)Genus Phylum Pathogen* Site 1 Site 2 Site 3 Site 4Acinetobacter Gamma-Proteobacteria Yes 1 (1) 1Aeromonas Gamma-Proteobacteria Yes 1 1Arthrobacter Actinobacteria No 3 (2)Bacillus Firmicutes Yes 3 (2) 2(1)Bradyrhizobium Alpha-Proteobacteria No 1 (1)Brevundiumonas Alpha-Proteobacteria Yes 1 1Caulobacter Alpha-Proteobacteria No 1 (1) 1 (1)Chryseobacterium Bacteroidetes Opportunistic** 6 (6) 6 (6) 8 (7)Comamonas Beta-Proteobacteria No 2Curtobacterium Actinobacteria No 1Cytophaga Bacteroidetes No 3Dickeya Gamma-Proteobacteria No 1 (1)Elizabethkingia Bacteroidetes Opportunistic 1Enterobacter Gamma-Proteobacteria Yes 1Erwinea Gamma-Proteobacteria No 1Flavobacterium Bacteroidetes No 1 2 (2)Glutamicibacter Actinobacteria No 1Janthinobacterium Beta-Proteobacteria No 2 1Kytococcus Actinobacteria Opportunistic 1 (1)Lysinibacillus Firmicutes No 2Lysobacter Gamma-proteobacteria No 1 (1)Massilia Beta-Proteobacteria No 3 1Microbacterium Actinobacter Yes 1 4 (4) 2 (2)Nitrobacter Alpha-Proteobacteria No 1 (1)Nocardioides Actinobacter No 1Pantoea Gamma-Proteobacteria Opportunistic 2Pedobacter Bacteroidetes No 3 1 1Polynucleobacter Beta-Proteobacteria No 1 (1)Pseudomonas Gamma-Proteobacteria Opportunistic 1 10 (2) 8 3Rhizobium Alpha-Proteobacteria No 1Rhodococcus Actinobacteria Yes 1Salmonella Gamma-Proteobacteria Yes 1Serratia Gamma-Proteobacteria Yes 1 1 (1)Sphingobacterium Bacteroidetes Yes 1 1Stenotrophomonas Gamma-Proteobacteria Yes 3 (1) 13 (6) 2 5 (3)Varivorax Beta-Proteobacteria No 7 (7) 2 (2) 1 (1)Xanthomonas Gamma-Proteobacteria No 1Total 29 53 29 31* pathogen is defined as one that causes a disease in humans, some genus designated as a no may still contain plant pathogens; ** opportunistic means the genus may contain species that cause human disease if the host is compromised( ) numbers of isolates that were tet resistant

b)

PhylumNumber

of isolates

Percent of total

Number of tetR

isolates

Percent of isolates from

phylum1 Alpha-Proteobacterium 7 5 4 572 Beta-Proteobacterium 20 14 11 553 Gamma-Proteobacterium 59 41.5 16 274 Bacteroidetes 34 24 21 625 Actinobacter + 15 10.5 9 606 Firmacutes + 7 5 3 43

Total 142 73

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draft...draft 1 1 2 3 widespread detection of antibiotic resistant bacteria from natural aquatic...

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