Treatment enhances the prevalence of antibiotic-resistant bacteria and antibiotic resistance genes in the wastewater of Sri Lanka, and India

Journal Pre-proof Treatment enhances the prevalence of antibiotic-resistant bacteria and antibiotic resistance genes in the wastewater of Sri Lanka, and India Manish Kumar, Bhagwana Ram, Himaya Sewwandi, Sulfikar, Ryo Honda, Tushara Chaminda PII:

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https://doi.org/10.1016/j.envres.2020.109179

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YENRS 109179

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Environmental Research

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Please cite this article as: Kumar, M., Ram, B., Sewwandi, H., Sulfikar, , Honda, R., Chaminda, T., Treatment enhances the prevalence of antibiotic-resistant bacteria and antibiotic resistance genes in the wastewater of Sri Lanka, and India, Environmental Research (2020), doi: https://doi.org/10.1016/ j.envres.2020.109179. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.

1

Treatment Enhances the Prevalence of Antibiotic-Resistant Bacteria

2

and Antibiotic Resistance Genes in the Wastewater of Sri Lanka, and

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India

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Manish Kumar1*, Bhagwana Ram2, Himaya Sewwandi3, Sulfikar4, Ryo Honda5,

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Tushara Chaminda3

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1Department 2Department 3Department

of Earth Sciences, Indian Institute of Technology Gandhinagar, India

of Civil Engineering, Indian Institute of Technology Gandhinagar, India

of Civil and Environmental Engineering, University of Ruhuna, Galle, Sri Lanka

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4Graduate

School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan

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5Faculty

of Environmental Design, Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan

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*Corresponding Author:

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Manish Kumar, Ph.D. [The Univ. of Tokyo]

20

Assistant Professor | Dept. of Earth Sciences | Room No. 336A, Block 5|

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Indian Institute of Technology Gandhinagar| Gujarat - 382 355 | India |

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+91-863-814-7602 | | Office: 07923952531 | Ext: 2531(O); 1531 (H) |

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[email protected] | http://www.iitgn.ac.in/academics/es/

24 1

25

Abstract:

26

Wastewater treatment plants (WWTPs) are being debated for being the hot spots for the

27

development of antibiotic resistance in pathogenic microbial communities. We observed

28

the prevalence of antibiotic-resistant bacteria (ARB), antibiotic resistance genes (ARG),

29

and multidrug resistance (MDR) in two municipal WWTPs and one hospital WWTP in

30

Western and Southern Sri Lanka, and compare the results with particular reference to

31

Indian and the World scenario to trace the imprints of treatment on ARB and ARG.

32

Result suggests that although wastewater treatment resulted in higher than 1.06 log

33

Escherichia coli (E. coli) reduction at all WWTPs, yet the percent of E. coli resistant to

34

most of the antibiotics increased from influent to effluent. Higher prevalence of ARB,

35

ARG, and MDR were noted in hospital WWTP owing to the higher antibiotic

36

concentrations used and excreted by the patients. With reference to India, the WWTPs in

37

Sri Lanka showed more ARB and a consistent increase in its percentages after the

38

treatment but were less resistant to Fluoroquinolone (FQ). E. coli strains isolated from

39

each location of both countries showed multidrug resistance, which has increased after

40

the treatment and was strongly correlated with FQ in every WWTP. Resistant genes for

41

Fluoroquinolone

42

sulphonamides (sul1) were common in all the wastewaters except additional parC gene

43

in the hospital effluent of Sri Lanka, implying much higher resistance for quinolones,

44

especially for Ciprofloxacin. Multivariate statistical treatments suggest that effluent

45

showed higher loadings and association for MDR/ARB, where pH change and more

46

extensive interaction with metals during the treatment processes seem to have

47

profound effects.

(FQ)

(aac-(6')-1b-cr,

qnrB,

48

2

qnrS),

β-lactams

(ampC),

and

49

Keywords: Antibiotic resistance; antibiotic resistance genes; Multi-drug resistance;

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Wastewater; Sri Lanka; India

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1. Introduction

52

Common infectious diseases might soon become untreatable and life-threatening owing

53

to the increasing prevalence of antibiotic-resistant bacteria (ARB) and multi-drug

54

resistance (MDR)(Ram and Kumar, 2020; Huang et al., 2012; Kumar et al., 2019a;

55

O’Neill, 2014; WHO, 2014). Genetic level capabilities of resisting antibiotics in the

56

microbes have led to the frequent use of higher doses and more expensive antibiotics or

57

antibiotic cocktails (Huang et al., 2012). If common infections require costly treatments,

58

the condition will be worst in developing countries. By the year 2050, more deaths may

59

be due to antimicrobial-resistant infections compared to other significant causes

60

(O’Neill, 2014). If the situation is not seriously addressed, a post-antibiotic era is

61

possible, according to the World Health Organization (WHO, 2014). Seventy-one

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countries that include Sri Lanka witnessed a 36% increase in antibiotic consumption

63

between 2000 and 2010 (Alam and Deng, 2015). As per the estimate, the global use of

64

antimicrobials will increase by 67% (63,151 to 105,596 tons) between 2010 and 2030

65

(Van Boeckel et al., 2015), which will significantly influence microbial ecology

66

demonstrated by the detection of antibiotic resistance bacteria (ARB) and antibiotic

67

resistance genes (ARG) in municipal solid waste leachate, sludge and the ambient water

68

(Zhang et al., 2015), sediments (Storteboom et al., 2010), wastewater (Reinthaler et al.,

69

2003), surface water (Honda et al., 2016; Kumar et al., 2019b), drinking water and

70

groundwater (Al-Judaibi, 2014).

71 72

Bacteria exposed to available antibiotics develop resistance and become ARB with time

73

(Threedeach et al., 2012) and reduce the effectiveness of the therapeutic potential of 3

74

drugs (Zhang et al., 2009). Resistance occurs naturally over time through genetic

75

mutation or by acquiring resistance from other bacteria (Al-judaibi, 2014), resulting in

76

the ability to survive and multiply in the presence of the antibiotic (Alam and Deng,

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2015). Antibiotic-resistant bacteria (ARB) spread vertically and horizontally, which

78

implies passing resistant genes to new generations and exchanging resistant genes

79

between bacterial species (Al-judaibi, 2014; Proia et al., 2016). The food and drinking

80

water is the medium for the transfer of ARGs to humans (Wilcks et al., 2004). Soil and

81

water are recipients of ARGs and sources of clinical concern and subsequently amplify

82

ARGs (Heuer et al., 2011).

83 84

Wastewater treatment plants (WWTPs), mainly designed to control primary pollution

85

parameters (biochemical oxygen demand (BOD5), chemical oxygen demand (COD),

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dissolved and suspended solids), are reservoirs of human and animal commensal

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bacteria, which persist and are released into the environment with the final effluent

88

(Reinthaler et al., 2003). The effluent discharged into water bodies plays a significant

89

role in spreading ARB due to the availability of residual antibiotics (Tennstedt et al.,

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2003) owing to horizontal gene transfer, nutritional richness, and high bacterial count

91

(Dröge et al., 1999). Davies (2007) studied and reported a significant relationship

92

between the development of resistance and the use of antibiotics. Gao et al. (2012)

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reported an association between numbers of ARG and ARB with antibiotic

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concentrations in sludge (Heberer, 2002). The biological treatment promotes bacterial

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growth and genetic exchange in WWTPs, which may lead to a further increase in ARG

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(Du et al., 2015). Effluent from urban WWTPs has been shown to increase ARG in

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downstream river sediments (Marti et al., 2013). Effluents from urban WWTPs are a

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primary anthropogenic source for the spreading of ARB and ARG in the environment 4

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(Rizzo et al., 2013). Treated effluent of urban WWTP can discharge 109–1012 Colony

100

Forming Units (CFU) per day, per inhabitant equivalent (Novo and Manaia, 2010).

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Among these, at least 107–1010 may have any acquired antibiotic resistance (Rizzo et al.,

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2013), implying the importance of urban WWTPs in the accumulation and spread of ARB

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in the environment. Perhaps the most pressing concern is the release of ARGs from

104

urban WWTPs.

105 106 107

Concentrations of antibiotics in hospital effluent are typically 100 times greater than in

108

the Sewage Treatment Plant (STP) effluent (Kümmerer, 2009). The high concentration

109

of antibacterial agents in hospital wastewaters is due to high use and low dilution

110

compared to household effluent (Duong et al., 2008). Being classified as a domestic

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effluent, hospital waste is not subjected to legal requirements to reduce microbial loads

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before discharge into municipal sewers, most of which connect to rivers and streams

113

(Devarajan et al., 2016). The pollution by microbial contaminants in freshwater

114

resources could extend up to several kilometers from WWTPs discharge point (Proia et

115

al., 2016). The correlation between urban water discharge and resistant bacteria in

116

rivers has been confirmed in the form of ARGs (Watkinson et al., 2007). E. Coli isolated

117

from the Chao Phraya Delta canal network in Thailand has increased antibiotic

118

resistance near urban lands (Honda et al., 2016). Antibiotic-resistant bacteria (ARB)

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have been found up to 1 km downstream of a discharge location in the Tordera River

120

Basin, Nothern Spain (Proia et al., 2016) and, up to 4 km in some other locations (Alam

121

and Deng, 2015).

122

5

123

However, a study on ARB and ARG in developing countries is very inconspicuous,

124

especially from Sri Lanka. Further, whether resistance may develop in WWTPs is

125

currently under discussion (Bouki et al., 2013) and considering the entirely different

126

characteristics of wastewater in developing from that of the developed country (Bouki

127

et al., 2013), an explicit demarcation in the prevalence of ARB, ARG, and MDR present in

128

the influent and effluent of various wastewaters of developing country is yet to be

129

established. Further, probably no study reported on ARB and ARG has taken into

130

account of metal contamination and applied multivariate statistical techniques to

131

enumerate the discussion on the effect of wastewater treatment in the developing

132

countries like Sri Lanka and compared with other results reported from elsewhere.

133 134

Under the light of above discussion, the objectives of the present study were to: (1)

135

determine the prevalence of antibiotic-resistant bacteria (ARB), antibiotic resistance

136

genes (ARG), and multidrug resistance (MDR) in the wastewaters (influent and effluent)

137

from two municipal and one hospital WWTP placed in Western and Southern Sri Lanka

138

and compare them with those produced in a city of the Western India, and 2)

139

statistically trace the imprints of treatment by comparing ARB, ARG, and metals in the

140

influent and effluents analyzed.

141 142

2. Materials and Methods

143

2.1. Sampling and metal analyses

144

Figure 1 illustrates the sampling locations of influent and effluent samples; a) three

145

WWTPs of Ahmedabad in India represented as S1, S2 and S3, b) three WWTPs in Sri

146

Lanka i.e. two municipal WWTPs at Ja-Ela (JETP) and Rathmalana (RMTP) and one

147

hospital WWTP at Karapitiya Hospital (KHTP); and c) Five samples at KHTP i.e. i) 6

148

influent, ii) effluent of KHTP, iii) discharge point in a municipal canal, iv) canal water

149

sample 20 m after the discharge point (K20) and v) canal water 50 m downstream of

150

KHTP effluent discharge (K50). JETP was designed for the treatment of wastewater

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generated from 36000-person equivalents with a capacity of 7250 m3 d-1. As per 2012

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data, JETP was receiving wastewater from 1302 municipal, 72 industrial, and 21

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institutional sources. Treated effluents are discharged into the Dadugam Oya. The RMTP

154

is designed to treat 17000 m3 of wastewater per day. The effluent of WWTP is released

155

into the sea through a 600 m long outfall pipe.

156 157

Both JETP and RMTP receive industrial, institutional and municipal wastewater. The

158

treatment process of both plants is based on the activated sludge method with pre-

159

denitrification and phosphorus removal. Karapitiya Hospital WWTP (KHTP) was

160

constructed in the 1980s with a capacity of 600 beds; however current occupancy is

161

nearly 1800 beds, hence the WWTP operates overcapacity. After chlorination processes,

162

treated effluent is discharged into the municipal drain. Samples were obtained before

163

and after chlorination and 20 and 50 m downstream from discharge (Figure 1c).

164 165

Among the sewage treatment plant (STP), Jaspur (S1), Chankheda (S2) and STP Vasna

166

(S3) were sampled in Ahmedabad city, Gujarat, India (Fig 1a). STP S1, S2, and S3 are

167

with capacities of 76, 35, 35 million liter per day (MLD) treatment respectively. All 3

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STPs are applying on the Activated Sludge Process (ASP) based treatment to the influent.

169

Effluents of S1 and S2 are used for wastewater irrigation (S1 irrigates for a net area of

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769 hectares (Bhavin et al., 2018)), and the effluent of S3 is released into the Sabarmati

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River. For antibiotic analysis, 50 ml samples were stored in sterile centrifuge tubes and

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chilled with dry ice until transfer to the laboratory. For metal analysis, the sample was 7

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collected in 125-ml polyethylene bottles. The samples were filtered using 0.45 µm filters

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and preserved with concentrated HNO3. Samples were analyzed by Inductively Coupled

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Plasma - Mass Spectrometry (ICP-MS) (Perkin Elmer NexION® 2000) using method

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APHA (APHA et al., 2005).

177 178

2.2. Total coliform and E. coli count

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Water samples were diluted in 10-fold steps with 0.8-0.85% NaCl solution. Samples

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were processed using 37 mm monitor kits (Advance Toyo, Tokyo Japan), which contain

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a gridded 0.45 µm membrane-filter inside with a pad underneath the membrane to

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absorb the culture media (Chromocult® Coliform Agar ES, Merck). Five ml of each

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diluted (or undiluted) water sample was filtered through the monitor kit followed by a 3

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ml culture media, and the kits were incubated for 22–24 h at 35.5 °C. Counting the dark

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blue/violet colonies and pink colonies indicated E. coli and other coliforms, respectively.

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Total coliform count is the sum of E. coli and other coliforms.

187 188

2.3. Antibiotic susceptibility test

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Antibiotic susceptibility test was carried out using KB disk diffusion method9. E. coli was

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cultured in sterile PERLCORE® Tryptic-Soy Broth (EIKEN Chemical Co. Ltd., Tokyo).

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Individual colonies with a similar shape were selected from previously incubated

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samples, suspended in 4-5 ml broth in sterile 15 ml centrifuge tubes, and incubated at

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35.5°C for 18 h. Agar medium for the antibiotic resistance test was prepared using

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PERLCORE® Sensitivity Test (ST) Agar (EIKEN Chemical Co. Ltd., Tokyo) following the

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manufacturer's protocol. The agar solution was autoclaved at 121°C for 20 min, cooled

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to 55°C, and 20–25 ml of solution was added to sterile 90-mm Petri dishes. After the

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agar was set, the prepared E. coli culture was spread out on the agar with a sterile 8

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cotton swab. After 3–5 min, antibiotic discs (KB Disk®, EIKEN Chemical Co., Ltd.,) of six

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antibiotics, levofloxacin (LVX), ciprofloxacin (CIP), norfloxacin (NFX), kanamycin

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monosulphate (KM), tetracycline (TC), and sulfamethoxazole (ST), were placed on the

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ST agar with a minimum of 24 mm between discs. After incubating at 37 °C for 16–18 h,

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the diameter of growth inhibition of the E. coli was measured. According to the

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inhibition zone diameter classify as resistance, intermediate and sensitive.

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2.4. DNA extraction and PCR for antibiotic-resistant genes

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Samples were kept in the dark and under dry ice during transport to the laboratory.

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Upon arrival in the laboratory, 2 mL of the samples were taken and allowed to stand in

208

the refrigerator until the supernatant appeared clear. The supernatant was discarded,

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and the sediment was sent frozen to Japan for DNA extraction using the Fast DNATM

210

spin kit following the kit protocol (MP Biomedicals, LLC, Ohio, USA). DNA extracts were

211

stored at -20 ˚C until PCR amplification. The quantity and the purity (A260/280 and

212

A260/230) of the DNA were determined using the Biophotometer D30 (Eppendorf,

213

Germany). DNA extracts were further purified using the ethanol purification method if

214

the absorbance ratio Abs 260/280 is below 1.8.

215 216

Ten genes that confer resistance to five classes of antibiotics mechanisms of action were

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amplified from the DNA extracts. ARG primers and annealing temperature are given in

218

Table S1. The five types of inhibition of DNA gyrases are; qnrB, qnrS, aac(6`)-1b, parC for

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fluoroquinolones; inhibition of cell wall synthesis bla-CTX, bla-TEM, bla-SHV, ampC for β-

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lactams, and vanA for vancomycin; inhibition of folate synthesis; and sul1 for

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sulphonamides. Gene amplification was performed for 30 cycles with an initial

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denaturation at 95 ˚C for 3 min, denaturation at 30 s, annealing at optimum temperature 9

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for 45 s, elongation at 72 ˚C for 1 min, and final elongation at 72 ˚C for 7 min. The

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reaction was performed using an Applied Biosystems 2720 thermocycler. The presence

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of antibiotic resistance gene amplicons in the PCR products was identified using the

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electrophoresis gel method. Agarose (2% w v-1) was used to run the PCR product for 30

227

min at 100 volts.

228 229

2.5. Statistical analysis

230

Correlation of resistance among the tested antibiotics was evaluated using the phi (φ)

231

coefficient, calculated based on 2×2 contingency tables established for each combination

232

of two antibiotics, indicating the number of isolates resistant to both antibiotics,

233

resistant to either antibiotic and sensitive to both antibiotics. The φ coefficient ranges

234

from -1 to +1 based on the correlation of two antibiotics. To ensure quality control blank

235

was prepared for dilution buffer, monitor kit, and media separately. Statistical Package

236

for the Social Sciences (SPSS 21) was used to carry out Principal Components Analysis

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(PCA) and hierarchical cluster analysis (HCA) after data normalization using z-scores.

238

An orthogonal varimax rotation was used to generate non-related PCs. Results were

239

then represented in a 2-dimension PCA diagram. Cluster analyses were done using the

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Ward method to show proximity among the analyzed parameters of all samples.

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3. Results and Discussion

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3.1. E. coli prevalence

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E. coli prevalence (CFU ml-1) was found to be reduced during treatment from influent to

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effluent (Table 1). E. coli in the influent ranged from 2493 CFU ml-1 at KHTP to 14367

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CFU ml-1 at RMTP. The log reduction in prevalence was 1.95, 2.36 and 1.06 in JETP,

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RMTP, and KHTP, respectively. In KHTP samples, the E. coli count was drastically 10

248

decreased from influent to effluent and then increased at the outlet, then reduced at the

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discharge point but again shoot up at 20 m downstream into the canal, implying a zone

250

of higher incubation creating down the lane owing to the revival of colonies. The

251

prevalence of E. coli in hospital wastewater was less than reported by Reinthaler et al.

252

(2003) (Table 1). The prevalence of E. coli in STPs of India ranged from 2893 CFU ml-1 to

253

96393 CFU ml-1 with a log reduction of 0.068, 0.164 and 1.008 at S1, S2, and S3

254

respectively. Bacteria are usually significantly reduced during the treatment process,

255

including resistant bacteria (Huang et al., 2012).

256

observations made as per the data obtained in Indian Scenario: i) All three STPs have an

257

entirely different biological source of contamination as reflected by ~X prevalence at S2,

258

~4X at S1 and 20X at S3, ii) Reduction ratio seems to be dependent on prevalence and

259

disinfection practices. The slight difference between total coliform and E. coli counts

260

suggests that the STP water, both influent and treated, comprises of E. coli.

Following are the significant

261 262

Overall, the prevalence of E. coli has usually significantly reduced during the treatment

263

process despite tertiary treatment not being part of many of the treatment plants

264

studied. However, total coliform count remains higher than the prescribed Recreational

265

Water Quality Criteria (The US Environmental Protection Agency (EPA) 2012) for all

266

freshwater bodies designated for “primary contact recreational use,” i.e. above threshold

267

value of 4.1 CFU ml-1, which still may cause 36 illness per 1000 recreators (EPA, 2012).

268 269

3.2. Resistance towards antibiotics

270

Figure 2 enumerates the results of the resistance percentages observed in Sri Lankan

271

wastewater with that of Ahmedabad, Western India. Resistance percentages of all

272

antibiotics increased from influent to effluent except to CIP for JETP samples (Figure 2). 11

273

The high potential of ARG exchange due to the extended incubation period could be the

274

reason for this increment. As the incubation period refer to the residence time inside the

275

reactor, both the microflora of purifying sludge and microorganisms present in the

276

effluents likely to contribute to ARG exchange. Fluoroquinolones (LVX, CIP, and NFX)

277

and non-fluoroquinolones (KM, ST, and TC) exhibited two patterns of resistance for each

278

sampling point. For JETP and RMTP samples, there was less resistance to

279

fluoroquinolones than to non-fluoroquinolones. Fluoroquinolones (LVX, CIP, NFX) are

280

drugs generally used in the treatment of urinary and respiratory tract infections, their

281

use being more common in hospitals and health care centers. This is in agreement with

282

the results reported in this work, from which a pattern of higher resistance to

283

fluoroquinolones was determined by resistant-bacteria from wastewaters of the

284

hospital treatment plant (KHTP). Non-fluoroquinolone antibiotics, although more

285

obsolete, are also prescribed for the treatment of particular human illness such as

286

respiratory and gastrointestinal infections (KN), or some skin, eyes, lymphatic system

287

infections, or from reproductive and urinary systems and urinary of livestock. That is

288

perhaps why JETP receiving the wastewater from domestic sources as well as dairy,

289

poultry, and food-processing companies showed higher resistance for NFQs.

290 291

It is observed that the resistance percentages was more than or equal to 50 in all cases.

292

Even though E. coli prevalence is reduced by treatment, the resistance of E. coli present

293

in the effluents was significantly high except for the resistance to CIP in the effluents of

294

JETP and RMTP. Being a hospital wastewater treatment facility, KHTP exhibited the

295

highest prevalence of ARB in their effluents except that for sulfamethoxazole (ST),

296

probably attributed to a considerably higher concentration of antibiotics excreted by

297

patients than in municipal or industrial WWTP. Similar to other WWTPs, KHTP has also 12

298

shown an increase in ARB (%) in terms of their resistance independently of the bacteria

299

number after treatment which gets diluted after the discharge into the urban canal.

300

However, the most interesting feature along the discharge of this effluent was observed

301

at 20 m downstream from the discharge location when the resistance to all antibiotics

302

notched up to 100% for all six antibiotics tested in this study. The 50 m downstream

303

location was >80% resistant to fluoroquinolones and >70% resistant to non-

304

fluoroquinolones.

305 306

In the case of STPs in India, the influent of S1 showed 40% resistance to a

307

fluoroquinolone and 0% resistance to non-fluoroquinolone. The high resistance for

308

fluoroquinolone is usually observed for the influents of domestic origin as they are

309

generally prescribed to treat a variety of illnesses such as respiratory and urinary tract

310

infections. The influent of S2 showed a 20% resistance to LVX, NFX, and TC, 40%

311

resistance to KM but the same at location S3 showed 0% resistance to all antibiotics

312

except TC. The resistance to fluoroquinolone was higher at S1 and S2 owing to the

313

expensive nature of fluoroquinolones antibiotics than the non-fluoroquinolone (NFQ),

314

urban area with higher income shows higher uses of it. In S1 and S2, resistance increases

315

after treatment as it has activated sludge process, an agreement with studies from

316

Austria and Sweden (Flach et al., 2018; Reinthaler et al., 2003). The municipal

317

wastewater contains high density and diversity of bacteria, high nutrients, exposure of

318

antibiotics, chlorine, detergents, long retention time and aeration, helps in enhancing the

319

generation and replication of ARB. The ARB and ARG increment or decrement or even

320

no substantial change is truly an unpredictable outcome in the treatment process (Bouki

321

et al., 2013).

322 13

323

Table 2 compares the present study with previous studies conducted on municipal and

324

hospital wastewater. The higher resistance percentages in Sri Lanka is likely due to the

325

long retention time and aeration during the treatment process (Sulfikar et al., 2018).

326

Treatment at JETP and RMTP are based on the activated sludge process, with pre-

327

denitrification and phosphorus removal and thus temperature, humidity and rich

328

nutrient present in the wastewater also become imperative for WWTPs of tropical

329

countries. The ARB increases in WWTPs due to the presence of antibiotics as well as

330

favorable conditions like high residence time, the presence of competing species, and

331

required growth conditions. These conditions are collectively responsible for creating

332

selection pressure for greater resistance against the antibiotic(s) by altering genes to

333

favor growth and reproduction (Kumar and Pal, 2017).

334 335

With reference to India, the following observations can be made for the comparison: i)

336

Even WWTP treating municipal wastewater had more antibiotic resistance in Sri Lanka

337

than that in Indian STPs. This can be both due to differences in the a) characteristics of

338

influent coming to the WWTPs as well as operation and b) maintenance and operation

339

(including retention time) of treatment facilities at Ahmedabad and Colombo; ii) In Sri

340

Lanka, E. coli showed higher resistance to the older generation antibiotics like ST and TC

341

compared to FQ, but in Indian scenario resistance for FQ were more compared to ST and

342

TC. This may be attributed to lifestyle, economy, and difference in medical prescriptions;

343

and iii) In Sri Lanka, the prevalence of an increase in antibiotic resistance after the

344

treatment was more consistently observed than that of in India. As far as the gravity of

345

the situation is concerned, the resistance to FQ is worse and thus Sri Lankan condition

346

seems better than India, however, WWTPs in Sri Lanka need more attention towards

347

maintenance and operation of the system. 14

348 349

3.3. Variation and comparison of antibiotic resistance in WWTPs

350

The difference of antibiotic resistance in WWTPs in influent to effluent is shown in

351

Figure 3. A similar resistance pattern was observed in JETP and RMTP (Figure 3a & b),

352

as both use an activated sludge treatment process. The resistance of isolated E. coli was

353

increased from influent to effluent during treatment, except for CIP at JETP. In the

354

activated sludge process, the recirculated bacteria are continuously exposed to the

355

antibiotics in influent and mean residence time treatment system was 4-6 d leading to

356

further resistance development (Kurasam et al., 2018), (Chitnis et al., 2004).

357

Investigation of numerous multi-resistant enterococci in WWTPs in Portugal showed

358

that biological treatment did not prevent dissemination to the environment (Da Costa et

359

al., 2006). The resistance percentages of three WWTPs were compared. The hospital

360

KHTP (Figure 3c) showed significantly higher resistance to fluoroquinolones compared

361

to the other two municipal WWTPs, similar to a previous study in southern Austria

362

(Reinthaler et al., 2003). Fluoroquinolones antibiotics are used in the household as well

363

as in poultry production for flue related disease.

364 365

STP Jaspur (S1) showed a decrease in resistance after treatment in the case of FQ, but in

366

the case of NFQ, it has increased with treatment (Figure 3d). Samples collected from

367

STP Chandkheda (S2) illustrated no change in the resistance for LVX, NFX, and ST

368

(Figure 3e) but the same has increased for CIP and KM, after treatment. Interestingly, in

369

the same treatment facility, the resistance of bacteria towards TC has decreased after

370

treatment. Antibiotic resistance was not detected in influent and effluent of STP Vasna

371

(S3) except TC (which was 20% in the influent) (Figure 3f). Before the final discharge to

372

the Dandugam Oya river, the effluent from JETP passes through a chlorination contact 15

373

basin for disinfection. The antibiotic resistance analysis for isolated bacteria showed

374

high rates among the detected strains, highlighting the importance of effluent

375

sterilization (Ge et al., 2012). However, chlorination is not efficient in reducing antibiotic

376

resistance. Munir et al. (2011) showed that disinfection by UV radiation and chlorination

377

did not significantly reduce ARGs and ARB in Michigan (USA) WWTPs. To reduce multi-

378

drug resistant E. coli strains by 99.99% in urban WWTP a chlorine dose of 2.0 mg L−1

379

was required with 60 min of contact time (Rizzo et al., 2012).

380 381

3.4. Multidrug resistance

382

Microorganisms having resistance to at least three classes of antibiotics are considered

383

multidrug-resistant (Coutinho et al., 2013). E. coli isolated from the JETP effluent and

384

KHTP influent, KHTP effluent, 20 m and 50 m downstream exhibited 100% of resistance.

385

For all WWTP samples, at least 20% of colonies showed resistance to all six antibiotics

386

(Table 3). Compared to JETP and RMTP, the hospital WWTP (KHTP) had a higher

387

proportion of multidrug resistance which can be due to the higher concentration of

388

pharmaceutical products in the wastewater. Multidrug resistance has remarkably

389

increased when moving to effluent from influent. Multidrug-resistant has become a

390

significant concern for community health. New trials of antibiotics are less expensive

391

than prevention strategies, so most of the hospitals and medical officers focus on

392

treatment rather than prevention mechanisms. Such short-term practices have resulted

393

in a shift towards more expensive antibiotics in high-income countries while increasing

394

morbidity and mobility in developing countries (Alam and Deng, 2015).

395

16

396

3.5. Antibiotic resistance gene

397

We have taken a representative WWTP sample for ARG analyses i.e. one for municipal

398

(RMTP) and another for hospital WWTP (KHTP) (Table 4). The fluoroquinolone

399

resistance genes aac-(6')-1b-cr, qnrB and qnrS were detected at both KHTP and RMTP,

400

but parC was only found at KHTP. Although fluoroquinolones are relatively new

401

antibiotics, genes conferring resistance are commonly found at WWTP. This might be

402

because quinolones are the drugs of choice for treating urinary tract infections

403

(Tennstedt et al., 2003). In other studies, sul and tet genes were the most common genes

404

found at the influent of WWTP (Table 4). This is not surprising as tetracyclines and

405

sulfonamides have been widely used for a long time, and the genes encoding resistance

406

persist in WWTP processes (Xu et al., 2017). Although sulfonamides are no longer used

407

in humans because of toxicity, they are still used in agriculture (Xu et al., 2016).

408

Of the tested genes, ampC, tetW and sul1, were detected in all samples and in contrast to

409

the developed country blaCTX, blaTEM, vanA and dfr1 were not detected. These results

410

are in accordance with the antibiotic-resistant test above where resistance to

411

sulfamethoxazole, tetracyclines were observed. The presence of qnrS confers resistance

412

to quinolones (e.g. levofloxacin, ciprofloxacin, norfloxacin). A mutation in gyrA gene may

413

cause low-level of quinolone resistance (Hooper, 2003), and further sequencing of the

414

PCR products of the gyrA gene is needed to determine if the quinolone resistance

415

determining (QRDR) region of gyrA gene detected was mutated or not. Furthermore,

416

Yang et al., (2012) found genes conferring resistance to quinolones (qnrS, aac(6)-Ib-cr )

417

were more likely to co-occur with ampC in plasmid of S. marcescens. We detected ampC

418

gene which confers resistance to β-lactam antibiotics (e.g. ampicillin). It is possible that

17

419

the resistance to quinolones observed in the present study was dictated by other types

420

of quinolone resistance determinant genes which was not tested at present.

421 422

3.6. Process insight using Metal and Multivariate statistical analyses

423

In order to appreciate the industrial and anthropogenic contribution and their influence

424

on the overall distribution of ARB prevalence, metal concentrations in the samples were

425

estimated along with parameters like pH, electrical conductivity (EC), total dissolved

426

solids (TDS), oxidation-reduction potential (ORP), salinity, temperature, and metals

427

were analyzed. The order of metal concentration were found as Zn>Cu>Pb>Co>As>Cd

428

and Cu>Pb>Co>As>Zn>Cd in Sri Lanka and India respectively and were less than

429

acceptable limit for drinking water by WHO 2017 (WHO, 2017). Concentrations of Zn,

430

Cu, Pb, Co, As and Cd in JETP and RMTP effluent were 27.36, 19.90, 6.95, 3.85, 0.53, and

431

0.22 µgL-1 and 31.26, 27.65, 6.97, 5.44, 0.96, and 0.26 µgL-1 respectively. Concentrations

432

of Cu, Pb, Co and As in S1, S2 and S3 effluent were 20.26, 7.36, 4.25, 1.40 µgL-1; 22.53,

433

6.66, 4.10, 2.08 µgL-1 and 17.79, 7.78, 4.01, 1.44 µgL-1 respectively. Zn and Cd were

434

below the detection limit in all samples collected from Ahmedabad, India (Ram and

435

Kumar 2019).

436 437

The entire dataset obtained in this study was grouped into two, i.e. influent and effluent

438

and subsequently PCA and HCA were performed to understand the imprints of

439

treatment. Figure 4a and Table S2 present the results of principal component analyses

440

performed on influent waters. There were three principal components (PCs) identified

441

that explain 90% of the total variance in the dataset of influent waters. The first

442

component (PC1) explains 55.33% of the total variance and exhibits significant loading

18

443

for antibiotic resistance (LVX, CIP, NFX, KM, TC, ST) with ORP, Zn, and Cd as well as

444

negative significant loadings for Mn, Ni, Cr, and As). The resistance to fluoroquinolones

445

was highly correlated, suggesting cross-resistance within this class as also reported in

446

the previous study (Davies et al., 2003; Honda et al., 2016). The second component

447

(PC2) was found to be represented by EC, TDS, salinity, and Cu with a variance of

448

19.64%. The third component (PC3) is represented by pH, temperature, and Pb with a

449

variance of 14.72 %. Figure 4b and Table S3 show the variation in the dataset of the

450

effluents of WWTPs collected from both the countries. There were three principal

451

components (PCs) identified that explain 87% of the total variance. The first component

452

(PC1) explains 35.72% of the total variance and exhibits significant loading for antibiotic

453

resistance (LVX, CIP, NFX, TC, ST) with negative loading of metals (Ni, Cr, As). The

454

second component (PC2) is represented by pH, ORP, Cu, Co and Pb with a variance of

455

31.53%. The third component (PC3) is represented by EC, TDS, and Salinity with a

456

variance of 19.82 %. Overall, it is observed that the difference among the PC1 and PC2

457

diminished after the treatment, as both were explaining ~36% and 32% of the variation

458

in the dataset which was ~55% and ~20% for the influent implying the removal of some

459

key contamination during the treatment processes.

460 461

As observed from figure 4a resistance for all six antibiotics resistance has been found

462

clubbed with Zn, Cd, Co, and ORP, while the prevalence of Coliform was found to be

463

influenced by pH mainly. On the contrary, the overall loading for resistance and

464

associated parameters has increased significantly (remain in the positive domain). New

465

parameters that get associated with resistance are Cu, pH, and temperature. One of the

466

exciting observations has been the distance between the prevalence of coliform and

467

resistance clusters which has been found much closer in the effluent waters than that of 19

468

influent waters. This is perhaps one of the major imprints of treatment on the

469

prevalence of total coliform, E. Coli and their resistance capabilities.

470 471

Likewise, cluster analyses further explain the association of water quality parameters,

472

the prevalence of E. coli and total coliform and antibiotic resistance (Figure 4c & d).

473

Other than the observations of PCA being substantiated by cluster analyses, the major

474

observations may be made from the dendrograms are: i) Metal contamination do

475

influence prevalence of coliforms; ii) antibiotic resistance remain associated with

476

supplements like Zn; and iii) Water quality parameters like pH, salinity, EC, temperature

477

become more influential on resistance after the treatment signifying the change brought

478

in these parameters during the treatment processes.

479 480

3.7. Environmental Implication

481

With the increase in population, urbanization, and industrialization, there is a need to

482

reuse water. The WWTPs are not specially designed for the removal of emerging

483

contaminants like pharmaceuticals and personal care products (PPCPs), ARB, ARG, and

484

microplastics. With the time bacteria become resistant at low antibiotic concentrations

485

and genetic resistance is slowly acquired may result in a longer recovery time during

486

infections leading to the use of higher dose and new age antibiotics. This will lead to a

487

vicious cycle of more excretion of antibiotics and further resistance capabilities in

488

microbes. Although metal concentrations were found within the permissible limit in this

489

study yet it is likely to influence the ARBs. It has critical implications owing to the fact

490

that antibiotic resistance is acquired much faster than the rate of discovery of new

491

antibiotics. The present study highlights the essentiality for the inclusion of tertiary

492

treatment and disinfection facilities with every WWTPs to decrease the spread of ARB 20

493

and ARGs (Kumar et al., 2019c; Rizzo et al., 2019). The presence of antibiotic-resistant

494

microbes in sewage sludge is an essential obstacle in designing its sustainable utilization

495

(Taki et al., 2020).

496 497

4. Conclusion

498

E. coli prevalence was reduced during treatment, but seemingly the remaining bacteria

499

could adapt in the presence of antibiotics that lead to a further increase in resistance.

500

With reference to India, the WWTPs in Sri Lanka showed more antibiotic resistance and

501

a consistent increase in the antibiotic resistance after the treatment. However, Indian

502

cases may be considered more alarming owing to higher resistance for FQ than Sri

503

Lanka where the Resistance Ratio for TC and ST were comparatively higher (≥0.9) than

504

for other antibiotics. E. coli strains of all the locations in both countries exhibited

505

multidrug resistance implying a serious health concern in the near future. Strains

506

showed a varying level of resistance for quinolone and non-quinolone groups of the

507

antibiotics. In general, resistance for non-quinolone antibiotics has increased, but the

508

same for quinolones decreased along the downstream, indicating strong influences of

509

the environmental factors. ARG screening test results were in agreement with the

510

antibiotic resistance test. However, for the quinolone resistance, further screening of

511

other types of quinolone resistance determinants is needed. Having identified the

512

critical locations where higher antibiotic-resistant E. coli can be found along with the

513

current level of resistance, treatments need to be developed and implemented to control

514

antibiotic concentrations in wastewater and surface water.

515

21

516

5. Acknowledgment

517

This study was supported by the Asia Pacific Network (APN) under the Collaborative

518

Regional Research Program (CRRP2016-06MY-Kumar). We thank Prof Patrick J Shea for

519

his valuable edits and comments on the work.

520 521

Author Contributions Author contributions were as follows: Manish Kumar supervised

522

and performed the entire interpretation and prepared the final draft of the paper.

523

Bhagwana Ram prepared all the diagram and table and put up the first draft. Sewwandi

524

collected the samples and performed the ARB analyses. Sulfikar performed the ARG

525

experiments. Ryo Honda supervised and guided ARG work while G.G.T. Chaminda

526

supervised sampling and guided analyses of ARB.

527 528

Additional Information Competing Interests: Authors declare no competing interests.

529 530

6. References

531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552

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26

Table

1 2

Austria

Spain

Poland

India

Sri Lanka

Table 1 E. coli prevalence (cfu mL-1) in WWTPs in Sri Lanka, India and comparison with other WWTPs Activat ed sludge

Effluen t

Sample

Source

Influent

JETP

Municipal, industrial

3.9x104

4.5x101

RMTP

Municipal

1.4x104

6.3x101

KHTP

Hospital

2.5x103

2.1x102

Jaspur

Municipal

1.7x104

1.4x104

Chandkhed a Vasna

Municipal

4.2x103

2.9x103

Municipal

9.6x104

Olsztyn

Hospital

9.5x103 6x102 1x105

Lyna WWTP

Municipal

WWTP

Municipal

Vuelta Ostrera

Municipal

Plant A

Municipal

Plant B Plant C

Municipal, landfill Municipal, hospital, nursery home

1.1x103 1.3x105 1.3x103 7.5x104

Receiving water before the outlet

Receiving water after the outlet

Referenc e

1.2x103

1.1x103

Present Study

Korzenie wska et al., 2013

4.5x102 - 6x103 1x102 3x102

Koczura et al., 2012

1.3x104 5.2x105

Pérez et al., 2010

6.1x104

2.1x104

2.3x102

0.6x101

1.2x101

2x104

3.2x104

2.2x102

1.6x101

3.5x101

3.8x104

8.9x103

2.4x102

1.0x101

2.5x101

3 4 5 6 7 8 9 10

1

Reinthale r et al., 2003

ASP

Chandkheda

ASP

Vasna

ASP

WWTP

ASP

Gdansk– Wschod'

ASP

Ireland

WWTP

ASP

WWTP

Austria

Canada

Poland

India

Jaspur

Portugal

Sri Lanka

Table 2 Antibiotic resistance percentage of isolated E. coli in urban WWTPs and comparison with previous studies Resistance to antibiotics (%) Biological WWTP Ref. process LVX CIP NFX KM TC ST I 50 90 55 95 65 75 JETP ASP E 70 75 70 100 95 90 I 60 75 55 65 75 90 RMTP ASP E 75 75 65 85 95 95 I 90 100 100 90 80 90 E 100 100 100 90 100 100 KHTP Ms 90 80 90 50 60 70 20 100 100 100 100 100 100 Present Study 50 90 80 90 70 80 90 I

40

40

40

0

0

0

E I E I E

20 20 20 0 0

0 0 20 0 0

0 20 20 0 0

20 40 60 0 0

0 20 0 20 0

20 0 0 0 0

E

NA

60

50

60

100

80

E

4

4

16

10

NA

NA 23

11

12.4

11.1

22

21.8

32.1

22.2

36.8

22.5

7

4

85

0

8

92

0

3

73

E

10/15

10

I

7.15 NA

NA

E

0.7

I ASP

Dundas Hamilton

NA

Waterdown Plant A

ASP

Plant B

ASP

Plant C

ASP

2.5 NA

NA

E

9.7

E

0

E

NA

NA

0

NA

NA

E

0

I

0

0

21

4

E

0

0

27

4

0

0

6

4

0

0

16

0

0

0

29

2

I E

NA

I

NA

Koczura et al., 2012 Luczkiewicz et al., 2011 Łuczkiewicz et al., 2010 Galvin et al., 2010 Ferreira Da Silva et al., 2007 Edge and Hill, 2005

Reinthaler et al., 2003

E 0 0 35 10 LVX – Levofloxacin, CIP – Ciprofloxacin, NFX – Norfloxacin, KM - Kanamycin Monosulphate, ST – Sulfamethoxazole, TC - Tetracycline, ASP-Activated sludge process, I-Influent, E- Effluent, Ms-municipal sewer 20-20 m downstream, and 50-50 m downstream

11 12 13 14 2

Table 3 Bacterial colonies resistant to multiple antibiotics Percent resistant colonies Location and sample Six Five Four Three point antibiotics antibiotics antibiotics antibiotics Influent 20 50 70 95 JETP effluent 30 70 75 100 Influent 45 45 65 85 RMTP effluent 55 70 75 90 Influent 60 80 100 100 effluent 90 100 100 100 KHTP Municipal sewer 40 50 50 90 20 m downstream 100 100 100 100 50 m downstream 50 60 80 100 Percent of total colonies 11 22 33 56 with 100% resistance 15 16 17 18 19 20

3

Table 4 Antibiotic-resistance gene detection in municipal WWTPs in various countries. Location BielefeldHeepen, Germany

Influent

Aerobic

Final effluent

Refs

NA

aminoglycosides, ESBL, blaTEM, shv, ctx, except vim-4

aminoglycosides, ESBL, blaTEM, shv, ctx except aacA1, aacA7, aadA9, aph(2')-Ib, per2, veb1

Szczepanowski et al., 2009

China, Singapore, USA, Canada

blaOXA1,2,10, ampC, TEM, IMP

NA

Zabrze, Poland

NA

dhfrA, sul1, erm, mef

NA

ZiembińskaBuczyńska et al., 2015

Beijing, China

tet,A, tetB, tetE, tetM, tetz, tetW; sul1, sul2, sul3; gyrA, qnrC, qnrD, parC

NA

NA

Xu et al., 2015

Monastir, CentralEastern Tunisia

sul1>>>erm, Int1, qnrA, blaTEM, qnrS

NA

sul1>>>erm, Int1, qnrA, blaTEM, qnrS

Rafraf et al., 2016

Guangzhou, China

tet, ampC Blactamase genes

tet and ampC genes

only tetA tetM tetS

Xu et al., 2017

Romania

sul1>tetW

NA

sul1>tetW

Lupan et al., 2017

USA

sul1, blaSHV/TEM

sul1, blaSHV/TEM

sul1, blaSHV/TEM

Quach-Cu et al., 2018

Warmia &Mazury District, Poland

Intl2, blaSHV, blaTEM, sul1, tetA, tetM, aac(6)-Ib-cr, qepA

NA

qepA, sul1, tet are highest; blaTEM n blaSHV lowest. aac(6)Ib-cr, Intl2

Korzeniewska and Harnisz, 2018

KHTP

aac-(6')-1b-cr, parC, qnrB, qnrS, ampC, sul1 NA

NA

Present study

RMTP

aac-(6')-1b-cr, qnrB, qnrS, ampC, sul1

NA -Not analyzed

21

4

NA

Y. Yang et al., 2012

1 2

Figure

3 4 5

Figure 1 Map showing sampling locations of influent and effluent samples; a) three

6

WWTPs of Ahmedabad in India represented as S1, S2 and S3; b) three WWTPs in Sri

7

Lanka i.e. two municipal WWTPs at Ja-Ela (JETP) and Rathmalana (RMTP) and one

8

hospital WWTP at Karapitiya Hospital (KHTP); and c) Five samples at KHTP i.e. i)

9

influent, ii) effluent of KHTP, iii) discharge point in a municipal canal, iv) Canal water

10

sample 20 m after the discharge point (K20) and v) canal water 50 m downstream of

11

KHTP effluent discharge (K50).

12 13 14 15 16 17 18 19

1

Resistive

80

80

60

60

CIP (%)

b) 100

LVX (%)

a) 100

40 20

0 c) 100

JI JE RI RE KI KE KD K20 K50 S1I S1E S2I S2E S3I S3E

JI JE RI RE KI KE KD K20 K50 S1I S1E S2I S2E S3I S3E

80 KM (%)

NFX (%)

60 40

60 40 20

JI JE RI RE KI KE KD K20 K50 S1I S1E S2I S2E S3I S3E

0 f) 100

80

80

60

60

ST (%)

TC (%)

40

0 d) 100

20

40

JI JE RI RE KI KE KD K20 K50 S1I S1E S2I S2E S3I S3E

40 20

20

0

0

JI JE RI RE KI KE KD K20 K50 S1I S1E S2I S2E S3I S3E

JI JE RI RE KI KE KD K20 K50 S1I S1E S2I S2E S3I S3E

20

Sensitive

20

80

0 e) 100

Intermediate

Sri Lanka

Sri Lanka

India

India

21

Figure 2 Bar diagram showing resistant percentage to antibiotics a) LVX, b) CIP, c) NFX,

22

d) KM, e) TC, f) ST in JETP, RMTP, and KHTP with I-influent, E-effluent, KD-discharge

23

point in municipal drain, and K20 and K50 - downstream distance in municipal drain of

24

20 m and 50 m, respectively.

25 26 27 28

2

NFX

KM

a) 100

b) 100

% Antibiotic Resistance

CIP

% Antibiotic Resistance

LVX

80

60

40

ST

TC

80

60

40 Inf

Eff

Inf

Eff

% Antibiotic Resistance

c) 100

80

60

d) 40

Dis e) 60

% Antibiotic Resistance

% Antibiotic Resistance

40 Inf

Eff

30 20 10 0

% Antibiotic Resistance

f)

K20

K50

40

20

0 Inf

Eff

Inf

Eff

Inf

Eff

25 20 15 10 5 0

29 30

Figure 3 Effect of treatment and discharge on variation of antibiotic resistance

31

percentage in WWTPs with Inf-influent, Eff-effluent Dis-municipal sewer, K20 and K50 -

32

downstream distance in municipal drains 20 m and 50 m, respectively at a) JETP, b)

33

RMTP, and c) KHTP d) STP Jaspur, e) STP Chandkheda, and f) STP Vasna

34 3

35 36 a)

c)

b)

d)

37 38 39

Figure 4. Statistical analysis results for the samples Principal component analysis diagram a)

40

Influent and b) effluent Cluster analysis diagram c) influent d) effluent

4

Highlights
Antibiotic resistance of E. coli was found increasing after treatment process.
The resistance genes aac-(6')-1b-cr, qnrB, qnrS, ampC, sul1 were detected in WWTPs
Cross-resistance within fluoroquinolones was stronger than non-fluoroquinolones.
Sri Lankan WWTPs exhibit more and consistent increase in resistance after the treatment than India.

भारतीय ौ ोिगक सं थान गाँधीनगर पालज, गांधीनगर, गुजरात 382 355 INDIAN INSTITUTE OF TECHNOLOGY GANDHINAGAR PALAJ, GANDHINAGAR, GUJARAT 382 355

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Japan Society for the Promotion of Science (JSPS) alumni Associate Editor, Hydrological Research Letter Associate Editor, Groundwater for Sustainable Development https://www.researchgate.net/profile/Manish_Kumar138

Declaration:

We declare to have no competing financial interest. We declare no conflict of interest.

Dr. Manish Kumar (Corresponding author)

IITGN

Dr. Manish Kumar Assistant Professor, Earth Sciences, 336A, Block-5