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Using multiple antibiotic resistance profiles of coliforms as a tool to investigate combined sewer overflow contamination.

Introduction

The Anacostia River is an urban tributary in a highly industrial surrounding, making it a dynamic and unique environment in which to study fecal pollution. It flows approximately 8.5 miles from Prince George's County, Maryland, through Washington, DC, before finally joining the Washington Canal and emptying into the Potomac River. Its watershed covers 176 square miles and contains 13 subwatersheds (National Resources Defense Council, 2016).

Due to the District of Columbia's combined sewer overflow (CSO) system, which allows the release of untreated wastewater directly into the river, the area poses a risk to public health. The fecal coliform bacteria and other pathogens deposited into the river debilitate water quality and create hypoxic conditions, leading to large-scale fish death and the deterioration of local wildlife (Stoddard et al., 2008). This problem occurs when excessive rainfall overwhelms the internal barrier that keeps the water runoff and sewage waste separated. When this occurs, wastewater is directed from sewage lines into the river. CSO accounts for an estimated 73% of the average annual increase in fecal coliform bacteria along the District of Columbia region of the Anacostia River, amounting to 348 trillion most probable number (MPN) fecal coliforms per year (District of Columbia Water and Sewer Authority, 2002). The Washington Suburban Sanitary Commission estimates 839 overflows occur each year, releasing an estimated 2.5 billion gallons of untreated water into the environment (District of Columbia Water and Sewer Authority, 2004).

While coliform bacteria will not likely cause illness, their presence in drinking water indicates that disease-causing organisms (pathogens) could be in the water system. Fecal coliforms have frequently been surveyed as indicators of the potential presence of human enteric pathogens (Meng, Fratamico, & Feng, 2015). Fecal coliforms are gram-negative bacilli able to ferment lactose at elevated temperatures and include species such as E. coli and Klebsiella pneumoniae (Holt & Krieg, 1994). The presence of antimicrobial-resistant coliforms in water samples is a strong indicator of fecal pollution from animal and/or human sources. Studies have shown major sources of fecal water pollution can be determined by conducting a multiple antibiotic resistance (MAR) analysis (Hagedorn et al., 1999; Scott, Rose, Jenkins, Farrah, & Lukasik, 2002; Simpson, Santo Domingo, & Reasoner, 2002), or as it is now frequently called, an antibiotic resistance analysis.

MAR can be used to differentiate fecal E. coli (and occasionally enterococci) from different loci by assessing the resistance profiles from bacterial isolates using antimicrobial agents with varied purposes to reveal the type of contaminating microbiome (Parveen et al., 1997; Whitlock, Jones, & Harwood, 2002). MAR analysis includes both library-dependent and nonlibrary-dependent approaches for studying and tracking the sources of microbial pollution (called bacterial source tracking). Several studies, for example, have focused on comparing MAR profiles of Enterococcus isolates with known source libraries for determining and tracking the microbial pollution source (Wiggins, 1996). Our approach, on the other hand, has been to use the nonlibrary approach, which does not use fecal coliforms from known sources (e.g., exclusively from human, livestock, or wildlife origins); this offers more rapid results, which is useful when human health hazards are suspected (Kaspar, Burgess, Knight, & Colwell, 1990).

Few studies have been carried out to determine the variance of MAR profiles of fecal coliforms in the Anacostia watershed area. Therefore, our research links pollution-derived coliforms levels (CSO versus nonpoint source [NPS]) and antimicrobial resistance in water samples to provide insight into the selective pressures exerted by antimicrobial use in an urban watershed. The establishment of these standards also has the potential to facilitate the detection of contamination sources, serving as a useful monitoring tool for improved planning and proper water quality management.

Methods

Collection of Samples

Both CSO and NPS samples were collected in September 2011 along the Anacostia River between the John Philip Sousa Bridge and the 11th Street Bridge. CSO samples were taken from CSO-17, located at 38[degrees] 87' 56.97" N and 76[degrees] 98' 50.72" W, and CSO-18, located at 38[degrees] 87' 70.08" N and 76[degrees] 98' 12.23" W. These sites drain a 291-acre area consisting of 84% residential and 16% commercial land. At these sites, 0.4 inches of rainfall cause an overflow to occur, which leads to a combined 67 overflows a year approximately, releasing an estimated 26-million gallons of untreated water into the Anacostia (District of Columbia Water and Sewer Authority, 2004). NPS samples were taken at midstream 200 feet east of the John Philip Sousa Bridge at 38[degrees] 87' 74.46" N and 76[degrees] 97' 94.28" W. Three 1-L samples were collected at each area. Samples were stored in sterile plastic collection bags at 4 [degrees]C and analyzed within 24 hours.

Isolation, Enumeration, and Identification of Fecal Coliforms

We filtered 50 mL portions of the samples recovered through a 0.2 pm pore-size nitrocellulose filter. Filters were then incubated at 42.5[degrees]C after being placed on des oxycholate agar and further differentiated on Hektoen enteric agar and MacConkey agar. These selective agars, which were all made in-house using Difco agar powder, were used to confirm the isolation of presumptive fecal E. coli, as they both differentiate for gram-negative enteric bacilli. Using sterile toothpicks, the presumptive E. coli colonies were transferred to a master MacConkey agar plate in an 8 x 8 colony-grid and stored at 42.5 [degrees]C in preparation for MAR analysis. A total of 192 colonies were isolated from NPS samples and 128 colonies were isolated from CSO samples.

MAR Analysis

The method of Kaspar and co-authors (1990) was used for MAR analysis, including antimicrobial agents chosen, concentrations, and resistance denotation. Stock solutions of antimicrobials used in animal feeds (chlortetracycline and oxytetracycline) and clinical applications were filtered, sterilized, prepared, and infused onto Mueller-Hinton (MH) agar plates (Krumperman, 1983). The following concentrations were used: 10 [micro]g/mL ampicillin, 25 [micro]g/mL chlortetracycline, 25 pg/mL oxytetracycline, 25 [micro]g/mL nalidixic acid, 50 [micro]g/mL chloramphenicol, 50 [micro]g/mL kanamycin, 50 [micro]g/mL streptomycin, and 25 [micro]g/mL tetracycline. Antimicrobial agents were commercially obtained from Sigma-Aldrich. Isolates were then replica plated onto each of the eight antimicrobial plates and a control plate that lacked any antimicrobial agent. Replica plating was done by using sterile toothpicks to transfer isolates from the master 8 x 8 MacConkey agar grid plates to the corresponding 8 x 8 inoculated MH agar plate grid. Isolates were given identification numbers to ensure proper replication.

Plates were then incubated at 42.5 [degrees]C for 24 hours. Isolates were identified as antimicrobial resistant if growth on the antibiotic-containing agar was indistinguishable from that on the control plate without an antimicrobial agent (Hagedorn et al., 1999). MAR indices for each sample site were calculated as: [(the number of antimicrobial agents to which all isolates were resistant)/ (number of antimicrobials tested x number of isolates inoculated per site)] (Kaspar et al., 1990). Significant differences between antimicrobial-resistance patterns at each site were determined by a two-sided test of binomial proportion (p < .05). Interiso late relationships were examined by using DendroUPGMA (Garcia-Vallve, Palau, & Romeu, 1999).

Results

Isolates from both CSO sources showed significantly greater resistance (p < .05) and higher MAR indices than the NPS sites, with an average MAR index of 0.36. In contrast, NPS isolates exhibited resistance with an average MAR index of 0.07. Euclidian metric analysis showed that isolates from the CSO sources contained 41 different resistance patterns compared with 15 among NPS isolates. It was also revealed that 96.9% of CSO samples exhibited resistance, with 54.7% being resistant to three or more different antimicrobial agents. With respect to NPS samples, only 43.8% exhibited resistance and 3.1% were resistant to three or more different antimicrobial agents (Table 1).

CSO samples expressed resistance to all eight antimicrobial agents in 7.8% of the samples. NPS samples showed resistance to no more than six antimicrobial agents. Ampicillin resistance was the most prevalent of all the antimicrobials tested, observed in 96.0% of CSO isolates, which exhibited some type of resistance and in 96.4% of resistant NPS isolates (Figure 1).

Discussion

Our results show that CSO samples have a greater proportion of multiple drug resistant coliforms, consistent with the hypothesis that pollution-derived coliform levels are strongly linked to antimicrobial resistance. These results are consistent with other studies that have shown a similar correlation between the abundance of antimicrobial resistance and pollution (Ash, Mauck, & Morgan, 2002; Hagedorn et al., 1999; Kaspar et al., 1990; Parveen et al., 1997; Whitlock et al., 2002; Young, Juhl, & O'Mullan, 2013). In addition, we noted that the majority of isolates exhibited resistance to ampicillin. This result, too, was reflected in similar studies of antibiotic resistant bacteria in rivers (Ash et al., 2002; Young et al., 2013). We have also carried out preliminary minimum inhibitory concentration (MIC) testing on some of our isolates against ampicillin and have found that MIC values are considerably high, up to 1,000 [micro]g/mL (D. Morris, personal communication, January 15, 2012).

It is possible that this resistance (and others) might be acquired by horizontal gene transfer, presumably through conjugation. The samples for this study were collected at the lower watershed area of the Anacostia River, where the flow of water is sluggish and warm in the late summer and early fall. These factors might encourage gene exchange through horizontal gene transfer. We have no direct evidence, however, that this occurs. Nevertheless, this area might well function as a reservoir of resistant bacteria.

Due to financial and resource constraints, the study lacked antimicrobial agents that are more currently in use and that are able to differentiate if the contamination is from human, livestock, or pet sources. Future studies should use antimicrobial agents such as cephalosporins, fluoroquinolones, and trimethoprim, which are now more commonly in use over some of the redundant (oxytetracycline, chlortetracycline, and tetracycline) antimicrobials or ones that are no longer commonly used to treat clinical infections (kanamycin, chloramphenicol, and nalidixic acid) to better determine the pollution source.

Conclusions

While the District of Columbia Water and Sewer Authority and the Anacostia Watershed Society have made efforts to remediate the Anacostia River (National Resources Defense Council, 2016), the careful monitoring of bacterial populations is still necessary. We believe that using MAR profiles on sites along the Anacostia River (CSO and NPS), as we have described here, is a useful and simple tool for monitoring the rehabilitation of the Anacostia watershed area. While this study focused on examining the effects of a CSO system, the potential of using MAR profiles goes beyond the scope of this study. We are able to conclude, based on this study, that the health risks of CSO sites, which are more strongly linked to antimicrobial resistance than NPS sites, highlight the impact of pollution-derived contamination. It is also feasible, however, to determine the source of pollution by examining the relative resistance of antimicrobial agents based upon their use (e.g., humans, farm animals, and pets).

This research project indicates the ability of MAR profiles to be used as a marker of the extent of sewage contamination. With the appropriate methodological modification, it also demonstrates the possibility of using MAR profiles as a monitoring tool to indicate the type of contaminating microbiome. This technique can be applied to water systems around the country in need of rehabilitation and monitoring for improved water quality management.

Gaurav Dhiman

Emma N. Burns

David W. Morris, PhD

Department of Biological Sciences

The George Washington University

Acknowledgements: The authors would like to thank Mark Mallozzi for his help in compiling the data and the 2010 George Washington University Dean's Seminar Course, "Do We Need Biotechnology?," that served as the catalyst for this paper. This work was supported by a grant from the University of the District of Columbia Water Resources Research Institute.

Corresponding Author: Gaurav Dhiman, Department of Biological Sciences, The George Washington University, 2023 G Street NW, Lisner Hall 348, Washington, DC 20052. E-mail: gdhiman@gwmail.gwu.edu.

References

Ash, R.J., Mauck, B., & Morgan, M. (2002). Antibiotic resistance of gram-negative bacteria in rivers, United States. Emerging Infectious Diseases, 8(7), 713-716.

District of Columbia Water and Sewer Authority. (2002). Combined sewer system long term control plan. Retrieved from http://www. dcwater.com/workzones/projects/pdfs/ltcp/Complete%LTCP %For%CD.pdf

District of Columbia Water and Sewer Authority. (2004). CSO overflow predictions for average year. Retrieved from_https://www. dcwater.com/wastewater_collection/css/CSO%20Overflow%20 Predictions%20%20for%20Average%20Year.pdf

Garcia-Vallve, S., Palau, J., & Romeu, A. (1999). Horizontal gene transfer in glycosyl hydrolases inferred from codon usage in Escherichia coli and Bacillus subtilis. Molecular Biology and Evolution, 16(9), 1125-1134.

Hagedorn, C., Robinson, S.L., Filtz, J.R., Grubbs, S.M., Angier, T.A., & Reneau, R.B., Jr., (1999). Determining sources of fecal pollution in a rural Virginia watershed with antibiotic resistance patterns in fecal streptococci. Applied and Environmental Microbiology, 65(12), 5522-5531.

Holt, J.G., & Krieg, N.R. (Eds.). (1994). Bergey's manual of determinative bacteriology. 9th ed. (p. 787). Baltimore, MD: Williams & Wilkins.

Kaspar, C.W., Burgess, J.L., Knight, I.T., & Colwell, R.R. (1990). Antibiotic resistance indexing of Escherichia coli to identify sources of fecal contamination in water. Canadian Journal of Microbiology, 36(12), 891-894.

Krumperman, P.H. (1983). Multiple antibiotic resistance indexing of Escherichia coli to identify high-risk sources of fecal contamination of foods. Applied and Environmental Microbiology, 46(1), 165-170.

Meng, J., Fratamico, P.M., & Feng, P (2015). Pathogenic Escherichia coli. In Y. Salfinger & M.L. Tortorello (Eds.), Compendium of methods for the microbiological examination of foods (5th ed.). Washington, DC: American Public Health Association.

National Resources Defense Council. (2016). Healing the Anacostia's troubled waters. Retrieved from http://www.nrdc.org/water/pollution/fanacost.asp

Parveen, S., Murphree, R.L., Edmiston, L., Kaspar, C.W., Portier, K.M., & Tamplin, M.L. (1997). Association of multiple-antibiotic-resistance profiles with point and nonpoint sources of Escherichia coli in Apalachicola Bay. Applied and Environmental Microbiology, 63(7), 2607-2612.

Scott, T.M., Rose, J.B., Jenkins, T.M., Farrah, S.R., & Lukasik, J. (2002). Microbial source tracking: Current methodology and future directions. Applied and Environmental Microbiology, 68(12), 5796-5803.

Simpson, J.M., Santo Domingo, J.W., & Reasoner, D.J. (2002). Microbial source tracking: State of the science. Environmental Science & Technology, 36(24), 5279-5288.

Stoddard, R.A., A twill, E.R., Gulland, EM., Miller, M.A., Dabritz, H.A., Paradies, D.M., ... Conrad, PA. (2008). Risk factors for infection with pathogenic and antimicrobial-resistant fecal bacteria in northern elephant seals in California. Public Health Reports, 123(3), 360-370.

Whitlock, J.E., Jones, D.T., & Harwood, VJ. (2002). Identification of the sources of fecal coliforms in an urban watershed using antibiotic resistance analysis. Water Research, 36(17), 4273-4282.

Wiggins, B.A. (1996). Discriminant analysis of antibiotic resistance patterns in fecal streptococci, a method to differentiate human and animal sources of fecal pollution in natural waters. Applied and Environmental Microbiology, 62(11), 3997-4002.

Young, S., Juhl, A., & O'Mullan, G.D. (2013). Antibiotic-resistant bacteria in the Hudson River Estuary linked to wet weather sewage contamination. Journal of Water and Health, 11(2), 297-310.

TABLE 1

Antimicrobial Resistance Profile

Sample     Total #    # of Isolates Resistant to Each Antimicrobial
Source     of         Agent (% of Total E. coli Isolates)
           Isolates

                      Amp        Cam        Ctet       Kan

NPS        192        81         2          1          4
                      (42.2%)    (1.0%)     (0.5%)     (2.1%)
CSO        128        119        30         47         26
                      (92.9%)    (23.4%)    (36.7%)    (20.3%)

                      Isolates Resistant to # of Antimicrobial
                      Agents (% of Total E. coli Isolates)

                      Zero       One        Two        Three
NPS                   108        53         25         1
                      (56.3%)    (27.6%)    (13.0%)    (0.4%)
CSO                   4          33         21         26
                      (3.1%)     (25.8%)    (16.4%)    (20.3%)

Sample     # of Isolates Resistant to Each AntimicrobiaTotal #
Source     Agent (% of Total E. coli Isolates)         of
                                                       Resistant
                                                       Isolates

           Nal        Otet       Strp       Tet

NPS        8          21         2          8          84
           (4.2%)     (10.9%)    (1.0%)     (4.2%)     (43.8%)
CSO        61         19         25         46         124
           (47.7%)    (14.8%)    (19.5%)    (35.9%)    (96.9%)

           Isolates Resistant to # of Antimicrobial
           Agents (% of Total E. coli Isolates)

           Four       Five       Six        Seven      Eight
NPS        3          1          1          0          0
           (1.6%)     (0.4%)     (0.4%)     (0.0%)     (0.0%)
CSO        10         7          8          9          10
           (7.8%)     (5.5%)     (6.3%)     (7.0%)     (7.8%)

Sample     Average
Source     MAR
           Index

NPS        0.07

CSO        0.36

NPS

CSO

NPS = nonpoint source; CSO = combined sewer overflow; MAR = multiple
antibiotic resistance; Amp = ampicillin; Cam = chloramphenicol; Ctet =
chlortetracyline; Kan = kanamycin; Nal = nalidixic acid; Otet =
oxytetracycline; Strp = streptomycin; Tet = tetracycline.


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Title Annotation:ADVANCEMENT OF THE SCIENCE
Author:Dhiman, Gaurav; Burns, Emma N.; Morris, David W.
Publication:Journal of Environmental Health
Article Type:Report
Geographic Code:1USA
Date:Oct 1, 2016
Words:2768
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