MASTER’S THESIS IN
MOLECULAR MEDICAL BIOLOGY
45 hp
HT2012-‐VT2013
Molecular detection of antibiotic resistance genes in
sludge from wastewater treatment
Mohamad Salahaldin
maharith@hotmail.com
Örebro University 2013
Master program in Molecular Medical Biology
Abstract
Bacterial antibiotic resistance is an increasing global health problem, leaving few therapeutic options available for the treatment of pathogenic infections. The development of new antibiotics has been slow since their discovery more than 8 decades, therefore, monitoring the extent and distribution of antibiotic resistance is of great importance. The aim of this study was to determine the presence of antibiotic resistance genes in sludge samples obtained from three wastewater treatment plants (WWTPs) in Sweden. Samples were collected and analyzed for the presence of nalidixic acid (NA), chloramphenicol (CHL), and tetracycline (TC) resistance genes using polymerase chain reaction (PCR). The DNA extracted from Eskilstuna and MälarEnergi sludge showed the presence of NA and TC resistance genes, whereas Örebro sludge was found to have resistance for TC antibiotic genes. To validate the results, PCR detection for resistance genes was performed on Escherichia coli isolates from the sludge samples. Antibiotic susceptibility testing was used to confirm the genetic analysis for antibiotic resistance genes detection in these E. coli. The PCR results for TC resistance genes correlated between sludge PCR analysis and bacterial isolates for all 3 WWTPs. Based on the results obtained from the genotypic analysis of sludge and E coli, incomplete compatibility in regards to NA, and CHL were observed. However on the basis of antibiotic susceptibility testing, E coli isolates from MälarEnergi sludge samples unveiled the majority presence for antibiotic resistance genes. The results suggest that extra monitoring for the wastewater treatment facilities are vital to minimize the rising incidence of antibiotic resistant bacteria.
Keywords
Introduction
The emergence of multidrug resistant bacteria in the environment is increasing and this may lead to a community health risk due to the limitation of treatment for infectious diseases. The extensive use of antibiotics in human medicine eventually ends up in the environment via ineffective sewage treatment (Jury et al., 2010; Kummerer, 2009). Sewage sludge is produced from the primary treatment of wastewater from houses, hospitals, and industries. Sewage water flows through preliminary processing that involves removal of large solid inorganic materials and grit. It is then followed by primary and secondary sewage treatment steps, comprising of the elimination for smaller organic solids and biological particles. In some of the wastewater treatment plants (WWTPs), the primary and secondary treatment may be merged to one basic operation (EPA, 2004b). However, some of the undissolved pollutants and bacteria including endospores cannot be effectively removed by further treatment continue to exist there. They ultimately end up in the environment (EPA, 2004a) thereby contributing towards increased incidence of antibiotic resistance. According to the European commission description for sludge, it originates from wastewater treatment and tends to concentrate heavy metals, nutrients, poorly biodegradable traces of organic compounds as well as potentially pathogenic organisms (European Commission). Although sludge is beneficial in agricultural application by recycling organic matter and nutrients, it can also be a potential source for distribution of antibiotic resistant pathogens.
The most rapid strategy for detection of antibiotic resistance is done via the analysis of genetic material by polymerase chain reaction (PCR), but due to the accumulation of different forms of inhibitors like humic substances and polysaccharides in the sludge this has been an obstacle towards the amplification efficiency of the desired targets (Matheson et al., 2010).
Therefore many modification methods were suggested to reduce these inhibitory effects including the type of DNA polymerase selected in the amplification, DNA purification methods and/or changing the parameters of the reaction (Bessetti, 2007). This can improve the success of amplification of specific genes for a clearer judgment. These methods would eliminate the need for isolation and culturing of individual bacteria.
Antimicrobial substances are natural, synthetic, and semi-synthetic compounds that can selectively kill or inhibit microorganisms. Antibiotics are an example of these natural substances produced by some microorganisms and can have bacteriostatic or bactericidal effects on other competing bacterial species (Davies, 2006). The introduction of antibiotics to treat infections was adopted in 1928 (Prasad and Smith, 2013), and since then a wide and extensive use of such medications has been applied to all types of infections. Their extensive use has led to the emergence of new strains resistant to most therapeutic agents (Zhang et al., 2006). Several factors can lead to the development of antibiotics resistance phenomenon, however, it’s mainly due to misuse and abuse of the available antibiotics (Jury et al., 2010; da Costa et al., 2013).
The presence of antibiotics in the environment provide selective pressure (Tello et al., 2012) for acquiring resistance genes by mutations or from the environment via horizontal gene transfer mechanisms using conjugation, transformation, and transduction (Threedeach et al., 2012; Alanis, 2005; Zhang et al., 2009; da Costa et al., 2013). It is evident that environmental bacteria may be a source of multidrug resistance and novel resistance genes for the pathogens (Li et al., 2010) although, antibiotics are considered as one of the cornerstones for infectious diseases treatment (Davies, 2006). The broad application of antibiotics against all infections caused by both Gram positive and Gram negative bacteria resulted in the
emergence of multidrug resistant pathogen. Combined with the continuous emergence of antibiotic resistant bacteria, the slow development of new drugs can lead to a compromised society health.
Multidrug resistant (MDR) bacteria can be defined as bacterial species resistant to more than one class of antimicrobial agents (Siegel et al., 2006). Infections caused by MDR bacteria are difficult to treat. For example, methicillin resistant Staphylococcus aureus (MRSA) that causes skin and wound infections is resistant to most antibiotics including β-lactams (ampicillin, methicillin, oxacillin, cephalosporin, carbapenems, etc.). MRSA is difficult to treat with conventional antibiotics for staphylococci (Rosenberg Goldstein et al., 2012). Another example is vancomycin resistant enterococci (VRE) that cause urinary tract infections (UTI), bacteremia, and meningitis. VRE isolates are resistant to vancomycin, the drug of choice for the treatment of Gram positive infections. Such isolates have been recently found to be present in the Swedish environment (Elmarghani, 2013). Another pathogenic group of big concern is extended spectrum beta lactamase (ESBL) isolates including members from Enterobacteriaceae, and E. coli (Dahbi et al., 2013). MDR bacteria are an increasing public health problem, and few therapeutic options are available to treat these infections. The presences of these pathogens cause serious community health risks and unnecessary expenditure towards upgrading of health care system. Moreover, a multidisciplinary approaches like bacterial surveillance, infection control procedure and antimicrobial administration should be implemented to reduce the propagation of these bacteria (Silva et al., 2012). The increasing incidence of MDR presence in the environment (Reinthaler et al., 2003; Hilde and Henning, 1994), can lead to the proliferation of health problems in immunocompromised patients which might be very difficult to treat with existing antibiotics.
The escalading dissemination of MDR bacteria in environment were evident via multiple studies during the last century, some of the causes for such spreading attributed to the incomplete (poor) metabolism, the disposal of unused, and improper use of antibiotics (Bouki et al., 2013; Reinthaler et al., 2003). Such bacteria are resistant to varieties of classes of antibiotics like β- lactams, macrolides, fluoroquinolones (quinolones) and tetracycline (Jury et al., 2010). Nalidixic acid (NA) is a broad spectrum, first generation synthetic quinolones antibiotics that was discovered in 1962, and is effective against Gram negative bacteria thus used for the treatment of UTI. Chloramphenicol (CHL) a broad spectrum antibiotic discovered in 1949 and is routinely used as treatment of eye infections and serious infections caused by anaerobes. Tetracycline (TC) is another example of broad spectrum antibiotic, discovered in 1945 and is used against a diverse numbers of infections including UTI, skin infections (acne), sexually transmitted diseases as in gonorrhea and chlamydia, as well eye infections. The dependable and simple use of these antimicrobial substances led to the propagation of antibiotic resistant stains and this narrowed the option for alternative treatment.
The main aim from the current study was to determine the presence of antibiotic resistance genes in sludge. Sewage sludge samples were collected from three different WWTPs in Sweden with the proposing hypothesis that limited or no antibiotic resistance genes to be present as Sweden consider one of the stringent places in the regulation of antibiotics use. The presence of these genes was monitored by PCR from sludge using primers for NA, CHL and TC resistant genes in E. coli as examples for the detection of antibiotic resistance genes. E. coli was a good model for the testing of antibiotic resistance genes since it present as normal microflora in the animal gastrointestinal tract including
human and some strains are also known pathogens. Additionally E. coli is commonly used as a fecal indicator bacterium for water contamination (Chao, 2006).
Methods
Sample collection
Sewage sludge samples were obtained from three different WWTPs in Sweden; Eskilstuna Energi och Miljö (Eskilstuna), Skebäck reningsverket (Örebro) and MälarEnergi (Västerås). Dewatered sludge samples were collected in disposable plastic containers and brought to the lab on ice packs. Afterwards, these sludge samples were processed and stored at 20°C or -80°C.
DNA isolation from sludge
Two methods were used for DNA extraction from the sludge; MO-BIO PowerSoil® DNA Isolation Kit (MO BIO Laboratories Inc. USA) and SoilMasterTM DNA Extraction Kit (Epicentre Biotechnologies, USA). The MO-BIO PowerSoil® is based on bead beating with an inhibitor removal technology (IRT) for isolation, and involved homogenization of sludge using glass beads and vigorous shaking. An amount of 250 mg sludge was used for the extraction according to the manufacturer’s protocol, DNA was finally eluted with 100 µl elution buffer and stored at -20°C until PCR analysis.
The SoilMasterTM DNA Extraction Kit used hot detergent lysis combined with chromatography to remove enzyme inhibitors. Here 100 mg of sludge was used for the extraction purpose according to manufacturer’s recommendations, followed by enzymatic treatment with minimal vortexing steps to reduce DNA shearing. The DNA was eluted with 300 µl tris-ethylenediaminetetraacetic acid (TE) buffer, and stored at -20°C for further use.
DNA Isolation from bacteria
Bacterial isolates were obtained from sludge leachate, plated on ChromoCult™ agar media (Merck, Germany) followed by incubation at 37°C. Bacterial DNA was extracted by boiling a bacterial suspension in water. A loop full of the colonies was added to 200 µl sterile water, microwaved at maximum power for 2 minutes and the lysed suspension was centrifuged for 15 minutes at a full speed (Eppendorf, Australia). The supernatant from the centrifuged tubes was transferred to new 1.5 ml clean plastic tube, DNA was quantified spectrophotometrically using the NanoVue (GE Healthcare, Germany) and then stored at -20°C for further use.
PCR amplification
Presence of antibiotic resistance genes was checked by PCR reaction using iCycler- Thermal cycler (BIO RAD - UK) automated PCR analyzer (Table 1, 2). Three different DNA polymerases were evaluated to determine the optimum PCR amplification, DreamTaq DNA polymerase (Fermentas, Thermo Scientific), Phusion™ High-Fidelity DNA polymerase (FINNZYMES, Thermo Scientific), and Pfu DNA polymerase (native, Thermo Scientific). The reaction mixture and PCR protocols were followed according to the manufacturers recommendation.
PCR reactions for the three polymerases used contained 30 ng of template DNA, 0.2 pmol primers, 0.2 mM of dNTP (Fermentas, Thermo Scientific), and 1X reaction buffer. The concentrations of the polymerase used were, a 1.25 U of DreamTaq™, 0.5 U of Phusion™ High-Fidelity and a 0.625 U of Pfu DNA polymerase for a total of 25 µl mixture. Cycling conditions were; 95°C initial denaturation temperature for 5 minutes and for 45 seconds per cycle, annealing depending on the optimal temperature for each primer (Table 1, 2) set for 45
seconds per cycle, a 72°C extension temperature for 1 minute per cycle and final extension for 10 minutes. Except in the case of Phusion™ High-Fidelity, the initial denaturation temperature was 98° C for 30 seconds and 10 seconds per cycle, the annealing time was 30 seconds per cycle instead of 45 seconds. Total of 30 cycles were used.
The amplified products were separated by gel electrophoresis on a 1.2% agarose gel located inside electrical current running chamber and visualized with ethidium bromide (EtBr) staining. A final concentration of 10µg/µl EtBr was used. Additionally, GeneRuler 100 bp DNA Ladder 0.5 µg/µl and GeneRuler 1 kb Plus DNA Ladder 5.0 µg/µl (Fermentas, Thermo Scientific) were used as markers for the amplicons product sizes. In addition, a negative control without template, and positive control strains of E. coli CO1 and E. coli pBR322 were used to check the functionality and performance of PCR reaction.
Antibiotic susceptibility analysis
E. coli isolates were inoculated in Luria-Bertani agar (LB) and incubated at 37°C for 24 hours (BD Bioscience, Germany). Bacterial suspension was prepared by adding 2-4 colonies to a 5 ml tube containing 0.9% normal saline (NaCl), to achieve absorbance of 0.17 – 0.18 at wavelength of 600 nm (equivalent to 0.5 McFarland standards). The suspension was spread onto Mueller-Hinton agar media (MH, BD diagnostics, EU) using a sterile cotton swab, and the epsilometer test (E-test) strips (bioMérieux, France) were laid on the top of the lawn. The inoculated MH plates were incubated overnight at 37°C. An exponential gradient represented by elliptical zone of inhibition was produced and the minimum inhibitory concentration (MIC) of the drug was identified where the ellipse of growth met the strip. The readings were
interpreted according to the European Union standards for E. coli. The strips concentrations were; TC 0.016-256 µg/mL, CHL 0.016-256 µg/mL and NA 0.016-256 µg/mL (EUCAST).
Ethical approval
No ethical permit was needed for conducting the study, as it did not include the use of animals or humans.
Results
Optimization of DNA isolation from sludge
In order to obtain a sufficient amount and high quality DNA, two different isolation kits were evaluated. The quality of DNA isolated from the sludge was quantified and compared between these kits (Table 3). The difference in total DNA yields was approximately 10 fold higher for PowerSoil® than the amount of SoilMasterTM kit. The amount of DNA from SoilMasterTM was 9.99 ng/mg of sludge comparing to 36.32 ng/mg of sludge from PowerSoil® suggesting that better DNA recovery had been achieved. Although, there were differences in the initial amount of sludge used, the advantage of better DNA yield goes with PowerSoil® kit, even if the same starting sludge amount was used. The DNA isolated using PowerSoil® kit was used for subsequent experiments.
Antibiotic resistance genes detection
Isolated sludge DNA was used as template for amplification of antibiotic resistance genes using 3 different DNA polymerases. It was done to optimize PCR conditions and compare the pattern of amplification with different DNA polymerases (Figures 1-3). The PCR amplification using DreamTaq DNA polymerase led to the presence of multiple bands in the sludge DNA tested (Figure 1). PCR amplification using Phusion™ High-Fidelity DNA polymerase led to a decreased number of multiple bands in the same DNA sample (Figure 2). PCR amplification with Pfu DNA polymerase further reduced the number of non-specific bands (Figure 3). It indicated that different polymerases showed different results with the same sludge DNA despite the fact that all of these polymerases successfully amplified the targeted genes. The best amplification was obtained with Pfu DNA polymerase (Figure 3),
therefore the PCR results obtained using Pfu polymerase were finally used for the comparison with the bacterial analysis.
Comparison of the antibiotic resistance in bacterial isolates
Ten E. coli isolates recovered from the sludge leachate sample from each WWTP (total 30) were used for analyzing the antibiotic resistance pattern. DNA was isolated from each of the isolates and used for PCR amplification of the same antibiotic resistance genes (Table 4). The antibiotic resistance phenotype of the same E. coli strains was also tested using E-test (Table 4). Phenotypically, Eskilstuna showed one isolate that was resistant to NA, and CHL. Two E. coli isolates were found to be resistant to CHL and TC from Örebro in both E-test and PCR, while MälarEnergi revealed the presence of two isolates resistant to CHL, TC from each test and one isolate resistant to NA in both analysis. According to the PCR results, all 30 isolates across the 3 WWTPs were resistant to NA. However, none of the bacterial isolates were resistant to CHL except two from MälarEnergi and one from Örebro. For TC, out of the 10 bacterial isolates, 9 were found to be resistant from Eskilstuna and MälarEnergi and 7 were found resistant from Örebro. From these data, we can conclude that overall MälarEnergi had the most number for antibiotic resistance genes tested in comparison to the other two locations.
Comparison of antibiotic resistance genes in sludge and bacterial isolates
A comparison was made within the results obtained from sludge DNA to bacterial isolates (Table 5). In all WWTPs, the PCR results of TC resistance genes matched closely in sludge
DNA and bacterial isolates. In contrast, the PCR results did not match completely between bacterial isolates and sludge DNA for NA resistance gene, as a difference can be seen in the case of Örebro sludge samples. Similarly, CHL resistance genes differ in the PCR results for Örebro and MälarEnergi sludge samples.
Discussion
In this study, DNA was extracted from WWTPs dewatered sludge samples, in order to monitor the presence of antibiotic resistance genes. The quantity of the extracted DNA indicated that the bead beating method (MO-BIO PowerSoil®) worked better for increased
recovery of DNA isolated from the sludge. This method relied on rapid and thorough homogenization for complete cell lysis and more DNA capture to the silica membrane. The DNA yield from the column chromatography based kit (SoilMasterTM) was found to be much lower in comparison to the first method. It indicated that the bead beating method was the better choice for the recovery of DNA from complex heterogenous mixtures such as sludge.
The presence of antibiotic resistant microorganisms in the sludge could be one of the factors affecting community health; as treated sludge might be finally used for agricultural applications. In this study the occurrence of antibiotic resistance genes in the sludge samples against NA, CHL and TC was tested using PCR as a tool. The reason for choosing the former antibiotics as marker for antibiotic resistance detection was because of (1) the chronological order for their discovery earlier last century (2) the accessibility of these drugs, and (3) the different cellular targets, NA inhibits DNA gyrase activity thus interfering with bacterial replication, TC and CHL prevent protein synthesis by binding and interacting with the 30S and 50S ribosomal subunits, respectively. Since some of the PCR inhibitors present in the sludge DNA can affect PCR amplification process, three different DNA polymerases were tested for PCR amplification purpose. Such inhibitors can either interact with the template or interfere with the enzymatic activity of polymerase (Bessetti, 2007). Therefore, the choice of DNA polymerase in such cases has a large influence on PCR amplification of target gene (Matheson et al., 2010).
In the current study it was observed when sludge DNA was used as template, Pfu DNA polymerase produced the least amount of non-specific PCR products in comparison to DreamTaq and Phusion™ DNA polymerase. According to previous studies, using techniques other than conventional PCR as it retain limitations in the case of heterogenous communities as sludge due to the presence of analogous sequences in DNA between bacteria whether they were Gram negative or Gram positive, which would amplify non-specified genes. Such techniques like polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) can result in improved analysis for such complex communities (Balázs et al., 2013), which would reinforce the current results. Additional measures such as sequencing and extending the antibiotics tested list should be done before generating final conclusions.
The minimal dose of antibiotic which causes inhibition of a given bacteria is known as MIC (Pandey et al., 2011). In this study, the MIC of the antibiotics was measured using E-test to detect antibiotic resistance phenotype in E. coli isolates. It was also used to confirm the existence of antibiotic resistance genes. A study conducted on clinical staphylococci isolates compared identification by sequencing to identification by phenotypic biochemical tests (Heikens et al., 2005) and concluded that genotypic analysis is the more reliable technique. Similarly, another study was conducted to identify MRSA by comparing multiple methods of identification as in disk diffusion, MIC, Latex agglutination and multiplex PCR (Mohanasoundaram and Lalitha, 2008). They concluded from the result of their study that genotypic analysis is a strong confirmatory method for the identification. Based on this knowledge, we decided to use PCR for identification of antibiotic resistance genes as the genes are same in all species (fingerprints like). Contrary to the studies by Heikens and Mohanasoundaram, we found that phenotypic characterization was more reliable than PCR
because the detection of antibiotic resistance genes in a microbe may not necessarily give resistance to that antibiotic. Another attempt was made to compare the result of PCR from the sludge DNA with the results from E. coli isolates. The compiled Table 5 showed that MälarEnergi possesses the highest number of antibiotic resistance genes tested compared to Eskilstuna, and Örebro.
In conclusion, sludge samples from MälarEnergi (Västerås) had the most number of antibiotic resistance genes in comparison to the other two locations. This study was a small part of an extensive study to monitor the presence of antibiotic resistance genes obtained from WWTPs in Sweden. It suggests that additional treatment for wastewater to reduce the presence of antibiotic resistance bacteria in the environment is needed. A similar study indicated that WWTPs can be considered as a major source of antibiotics release to the environment and suggested the implementation of advanced treatment processes like sand filtration, adsorption, oxidation processes and disinfection of the wastewater needed to decrease the spread of antibiotic resistant bacteria in the environment (Rizzo et al., 2013). In general these steps can contribute towards the improvement of community’s health, due to the facts of the limitation for treatment and the delaying in effective therapy which could negatively affect the outcomes on the society (Cosgrove and Carmeli, 2003). All the mentioned risks on community’s heath plus major economic outcomes represented with high costs for not only the patients and families but also to the hospitals from the use of more expensive empirical drugs as second line treatment coupled with extensive diagnostic tests and fear from to the loss of all the advantages in medical care that they have brought about like advanced surgical procedures and cancer chemotherapy might be impossible to perform (ReAct, 2008), will create an anxiety in the society towards the future therefore cautions
Acknowledgements
The author would like to thank Dr. Manish Goswami for providing the bacterial isolates. I would also like to thank Dr. Jana Jass for her guidance and supervision for the study, the group members of system immunology & microbiology for their help, MOD laboratory and teaching staff, family and friends for their support.
References
Ahmed, M. O., Clegg, P. D., Williams, N. J., Baptiste, K. E. & Bennett, M. (2010).
Antimicrobial resistance in equine faecal Escherichia coli isolates from North West England. Ann. Clin. Microbiol. Antimicrob., 9, 3.
Alanis, A. J. (2005). Resistance to antibiotics: are we in the post-antibiotic era?. Arch. Med.
Res., 36, 697-705.
Balázs, M., Rónavári, A., Németh, A., Bihari, Z., Rutkai, E., Bartos, P., Kiss, I. & Szvetnik, A. (2013). Effect of DNA polymerases on PCR-DGGE patterns. Int.
Biodeterior. Biodegrad., 84, 244-249.
Bessetti, J. (2007). An introduction to pcr inhibitors. 9-10.
Bouki, C., Venieri, D. & Diamadopoulos, E. (2013). Detection and fate of antibiotic
resistant bacteria in wastewater treatment plants: a review. Ecotoxicol. Environ. Saf.,
91, 1-9.
Cosgrove, S. E. & Carmeli, Y. (2003). The Impact of Antimicrobial Resistance on Health
and Economic Outcomes. Antimicrob. Resis., 36, 1433-1437.
da Costa, P. M., Loureiro, L. & Matos, A. J. (2013). Transfer of multidrug-resistant
bacteria between intermingled ecological niches: the interface between humans, animals and the environment. Int. J. Environ. Res. Public Health, 10, 278-294.
Dahbi, G., Mora, A., Lopez, C., Alonso, M. P., Mamani, R., Marzoa, J., Coira, A., Garcia-Garrote, F., Pita, J. M., Velasco, D., Herrera, A., Viso, S., Blanco, J. E., Blanco, M. & Blanco, J. (2013). Emergence of new variants of ST131 clonal group
among extraintestinal pathogenic Escherichia coli producing extended-spectrum beta-lactamases. Int. J. Antimicrob. Agents, 5.
Davies, J. (2006). Are antibiotics naturally antibiotics? J. Ind. Microbiol. Biotechnol., 33,
496-499.
Elmarghani, I. M. (2013).Enterococcal distribution and responses to environmental waters,
Licentiate Thesis in Life Science 14. Sweden: Örebro University, 22
EPA, U. S. E. P. A. (2004a) Primer for Municipal Wastewater Treatment Systems.
http://www.epa.gov. 9-13. (accessed 2013-08-04).
EPA, U. S. E. P. A. (2004b) Primer for Municipal Wastewater Treatment Systems.
EUCAST. The European Committee on Antimicrobial Susceptibility Testing. Breakpoint
tables for interpretation of MICs and zone diameters. Version 3.1, 2013., http://www.eucast.org. (accessed 2013-08-04).
European Commission. Environment. European Commission,
http://ec.europa.eu/environment/waste/sludge/index.htm. (accessed
2013-08-04).
Geerlings, S. E., Brouwer, E. C., Gaastra, W., Stolk, R., Diepersloot, R. J. A. &
I.M.Hoepelman, A. (2001). Virulence factors of Escherichia coli isolated from urine
of diabetic women with asymptomatic bacteriuria: correlation with clinical characteristics. Antonie van Leeuwenhoek, 80, 119–127.
Heikens, E., Fleer, A., Paauw, A., Florijn, A. & Fluit, A. C. (2005). Comparison of
genotypic and phenotypic methods for species-level identification of clinical isolates of coagulase-negative staphylococci. J. Clin. Microbiol., 43, 2286-2290.
Hilde, K. & Henning, S. (1994). Transfer of Multiple Drug Resistance Plasmids between
Bacteria of Diverse Origins in Natural Microenvironments. Am. Soc. Microbiol., 60, p.4015-4021.
Jury, K. L., Vancov, T., Stuetz, R. M. & Khan, S. J. (2010), Antibiotic resistance
dissemination and sewage treatment plants. In Current Research, Technology and Education Topics in Applied Microbiology and Microbial Biotechnology, (A. M. Vilas), pp. 509-519. Spain: Formatex Research Center.
Kim, Y.-H., Jun, L. J., Park, S.-H., Yoon, S. H., Chung, J. K., Kim, J. C. & Jeong, H. D.
(2007). Prevalence of tet(B) and tet(M) genes among tetracycline-resistant Vibrio spp. in the aquatic environments of Korea. Dis. Aquat. Org., 75, 209-216.
Kummerer, K. (2009). Antibiotics in the aquatic environment--a review--part I.
Chemosphere, 75, 417-434.
Li, D., Yu, T., Zhang, Y., Yang, M., Li, Z., Liu, M. & Qi, R. (2010). Antibiotic resistance
characteristics of environmental bacteria from an oxytetracycline production wastewater treatment plant and the receiving river. Appl. Environ. Microbiol., 76, 3444-3451.
Matheson, C. D., Gurney, C., Esau, N. & Lehto, R. (2010). Assessing PCR Inhibition from
Humic Substances. Open Enzym. Inhib. J., 3, 38-45.
Mohanasoundaram, K. M. & Lalitha, M. K. (2008). Comparison of phenotypic versus
genotypic methods in the detection of methicillin resistance in Staphylococcus aureus. Indian J. Med. Res. , 127, pp 78-84.
Namboodiri, S. S., Opintan, J. A., Lijek, R. S., Newman, M. J. & Okeke, I. N. (2011).
Quinolone resistance in Escherichia coli from Accra, Ghana. BMC Microbiol., 11, 44.
Pandey, A., Afsheen, Firdous Ara & Tiwari, S. K. (2011). Isolation and Characterization of
Multi Drug Resistance Cultures from Waste Water. J. Pharm. Biomed. Sci., 13, 7.
Prasad, S. & Smith, P. (2013). Meeting the threat of antibiotic resistance: building a new
frontline defence. office of the cheif scientist, 7, 1-4.
ReAct, A. O. A. R. (2008), Economic aspects of antibiotic resistance, ReAct, pp. 4, Uppsala,
Sweden.
Reinthaler, F. F., Posch, J., Feierl, G., Wüst, G., Haas, D., Ruckenbauer, G., Mascher, F. & Marth, E. (2003). Antibiotic resistance of E. coli in sewage and sludge. Water
Res., 37, 1685-1690.
Rizzo, L., Manaia, C., Merlin, C., Schwartz, T., Dagot, C., Ploy, M. C., Michael, I. & Fatta-Kassinos, D. (2013). Urban wastewater treatment plants as hotspots for
antibiotic resistant bacteria and genes spread into the environment: a review. Sci. Total Environ., 447, 345-360.
Rosenberg Goldstein, R. E., Micallef, S. A., Gibbs, S. G., Davis, J. A., He, X., George, A., Kleinfelter, L. M., Schreiber, N. A., Mukherjee, S., Sapkota, A., Joseph, S. W. & Sapkota, A. R. (2012). Methicillin-resistant Staphylococcus aureus (MRSA) detected
at four U.S. wastewater treatment plants. Environ. Health Perspect., 120, 1551-1558.
Sabat, G., Rose, P., Hickey, W. J. & Harkin, J. M. (2000). Selective and sensitive method
for PCR amplification of Escherichia coli 16S rRNA genes in soil. Appl. Environ. Microbiol., 66, 844-849.
Saenz, Y., Brinas, L., Dominguez, E., Ruiz, J., Zarazaga, M., Vila, J. & Torres, C.
(2004). Mechanisms of resistance in multiple-antibiotic-resistant Escherichia coli strains of human, animal, and food origins. Antimicrob. Agents Chemother, 48, 3996-4001.
Siegel, J. D., Rhinehart, E., Jackson, M. & Chiarello, L. (2006). Management of
Multidrug-Resistant Organisms In Healthcare Settings. CDC Bulletin, 1-74.
Silva, d., Rotta, E. T. & Santos, R. P. d. (2012). Multidrug resistant organisms’ incidence in
a university Hospital in Porto Alegre. J. Infect. Control, 1, 3 (23-25).
Tello, A., Austin, B. & Telfer, T. C. (2012). Selective pressure of antibiotic pollution on
bacteria of importance to public health. Environ. Health Perspect., 120, 1100-1106.
Threedeach, S., Chiemchaisri, W., Watanabe, T., Chiemchaisri, C., Honda, R. & Yamamoto, K. (2012). Antibiotic resistance of Escherichia coli in leachates from
municipal solid waste landfills: comparison between semi-aerobic and anaerobic operations. Bioresour. Technol., 113, 253-258.
Wang, H., Dzink-Fox, J. L., Chen, M. & Levy, S. B. (2001). Genetic characterization of
highly fluoroquinolone-resistant clinical Escherichia coli strains from China: role of acrR mutations. Antimicrob. Agents Chemother., 45, 1515-1521.
Zhang, R., Eggleston, K., Rotimi, V. & Zeckhauser, R. J. (2006). Antibiotic resistance as a
global threat: evidence from China, Kuwait and the United States. Global Health, 2, 6.
Zhang, X. X., Zhang, T. & Fang, H. H. (2009). Antibiotic resistance genes in water
Table1. E. coli identification primers
* (1) Prokaryotic universal primers (Geerlings et al., 2001) (2) E. coli specific primers (Sabat et al., 2000)
Target
Gene Primer Sequence 5’-3’ Ta Size Reference*
16S rRNA
16S rRNA For AGGCCCGGGAACGTATTCAC
60 216 (1)
16S rRNA Rev GAGGAAGGTGGGGATGACGT
16S rRNA
ECA75F GGAAGAAGCTTGCTTCTTTGCTGAC
60 544 (2)
ECR619R AGCCCGGGGATTTCACATCTG
Table 2 primers for E. coli antibiotic resistance genes
Target Gene Primer Sequence 5’-3’ Ta
P.
Size Reference*
Nalidixic acid
gyrA gyrA1F GTGTTATAAACCGCCAAGTC 60 102 (1) gyrA1R ACTAGGCAATGACTGGAACA
gyrB gyrBF CAGACTGCCAGGAACGCGAT 60 204 (2)
gyrBR AGCCAAGCGCGGTGATAAGC
parC parCF CTGAATGCCAGCGCCAAATT 60 188 (3)
parCR GCGAACGATTTCGGATCGTC
Chloramphenicol
catI catIF AGTTGCTCAATGTACCTATAACC 55.3 585 (4)
catIR TTGTAATTCATTAAGCATTCTGCC
catII catIIF ACACTTTGCCCTTTATCGTC
catIIR TGAAAGCCATCACATACTGC 55.3 495 (4) cmlA cmlAF TGTCATTTACGGCATACTCG 55.3 455 (5)
cmlAR ATCAGGCATCCCATTCCCAT
Tetracycline
TETF GCGCTNTATGCGTTGATGCA 54 (6)
tetA TAR ACAGCCCGTCAGGAAATT 54 387 (6)
tetB TBR TGAAAGCAAACGGCCTAA 54 171 (6)
tetC TCR CGTGCAAGATTCCGAATA 54 631 (6)
tetD TDR CCAGAGGTTTAAGCAGTGT 54 484 (6) P. –Product, Ta –annealing Temperature
* (1) (Elmarghani, 2013) (2) (Wang et al., 2001) (3) (Namboodiri et al., 2011) (4) (Ahmed et al., 2010) (5) (Saenz et al., 2004) (6) (Kim et al., 2007)
Table 3 Comparison of the DNA Extraction methods used for sludge
Kit Method Sludge (mg) Conc.
ng/µl Total DNA (ng) Epicentre SoilMasterTMDNA Extraction Column chromatography 100 11.1 999 MO BIO
PowerSoil®DNA Isolation
Bead beating
NA –Nalidixic acid; CHL –Chloramphenicol; TC –Tetracycline; Geno –Genotypical; Pheno –Phenotypical.
Table 4 Phenotypical and Genotypical comparisons for E. coli isolates
Antibiotics:
location NA Geno Pheno CHL Geno Pheno TC Geno Pheno
Eskilstuna EA1301-2C + - - - + - EA1301-3C + - - - + - EA1301-4C + - - - - - EA1301-5C + - - - + - EA1301-6C + + - - + - EA1301-7C + - - - + - EA1301-8C + - - - + - EA1301-9C + - - + + - EA1301-10C + - - - + - EA1301-11C + - - - + - Örebro OA1301-1C + - - - + - OA1301-2C + - - - - - OA1301-3C + - - - - - OA1301-4C + - - - + - OA1301-5C + - - - - - OA1301-6C + - + + + + OA1301-7C + - - - + - OA1301-8C + - - - + - OA1301-9C + - - - + - OA1301-10C + - - - + - MälarEnergi MA1301-1C + - + + + + MA1301-2C + - - - + - MA1301-3C + - + + + + MA1301-4C + - - - + - MA1301-5C + - - - + - MA1301-6C + - - - + - MA1301-7C + - - - + - MA1301-8C + + - - + - MA1301-9C + - - - + - MA1301-10C + - - - - -
Table 5 Comparison of antibiotic resistance genes from sludge and E. coli
Nalidixic acid Chloramphenicol Tetracycline Eskilstuna E. coli + - + sludge + - + Örebro E. coli + + + sludge - - + MälarEnergi E. coli + + + sludge + - +
Figure legends
Figure 1-‐ Gel electrophoresis images of the PCR products from sludge DNA when using
DreamTaq DNA polymerase. (A) Eskilstuna; lane 1, 1 Kb plus DNA ladder; lane 2, negative control; lane 3, 4, positive controls E. coli CO1 with universal 16S rRNA and 16S rRNA E. coli specific primers respectively; lanes 5-7 denote for chloramphenicol genes catI, catII, and cmlA consecutively; lanes 8-10 denote for nalidixic acid genes gyrA, gyrB, and parC respectively; and lane 11 shows the presence of tetracycline multiplex of tetA, tetB, tetC, and tetD. (B) Örebro; lane 1, 1Kb plus ladder; lane 2-4 depict negative control and positive controls respectively as in (A); lane 5-7, presents nalidixic acid genes; lane 8-10 represent chloramphenicol genes; lane 9, show the presence of tetracycline multiplex genes. (C) MälarEnergi run for E. coli antibiotic resistance genes; lane 1, 1Kb plus ladder; lane 2, 3 depict negative control and positive control, respectively as in (A, B); lane 4-6, present nalidixic acid as in (B); lane 7-9 represents chloramphenicol as (B); and lane 10, show the presence of tetracycline multiplex genes as in (B).
Figure 2- Gel electrophoresis of the PRC products from sludge DNA when using Phusion
High-Fidelity DNA polymerase. (A) Presents: lane 1, 100 bp DNA ladder; lane 2, negative control; lane 3 positive control E. coli CO1 with universal 16S rRNA primer; lanes 4-6 denote for nalidixic acid genes gyrA, gyrB, and parC consequently for Eskilstuna; lanes 7-9 presents NA genes respectively for Örebro; and lanes 10-12 shows the presence of the NA genes for MälarEnergi. (B) Presents the run of chloramphenicol genes catI, catII, and cmlA; lane 1, 100 bp ladder; lane 2, 3 depict negative control and positive control respectively as in (A), the positive control was E. coli CO1 with 16S rRNA E. coli specific primers; lane 4-6, present
Eskilstuna; lane 7-9 presents Örebro; and lanes 10-12, presents MälarEnergi. (C) Presents the run for tetracycline multiplexgenes of tetA, tetB, tetC, and tetD as follows: lane 1, 100 bp DNA ladder; lane 2 depict negative control and lane 3 shows positive control E. coli pBR322 tetC; lane 4 present Eskilstuna template; lane 5 represent Örebro; and lane 6, shows MälarEnergi.
Figure 3- Gel electrophoresis of the PCR products from sludge DNA by using Pfu DNA
polymerase. (A) Presents: lane 1, 100 bp DNA ladder; lane 2, negative control; lane 3 positive control E. coli CO1 with universal 16S rRNA primer; lanes 4-6 denote for nalidixic acid genes gyrA, gyrB, and parC consequently in Eskilstuna; lanes 7-9 presents Örebro genes of NA; and lane 10-12 shows the presence of same genes for MälarEnergi. (B) Presents the run of chloramphenicol genes catI, catII, and cmlA for E. coli as follows: lane 1, 100 bp ladder; lane 2, depict negative control and lane 3 shows positive control E. coli CO1 with 16S rRNA E. coli specific primers; lane 4-6, present Eskilstuna; lane 7-9 represent Örebro; lanes 10-12, represent MälarEnergi. (C) Presents the run TC multiplexgenes of tetA, tetB, tetC, and tetD as follows: lane 1, 100 bp ladder; lane 2, depict negative control and lane 3 shows positive control E. coli pBR322 tetC; lane 4 present Eskilstuna; lane 5 Örebro; and lane 7, show MälarEnergi.
1 2 3 4 5 6 7 8 9 10 11 A 500 - C 500 - B 500 - 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 11 CHL NA TC NA CHL TC
1 2 3 4 5 6 7 8 9 10 11 12
A 500 -1 2 3 4 5 6 7 8 9 10 11 12
B 500 -1 2 3 4 5 6
C 500 - Figure 2 - 631 tetC - 484 tetD - 387 tetA - 188 tetB - 455 cmlA - 495 catII - 585 catI - 102 gyrA - 188 parC - 204 gyrB32
