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Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota 55108,1 Departamento de Microbiología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción, Chile,2 Department of Civil Engineering, University of Minnesota, Minneapolis, Minnesota 55455,3 Instituto de Medicina Preventiva Veterinaria, Facultad de Ciencias Veterinarias, Universidad Austral de Chile, Valdivia, Chile4
Received 28 March 2007/ Accepted 27 June 2007
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ABSTRACT |
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INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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A growing concern is the presence of these antimicrobial compounds in the environment (16). Wastewater from animal agricultural facilities (7), human sewage treatment plants (12), hospitals (19), and pharmaceutical plants (10) has been associated with increased levels of zoonotic pathogens as well as increasingly resistant and virulent organisms. Antibiotics from these sites can be dispersed into the environment and can potentially act as a selection pressure that further influences the acquisition and spread of resistance genes (5, 17, 20, 23). Although antimicrobial compounds can be found in the environment (13), the biological activity of these compounds is uncertain, especially given the low levels that are typically detected in the environment (2). Another challenge, along with incorporating these data into prediction models of antibiotic resistance, is to estimate the fate of these antibiotics in the environment.
Low concentrations of antibiotics have been detected by various techniques in soil, sediment, surface water, groundwater, sewage, and hospital effluents, and these levels are generally on the orders of magnitude of mg/kg, µg/liter, and even ng/liter (11, 13, 14). For example, a recent study of 139 streams located across the United States measured levels of different pharmaceuticals (13). The sampling sites were intentionally biased toward those streams that were under stronger anthropogenic influences. Levels of antibiotics such as chlortetracycline (CTC) ranged from 0 to 0.69 µg/liter. In another study in Iowa, the maximum chlortetracycline level that was detected in streams was 0.1 µg/liter (14). It is difficult to predict the biological effects that this extremely low level has on the bacterial population in the water, especially given the fact that this concentration is likely in constant contact with the bacterial population over an extended period of time. Therefore, the objective of this study was to estimate the selection potential of antibiotics in river water for resistance in aerobic bacterial populations. We performed this investigation in the laboratory using a continuous flow chemostat system containing river water and various concentrations of chlortetracycline. We hypothesized that all antibiotic concentrations would exert a selection pressure on the bacterial population but that the levels of bacterial resistance to tetracycline would be positively correlated with the amount of chlortetracycline added to the system. We selected chlortetracycline for this study because it is commonly used in agriculture and has been detected in aquatic ecosystems, and because many of the genes that code for resistance are well documented.
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MATERIALS AND METHODS |
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Water samples were collected from the Mississippi River adjacent to the Minneapolis Campus of the University of Minnesota. The samples were taken near the bank of the river in sterile bottles and kept obscured from light. The bottles were stored at 4°C and processed within 6 h of collection.
The initial culture medium was prepared similar to a 1/10-strength Luria-Bertani (LB) broth by combining 1 g of tryptone (Difco Laboratories, MI), 0.5 g yeast extract (Difco Laboratories, MI), 0.5 g NaCl, and 10 ml of 1 M phosphate buffer per liter. Initially, 1.5 liter of this medium was prepared by adding the powder components of the 1/10-strength LB broth directly to nonautoclaved river water and mixing by swirling the contents in a sterile graduated cylinder. Five hundred milliliters of this mixture was then placed into each of the three sterile 580-ml laboratory reactors (CYTOLIFT glass airlift bioreactor; Kimble/Kontes, NJ). Aeration was provided by a medical-grade compressed air tank. The chemostats were wrapped in foil, and Teflon tubing was connected to the overflow spout and directed into a waste container. The chemostats were kept at room temperature.
The day that the chemostats were set up corresponded to day –4 of the experiment. The chemostats were allowed to grow for 3 days. On day –1, three broth feed tanks were prepared by adding 8 liters of autoclaved 1/10-strength LB broth to each 10-liter medium reservoir. The medium from each tank was fed to each chemostat through a peristaltic pump with three separate pump heads (Cole-Palmer, IL). The flow rate of the broth medium was set to replace the entire volume (
600 ml) of the chemostat every 24 h (approximately 0.41 ml/min).
On day 0 of each replicate, antibiotic was added to two of the three chemostats. For replicates 1 and 2, CTC (Sigma Aldrich Co., MO) was inoculated into two of the chemostats at concentrations of 8 µg/liter and 32,000 µg/liter, respectively; the third chemostat was maintained as a control. In replicates 3 through 6, CTC was inoculated into two of the chemostats at concentrations of 8 µg/liter and 800 µg/liter, respectively, with the third chemostat again serving as a control. A stock CTC suspension in sterile water was freshly prepared each day and was added twice daily for the duration of the experiment.
Antibiotic quantification.
The amount of CTC present in the chemostats was determined with a commercially available competitive enzyme-linked immunosorbent assay (ELISA) (RIDASCREEN tetracycline; R-Biopharm AG, Darmstadt, Germany). The test was shown to be sensitive and specific for CTC quantification in aqueous samples (15) and was performed and interpreted according to the manufacturer's instructions. The samples for this assay were taken on days 0, 3, 7, and 10 of the experiment. Due to the test range of the ELISA kit, CTC levels were assayed only from the 8-µg/liter chemostat. In addition, all river water samples were frozen for testing immediately after collection. Then, on the days of collection, 1-ml water samples were taken from the chemostat before the morning addition of CTC and then again at 30 min, 120 min, 240 min, and 360 min after the addition of CTC. Using this method, a degradation curve was estimated. After the last sample was taken for the ELISA, the second daily CTC dose was added to the chemostat.
Cultivable aerobic bacterial count.
A 30-ml sample was taken daily from each chemostat, in the morning prior to the addition of the CTC. Samples were collected from days 0 through 10 of each replicate. A 0.5-ml aliquot of each sample was 10-fold serially diluted in sterile water and then plated onto 1/10-strength LB agar and 1/10-strength LB agar supplemented with 16 µg/ml CTC (LB-CTC). The original river water sample was processed on the day of collection in an identical fashion. This original river water sample was thus processed before the addition of any medium. The viable bacterial count was determined by plating 0.1 ml of each 10-fold dilution, and each dilution was plated in duplicate. The LB plates were grown at 30°C for 24 h, and the LB-CTC plates were grown at 30°C for 24 h and checked for growth and then incubated for an additional 24 h, if needed. Colony counts were then based on the average of the duplicate plates.
PCR detection of tet resistance genes.
Daily samples from the chemostat were tested by PCR for the presence of the tetracycline resistance genes tet(A), tet(B), tet(C), tet(D), tet(E), tet(L), tet(M), tet(S), and tet(Q), using previously published primers (4, 18). Fifteen milliliters of the daily sample that was collected from each chemostat was centrifuged at 8,000 x g for 5 min to obtain a pellet. This pellet was suspended in 0.4 ml of Tris-EDTA buffer, and DNA was subsequently extracted from this cell pellet with a commercially available kit (QIAamp DNA Stool minikit; QIAGEN, Valencia, CA). Extracted DNA was stored at –20°C.
For PCR amplification, each 25-µl PCR mixture contained 2.5 µl of 10x PCR buffer, 1.5 µl of MgCl2 (25 mM), 0.125 µl of each primer (30 µM), 0.25 µl of each deoxynucleoside triphosphate (80 mM), 18.4 µl of sterile water, and 0.1 µl of Taq DNA polymerase (Promega, Madison, WI). Two microliters of extracted DNA was used as the template in each reaction. Amplification was performed on a thermocycler (PTC-200; MJ Research, Waltham, MA) using the following programs. For the tet(A), tet(B), tet(C), tet(D), tet(E), tet(L), and tet(M) genes, the program consisted of 95°C for 5 min, followed by 30 cycles of denaturation at 95°C for 1 min, annealing at 55°C for 1 min, and extension at 72°C for 1 min, with a final extension at 72°C for 10 min. For the tet(S) gene, the program consisted of 94°C for 5 min, followed by 30 cycles of denaturation at 94°C for 30 s, annealing at 50°C for 30 s, and extension at 72°C for 30 s, with a final extension at 72°C for 7 min. For the tet(Q) gene, the program consisted of 94°C for 5 min, followed by 30 cycles of denaturation at 94°C for 30 s, annealing at 63°C for 30 s, and extension at 72°C for 30 s, with a final extension at 72°C for 7 min. Control strains were included in each run. Amplicons were separated on 1.5% agarose gels, stained with ethidium bromide, and visualized under UV light.
Statistical analysis.
Data were recorded as the total count of cultivable aerobic bacteria, expressed as CFU/ml, for both the LB and LB-CTC plates over time. The ratio of the LB-CTC count to the LB count was also calculated for each time point for each chemostat. This ratio served as a normalization of the LB-CTC count, adjusting for the total amount of cultivable aerobic bacteria. Means and standard deviations (SD) were calculated for each treatment group among replicates for each time point.
To evaluate the effect of CTC in the chemostats over time, we utilized a general linear model procedure that included a between-subjects factor (the treatment group) and a within-subjects factor (the day). A covariate for replicates was also included in the model to account for interreplicate variability. Differences between consecutive days were assessed with repeated contrasts. Because four of the six replicates used 800 µg/liter as the high concentration in the chemostat, only these four replicates were used in the analyses that compared all three treatment groups (control, 8 µg/liter, and 800 µg/liter). To increase the power of the comparisons between the control and 8-µg/liter chemostats, the analysis was repeated for these two groups, using all six replicates.
Three different models were made for each comparison. First, differences in total CFU/ml on LB plates were compared among treatment groups. In the second analysis, total CFU/ml on LB-CTC plates was compared among treatment groups. In both of these analyses, the total plate counts were log transformed prior to analysis. Finally, for each chemostat on each day, the ratio of the total LB-CTC to LB was compared among the treatment groups. The ratio was square root transformed prior to analysis to improve the normality assumption of the general linear model analysis. Assumptions of the models were evaluated with Box's M test and Mauchly's test of sphericity. Significance levels were set at an 
of 0.05 for all analyses. Standard statistical software was used (SPSS version 14.0; SPSS Inc., IL).
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RESULTS |
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Total bacterial counts on the LB plates over time are shown in Fig. 1A. The total counts averaged between 8 and 9 log10 CFU/ml over the course of the entire experiment for the control, the low-CTC, and the high-CTC chemostats. There were no overall statistically significant differences among treatment groups (P = 0.51). There were several significant daily fluctuations in the total count on LB, occurring on days 4, 6, and 8 (P < 0.01). Finally, there was one significant time by group interaction, occurring on day 5 (P = 0.035), due mainly to an approximate 0.5 log10 decline in the total count in the high-CTC treatment group.
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Finally, the data for the ratio of the LB-CTC-to-LB counts are shown in Fig. 1C. For analysis, the square root transformation of the ratio was used. Similar to the LB-CTC counts, there was a significant increase in the ratio between days 0 and 1 (P = 0.05), and this was the only significant change over time. This change again included the control group. Overall, there were statistically significant differences among the treatment groups (P = 0.039), as well as significant variation among replicates (P = 0.049). The pairwise comparisons of treatment groups showed that the overall ratios in the high-CTC group were significantly higher than those of the control group (P = 0.038) and the low-CTC group (P = 0.019). There was no significant difference between the control and low-CTC groups in this analysis (P = 0.669). In the comparison of all six replicates for the control and low-CTC groups, again there were no significant differences between the groups (P = 0.887). In addition, there were no significant differences among replicates in this second analysis (P = 0.278).
PCR detection of tet resistance genes.
There was variation in the array of tetracycline resistance genes present in each replicate (Table 2). The tet(A-E) efflux genes were more common than the ribosomal protection protein genes tet(L), tet(M), tet(S), and tet(Q). The tet(A) gene was present in all chemostats of all replicates. In general, a greater diversity of tet resistance genes was detected in the high-CTC chemostat and with a greater frequency than in the low-CTC and control chemostats, and the low-CTC chemostat had a greater diversity of tet resistance genes than the control chemostat.
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DISCUSSION |
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In this study, the addition of CTC to the chemostats did not impact the overall level of cultivable aerobic bacteria. There were significant differences among the three treatment groups with respect to the level of tetracycline-resistant bacteria, however. The high-CTC chemostat had significantly higher bacterial colony counts on the LB-CTC as well as significantly higher LB-CTC-to-LB ratios than both the low-CTC and the control chemostats. In the comparisons of the low-CTC and control chemostats, using all six replicates, the differences in resistance were highly nonsignificant. It appears that in this in vitro experiment, low levels of CTC in river water do not select for increased levels of tetracycline resistance among cultivable aerobic bacteria.
Bacteria from the control chemostats were able to grow on LB-CTC plates, even though the original river water samples had no growth on the LB-CTC plates. This could potentially be explained by the change in the microbial community after the medium was added to the sample as well as the fact that the total bacterial count was approximately 3 orders of magnitude greater in the chemostat than in the original sample. Under these circumstances, the resistant bacteria, which comprised less than 0.1% of the total bacterial count, would have a better chance at being detected following plating on LB-CTC.
In the first two replicates of this study, we used a CTC concentration of 32,000 µg/liter in the high-CTC chemostats. This level was used because it was similar to a therapeutic level of tetracycline and would indicate whether there was a biological effect on resistance levels in the chemostat at this high concentration. Because it appeared that there was an effect on resistance, we lowered the concentration of the high-CTC chemostat to 800 µg/liter for replicates 3 through 6. Consequently, there was a total of only 4 replicates with which to compare all three treatment groups, potentially reducing the power of the analysis. Even with this reduced power, we observed a significantly elevated level of resistance for the high-CTC chemostat. Future studies using similar chemostat systems could evaluate the nature of this effect along the CTC range of 8 to 800 µg/liter.
There were several limitations to this study that affect the generalizability of the results to aquatic ecosystems. First, the chemostats were maintained aerobically. Many tetracycline resistance genes, particularly genes that encode ribosomal protection proteins, have been found in cultivable obligate anaerobes (24, 26). In the presence of tetracycline, the spread of resistance may occur through horizontal gene transfer (9, 22). Without the anaerobic populations of bacteria, which potentially serve as reservoirs of resistance (21), the changes in resistance that might occur in a river ecosystem cannot be estimated. In our study, the predominant tetracycline resistance genes that were observed were those that encoded efflux proteins; these genes are most commonly found in gram-negative aerobic bacteria. We did not observe many of the genes encoding ribosomal protection proteins, indicating that populations of obligate anaerobes and many gram-positive aerobic bacteria were not present at detectable concentrations in the chemostats.
A major limitation of this study is the use of the in vitro system that will never be truly representative of the original river water aerobic bacterial population. The selection of specific aerobic bacterial subpopulations is likely due to the effects of the cultivation media. We did not compare the microbial diversity of the original river water with that of the chemostats over time to evaluate this effect. Even though this study was focused on a limited subpopulation of the original water sample bacteria, the purpose of this study was to estimate the potential selection pressure associated with low CTC levels, and therefore, we believe that the results of the study are internally valid, given the assumptions of the study design. Future studies can continue to evaluate the effects of low antibiotic concentrations in systems that are more representative of the natural ecosystem.
A major assumption of this study is that the biological effect of the antibiotic is likely to occur while in suspension in the aquatic ecosystem. In reality, it is possible that the antibiotic will select for resistance in sedimentary microbial populations. Different antibiotics have widely different properties in natural ecosystems, including variation in the strength with which they bind to soils of different types (1, 6, 8). Therefore, the fact that we found no significant biological effect on resistance in the low-CTC chemostat should not be equated with the absence of an environmental risk. Low concentrations of antibiotics in the environment may select for resistant bacterial populations once they are concentrated in soils or other locations.
Ultimately, ecological studies need to be performed to validate the findings of this and other investigations. These studies need to evaluate the relationships between the diversity and the quantity of resistance as a function of environmental concentrations of antibiotics. Unfortunately, these ecological studies are not simple to conduct due to the wide range of selection pressures and the transmission routes that can exist in complex ecosystems (25). Data from in vitro studies ideally should aid in the improved design of ecological investigations. Regardless of whether future studies demonstrate that low concentrations of antibiotics are impacting resistance levels in a detectable manner, attempts should be made now to reduce or eliminate the levels of human-produced antibiotics and antibiotic metabolites in the environment. Additional work is needed in the area of the fate and transport of these antibiotic compounds in order to devise strategies for reducing their concentrations in natural ecosystems.
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ACKNOWLEDGMENTS |
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We thank Ann Kremer, Janet Anderson, Kuldip Kumar, and Ashok Singh for technical assistance.
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FOOTNOTES |
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Published ahead of print on 6 July 2007.
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