Environ. Sci. Technol. 2010, 44, 580–587
Evidence of Increasing Antibiotic Resistance Gene Abundances in Archived Soils since 1940 C H A R L E S W . K N A P P , †,§ J A N D O L F I N G , † PHILLIP A. I. EHLERT,‡ AND D A V I D W . G R A H A M * ,† School of Civil Engineering & Geosciences, Newcastle University, Newcastle upon Tyne, United Kingdom NE17RU, and Alterra, Wageningen University and Research Center, Soil Science Centre 6700 AA Wageningen, The Netherlands
Received April 24, 2009. Revised manuscript received October 6, 2009. Accepted December 8, 2009.
Mass production and use of antibiotics and antimicrobials in medicine and agriculture have existed for over 60 years, and has substantially benefited public health and agricultural productivity throughout the world. However, there is growing evidence that resistance to antibiotics (AR) is increasing both in benign and pathogenic bacteria, posing an emerging threat to public and environmental health in the future. Although evidence has existed for years from clinical data of increasing AR, almost no quantitative environmental data exist that span increased industrial antibiotic production in the 1950s to the present; i.e., data that might delineate trends in AR potentially valuable for epidemiological studies. To address this critical knowledge gap, we speculated that AR levels might be apparent in historic soil archives as evidenced by antibiotic resistance gene (ARG) abundances over time. Accordingly, DNA was extracted from five long-term soil-series from different locations in The Netherlands that spanned 1940 to 2008, and 16S rRNA gene and 18 ARG abundances from different major antibiotic classes were quantified. Results show that ARG from all classes of antibiotics tested have significantly increased since 1940, but especially within the tetracyclines, with some individual ARG being >15 times more abundant now than in the 1970s. This is noteworthy because waste management procedures have broadly improved and stricter rules on nontherapeutic antibiotic use in agriculture are being promulgated. Although these data are local to The Netherlands, they suggest basal environmental levels of ARG still might be increasing, which has implications to similar locations around the world.
Introduction Over the last few decades, there has been growing concern about antibiotic resistance (AR). The emergence of methicillin-resistant Staphylococcus aureus (MRSA), vancomycinresistant enterococci (VRE), resistant Clostridium difficile and multiresistant pseudomonads, which are epidemic “Super * Corresponding author phone: (44)-0-191-222-7930; fax: (44)-0191-222-6502; e-mail:
[email protected]. † Newcastle University. ‡ Wageningen University and Research Center. § Current address: David Livingstone Centre for Sustainability, Department of Civil Engineering, University of Strathclyde, Glasgow, United Kingdom G1 1XN. 580
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Bugs”, threatens health care systems worldwide. Interestingly, resistance to antibiotic compounds in environmental microorganisms (i.e., species non-native to enteric systems) is a natural phenomenon, especially in soils; however, how the Super Bug epidemic relates to resistance in environmental species is not known. For example, although industrial production of antibiotic and antimicrobial agents has increased substantially since World War II (see Figure S1 in Supporting Information (SI); (1)), concurrent changes in basal AR levels in natural soil communities have not been assessed (2). This is relevant because exposure of environmental organisms to antimicrobial agents, heavy metals, and resistant commensal and pathogenic species might create reservoirs of resistance traits in soil organisms that may have broader consequences to public health. Although the link between resistant Super Bugs and environmental organisms is not defined, it would be of great value to contrast current soil AR levels with historic levels prior to large-scale industrial antibiotic production as a point of first investigation, which is the basis of this study. As a brief history, penicillin was one of the first widely available antibiotics (discovered by Alexander Fleming in 1928) and entered mass production in the early 1940s. It was soon followed by streptomycin, tetracycline, and other antibiotics (3, 4). Eventually, the increased production capacity of antibiotics and decreasing prices encouraged other applications of the drugs outside of medical settings. For example, low levels of antibiotic agents were more frequently being added as a prophylactic to animal feed because they were found to promote growth in livestock (5). As additional applications were identified, antimicrobial production and use continued to increase. Although detailed estimates of annual use and production are broadly not available (6), trade data suggest an exponential increase in antibiotic production prior to 1990 (e.g., Figure S1) with >50% of that manufacture being for agricultural purposes (7, 8). Although discoveries of resistant organisms and medical scares have promoted more prudent use of antimicrobial agents in human medicine (9) and agriculture (10), antibiotic resistance genes (ARG) are still readily detectable in sewage, surface water, oceans, sediments, and soils (11). Further, there is evidence that resistant bacteria in the environment can harbor AR traits, which might potentially confer resistance to pathogens via horizontal gene transfer and other mechanisms (12). Therefore, it is critical to assess whether past antibiotic and antimicrobial use has altered and continues to alter “background” resistance levels in nature to determine whether the chance of environmental organisms conferring resistance to pathogens is increasing. Fortunately, it was recently shown that DNA extracted from dried archived soils provides a wealth of information about historical microbial communities (13, 14). Following this precept, we acquired archived soil samples from rural sites across The Netherlands collected over five long-term experiments of different durations between 1940 and 2008, and quantified 18 resistance determinants in the soils, including ARG for extended spectrum beta-lactamases, tetracyclines, erythromycins, and glycopeptides. Although ARG do not indicate expressed resistance, they reflect the “potential” for resistance, which is the only viable marker for comparing historic samples over time to study long-term trends. Here the archived soils varied according to soil type, irrigation water use, fertilizer applications, and heavy metal exposures, which permitted speculation on factors that might affect local ARG levels over time. Finally, bacterial 16S-rRNA gene levels were determined to compare bacterial levels in 10.1021/es901221x
2010 American Chemical Society
Published on Web 12/21/2009
FIGURE 1. Five locations in The Netherlands from which archived soils were originally collected.
TABLE 1. Summary of Archived Soil Series, Including Sampling Timeframe, Soil Type, Water Use, and Total Fertilizer Use for the Five Sites site
location
years
A
Wieringerwerf 1960-1986
B
Heino
C
Slootdorp
D
Marknesse
E
Wijster
soil type
water use
marine silty loam, intense irrigation from reclaimed from IJsselmeer Waddenzee.
1950-1974 diluvial sand
ditch management, some groundwater (10-15%).
fertilizer use mostly inorganic, some manure (135 ton/ha) on certain plots either manure (1250 ton/ha) or inorganic; history of manure application
marine silty loam, intense irrigation inorganic; manure used in the reclaimed from from IJsselmeer 1960s (80 ton-manure/ha) Waddenzee. ditch irrigation from the IJsselmeer mineral phosphate fertilizers 1976-2008 marine clay (Vollenhovermeer branch) (80 kg/ha) 1940-1975
1978-2008 diluvial sand
ditches: VAM kanaal and Oude Diep
the soils at the time of collection, assess the effect of storage on DNA preservation, and normalize ARG levels to total bacteria within each soil for comparative analysis.
Materials and Methods Archived Soils for Study. Soils were acquired from TAGA, the soil archive of Alterra at Wageningen University and Research Centre, Wageningen, The Netherlands. TAGA includes soils from many previous soil studies that have been archived since 1879; the specific soils used here are summarized in Table 1 (see Figure 1 for locations). It should be strongly emphasized that original soil sampling was not for the purpose of this study and limited background information
mineral phosphate fertilizers (45 kg/ha)
is available for the sites, especially the older soils. Therefore, we must limit our speculation to general trends relative to gross characteristics. For example, Site A was sampled between 1960 and 1986 during an agricultural study on reclaimed marine silty loams in Wieringerwerf (15). The site was reclaimed from the Waddenzee in 1930 and used for agricultural purposes from about 1940 onward. The soil series was collected during an inorganic fertilizer study on crop rotation among oats, wheat, and potatoes that commenced in 1955. This site primarily received mineral fertilizers, including N, P, and K, although it also intermittently received manure for the oats, totalling about 135 ton-manure/ha over the study, although the timing VOL. 44, NO. 2, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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of manure addition was not recorded in detail. Site B samples were collected in Heino in the eastern Netherlands from a humic sandy soil (1950-1974) exposed to either manure or inorganic fertilizer, which ultimately received about 1250 ton-manure/ha over the experiment. It was assumed this site would act as a “positive control” for high residual ARG levels because of higher manure use; however, results indicated otherwise (see later). Samples from Site C were collected from 1940 to 1975 in Slootdorp in the Wieringermeer polder from the top layer of a marine silty loam (0-25 cm) from field experiments assessing K fertilizers (although only non-K-fertilized soils were available here). This experiment received about 80 tonmanure/ha over the experiment, primarily in the 1960s, although exact timing was not recorded. Site D consisted of light marine clay soils similar to soils from Wieringerwerf (sites A and C) and irrigation water was provided from the inlet of the IJsselmeer. Site E had more sandy soils (76.8% sand, 16-2000 µm) with a variable groundwater table (1.5 m in summer). Both sites D and E were experimental tracts testing rock phosphate as fertilizers and had no manure additions. Both had ditch irrigation systems; however, site E received water from VAM kanaal and Oude Diep, rather than the IJsselmeer. Sample Processing and DNA Extraction. Soils were airdried at 30-40 °C, ground, and then stored at room temperature in the archive (15). DNA was extracted from the soils using the UltraClean Soil DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA), with some modifications to the manufacturer protocol. Soils were sterilely weighed into prepared centrifuge tubes containing buffer and extraction beads (by weighing tubes before and after soil addition); usually 150-300 mg (as dry weight) of soil was used. The buffer was prechilled to 4 °C and samples were incubated for 30 min for rehydration. Cells were lysed in a Hybaid Ribolyzer (6.0 setting, 30 s; Hybaid Ltd., Middlesex, UK). The samples were then incubated at 70 °C for 10 min to aid the lysis of Gram-positive bacteria, and briefly reshaken in the ribolyzer. The samples were further purified following manufacturer protocols. DNA eluants were temporarily stored at -20 °C (long-term storage in -80 °C). Current and previous DNA analyses suggest that drying and storing has no major effect on the detectable part of the pro- and eukaryotic communities (15), although this was further verified in the current study. qPCR Methods. Penicillins, tetracyclines, macrolides (such as erythromycins), and vancomycin have been used for up to 80 years, depending on the drug. Resistance determinants for these antibiotics and antimicrobials were chosen for comparison because they represent different drug classes, targets, and modes of action, and have been used differently over time in agriculture and medicine. The abundances of 18 ARG and 16S rRNA genes (16) were quantified here using real-time qPCR (iCycler; BioRad, Hercules, CA). Although it was not possible to quantify all possible genes, the genes chosen crossed drug classes and reflected different mechanisms of resistance. Suites of primers and probes were either selected or designed to specifically target resistance genes, which are summarized Table 2. Assays for tetracycline-resistance determinants (tet(B), tet(L), tet(M), tet(O), tet(Q) and tet(W)) were based on previously published methods (17, 18). The assays for the detection of erythromycin-resistant-methylase determinants (erm(B), erm(C), erm(E) and erm(F)) were designed de novo (see below) with some previously published primers (19), whereas other assays were adapted from previously published methods: vanA (20), vanB (21), mecA (22, 23), and ampC (20), blaTEM-1 (24), blaCTX-M (25), blaSHV-1 (26, 27), and blaOXA-1 (28, 29). Probe and Primer Designs. The 16S rRNA gene assay was based on universal eubacterial probes and primers (16), and is commonly used in many qPCR reactions as a measure 582
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of the total bacteria. The suites of primers and probes for the tet determinants (17, 18), ampC (ampicillin resistance, a group-1, class-C ESBL) (20), vanA (acquired vancomycinresistance gene) (20), and blaCTX-M (a group 2, class D ESBL) (25) were adapted from established methods and used TaqMan probes. A second vancomycin-resistance assay (vanB) was modified from Palladino et al. (21). Minor changes were made to increase the universality of the assay via BLASTn searches on NCBI GenBank Web site (30); in particular, the original forward primer was replaced with the VanB-FL probe sequence, and the VanB-640 was incorporated as the TaqMan probe. The reverse primer (VanBR) remained the same. The methicillin-resistance (mecA) assay included the forward primer by Rieschl et al. (22) and TaqMan and reverse primer by Tan et al. (23). Erm determinants were modified to accommodate the need of qPCR to have shorter amplicons (usually 0.13). Therefore, contamination or differential DNA degradation was not apparent during sample storage, which has been noted elsewhere in carefully archived soils (15). ARG per 16S-rRNA relative abundances (normalized) were calculated for both individual genes and genes associated with each class of antibiotics for the five sampling sites (see Table S2 for the actual data employed, including absolute and relative gene abundances for each soil and site). As a first assessment of ARG trends over time, normalized individual ARG were curve-fit to an exponential model over time to determine general trends among genes at each site (Table 3). Seventy-eight percent (50 of 64) of normalized individual ARGs displayed increasing levels over time, although only 31% of the rate coefficients were statistically significant (p < 0.10). However, four of the five sites had consistently increasing normalized ARG levels; i.e., all coefficients at site C were positive, whereas sites A, E, and D all had less than three negative coefficients. In fact, only site B did not show broadly increasing normalized relative ARG levels. Further, the major classes of antibiotic resistance tested here showed broad increase over time, although increases in tetracycline resistance gene levels were statistically significant most frequently. Although these results are informative and show generally increasing levels of ARG over time, they do not describe longer-term trends in ARG levels because each soil-series represented a different window of time. As such, these normalized values were unitized to average the values observed between 1970 and 1979, which is when data exist for all five time-series. This approach provides a unitized time-series for each ARG and class of ARG that spans 1940 to 2008, and allows comparison of longer-term trends from sites with different background soil and other conditions. Figure 2 (and Table S3, which provides trend data for the individual genes) summarizes trends in unitized data (based on ARG detected at all sites, including site B), and shows significantly increasing trends in ARG abundance since 1940. These trends exist among all tested classes of antibiotics, and are very consistent with trends of normalized genes at each site (Table 3) and trends for individual genes over the longer-term based on the unitized ARG data (Table S3). Figure 2 confirms that the relative proportion of resident bacteria containing ARG is broadly increasing over time, ranging from 2 to 15 times higher in 2008 compared with 1970-1979 levels. It should be noted that trends in Figure 2 display higher levels of statistical significance than Table 3 (p < 0.05), probably because rate coefficients are being estimated based on larger data sets. Among individual ARGs, the highest rates of increase in normalized and unitized ARG abundance were the tetracyclines, particularly tet(Q), tet(O), and tet(M), and also the beta-lactamase, blaTEM-1. Interestingly, we see evidence of elevated extended spectrum betalactamases of the CTX-M family (i.e., blaCTX-M-1; see Table S3) that predate current clinical problems (32), which suggests that this resistance determinant may have originated from soils as has been speculated (33). While the majority of the detected ARG increased in abundance over time, variations definitely existed among genes and among sites, which can be informative of factors that might influence longer term retention of resistance genes in specific locations. Accordingly, various “local” factors were assessed, including ambient soil heavy metal levels; adsorption and drainage characteristics of each soil; differing levels of applied manure or other fertilizers; and varying irrigation practices among sites (see Table 1), and some limited
TABLE 3. Estimated First-Order Rate Coefficients for Individual Antibiotic Resistance Genes (ARG; Normalized to Bacterial 16S-rRNA Gene Levels) for the Five Study Sites over the Defined Sampling Windows (Positive Rate Coefficients Indicate Increasing ARG Levels over Time)
speculation is possible. For example, soil heavy metal levels were measured to determine whether elevated antibiotic resistance in specific soils might be due to previous heavy metal coselection (34). However, soil heavy metal levels showed no significant increases over the 68-year sampling record (see Figure S2 and Table S3 for data). In reality, Figure S2 shows increases in metal levels that parallel increasing ARG levels between 1940 and the late 1960s, but metal levels declined thereafter when unitized ARGs increased the most. Local differences in soil type, irrigation water source and patterns, and fertilization might also elucidate possible factors that influence ARG over time and space, although our observations are only qualitative due to very limited historic documentation. The soil series were not collected for our purposes, and important data, such as other chemicals used at the sites, patterns of manure application, and the quality and amounts of irrigation water used, were neither recorded nor are known. Regardless, some speculation is possible. For example, if one compares ARG trends at the three sites sampled prior to 1986, sites A and C generally display increases in relative ARG levels, whereas site B does not. Although the three sites received both irrigation water and manure, only site B had well-drained sandy soils and did not receive previously contaminated irrigation water. As background, both sites A and C have chronic problems with saltwater intrusion, therefore they have always required extensive freshwater irrigation from IJsselmeer, which, prior to the 1980s, was heavily contaminated by domestic and other waste inputs (35). In contrast, site B received 10 times more manure than sites A and C, although applications were not well recorded. Therefore, based on gene data, it would appear that irrigation water quality (either irrigation water and background saltwater) and soil drainage may be more important than manure application, although this is contrary to our original hypotheses about the effects of manure. Considerable evidence suggests that manure placement can input ARG into soils (36); however, within the context on long-term impacts, irrigation water quality and drainage seem to be more important, although this needs to be verified by further studies. Given that environment protection seems to be improving, the question is why might ARG levels be still increasing now? In our case, the answer may lie in specific practices in The Netherlands, although such local activities are not uncom-
mon around the world. For example, despite greater emphasis on conservative antibiotic use in agriculture, a surprising increase in use has occurred in The Netherlands since 1997 (see Figure S3; (37)). The actual reason for this increase under the more stringent European rules is under debate; however, this increase mimics increases in ARG levels at sites D and E, especially associated with tetracyclines (Table 3 and Figure 2). Regardless of cause, this recent upturn in ARG levels is just another step in apparently increasing resistance in the soils. If one looks back over time, one can see that different factors may have been more or less important during different historic periods, including heavy metal pollution, contaminated surface waters for irrigation, saltwater intrusion, and increasing use of antibiotics in agriculture. In total, our observations imply that single remediation approaches will not likely be successful in reducing overall AR proliferation, and more comprehensive strategies are needed that simultaneously address all causes, especially if antibiotic mass production continues. Although this study shows trends of increasing resistance in The Netherlands, the results argue for more and similar studies around the world because of the work’s implications. For example, our results imply there may be a progressively increasing chance of encountering organisms in nature that are resistant to antimicrobial therapy. With horizontal exchange of genetic material and increased diversity of bacterial hosts, each potential extrinsic source of resistance genes, either in the environment or among commensal organisms, increases the chance of acquired resistance in a pathogen (38). Further, given that soils might act as harbors of beneficial determinants to resident organisms, past antibiotic and antimicrobial exposure may have a lingering effect on the resistance gene pool, even when more prudent antibacterial use is common, although this must still be proven. Overall, our data imply that environmental factors promoting AR proliferation may not have yet been fully defined, which argues for further directed research into links between AR, and environmental and anthropogenic causes. The Standing Medical Advisory Committee’s report, The Path of Least Resistance (39), stated “.. .there is an uneasy sense that micro-organisms are ‘getting ahead’ and that therapeutic options are narrowing”, and our results hint that increasing resistance VOL. 44, NO. 2, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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data from 1940 to 2008 (Table S3); antibiotic production levels in the United States (kton per year) from 1950 to 1986 (Figure S1); heavy metals over time among soils based on unitized data from 1940 to 2008 (Figure S2); and utilization of selected antibiotics in agriculture in The Netherlands between 1997 and 2007 (Figure S3). This information is available free of charge via the Internet at http://pubs.acs.org/.
Literature Cited
FIGURE 2. Relative increase of antibiotic resistance genes among soils collected at five sites in The Netherlands from 1940 to 2008. All values have been normalized to 16S rRNA gene abundances. Normalized values were then grouped according to decade and unitized relative to mean observed values from 1970 to 1979 for each site. Normalization and unitization were required to account for differences in bacterial abundances among sites and place data from each site into a common unit of measure. Each time series represents the unbiased sum of standardized values from all five sites. Table S2 provides detailed data for each site. mecA, blaOXA-1, vanA and ampC were analyzed, but were below detection limits. Shaded areas are the best-fit curves for each class of detected antibiotics assuming a first-order model, which represents the basal level of resistance genes within the soils. Inset rate coefficients are for each class of antibiotic. Rate coefficients for each individual detected gene are provided in Table S3. in soil bacteria may be another factor impacting our battle against global AR.
Acknowledgments We thank T. Schwartz and H. J. Mo¨nstein for providing DNA standards, and William Sloan for comments on the manuscript. C.W.K., J.D., and D.W.G. were funded by ECOSERV, an EU Marie Curie Excellence Programme (MEXT-CT-2006023469).
Supporting Information Available Additional information on the regression analysis of logtransformed 16S-rRNA gene abundances versus time (Table S1); the abundance of 16S-rRNA genes and ARG for each sample-year and sample-site (Table S2); exponential rate coefficients for ARG genes and heavy metals based on unitized 586
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