Correlation Between Upstream Human Activities ... - ACS Publications

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Correlation Between Upstream Human Activities and Riverine Antibiotic Resistance Genes Amy Pruden,*,†,‡,§ Mazdak Arabi,‡,§ and Heather N. Storteboom‡ †

Via Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, U.S.A., 24061 Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, U.S.A., 80523

‡

S Supporting Information *

ABSTRACT: Antimicrobial resistance remains a serious and growing human health challenge. The water environment may represent a key dissemination pathway of resistance elements to and from humans. However, quantitative relationships between landscape features and antibiotic resistance genes (ARGs) have not previously been identified. The objective of this study was to examine correlations between ARGs and putative upstream anthropogenic sources in the watershed. sul1 (sulfonamide) and tet(W) (tetracycline) were measured using quantitative polymerase chain reaction in bed and suspended sediment within the South Platte River Basin, which originates from a pristine region in the Rocky Mountains and runs through a gradient of human activities. A geospatial database was constructed to delineate surface water pathways from animal feeding operations, wastewater treatment plants, and fish hatchery and rearing units to river monitoring points. General linear regression models were compared. Riverine sul1 correlated with upstream capacities of animal feeding operations (R2 = 0.35, p < 0.001) and wastewater treatment plants (R2 = 0.34, p < 0.001). Weighting for the inverse distances from animal feeding operations along transport pathways strengthened the observed correlations (R2 = 0.60−0.64, p < 0.001), suggesting the importance of these pathways in ARG dissemination. Correlations were upheld across the four sampling events during the year, and averaging sul1 measurements in bed and suspended sediments over all events yielded the strongest correlation (R2 = 0.92, p < 0.001). Conversely, a significant relationship with landscape features was not evident for tet(W), which, in contrast to sul1, is broadly distributed in the pristine region and also relatively more prevalent in animal feeding operation lagoons. The findings highlight the need to focus attention on quantifying the contribution of water pathways to the antibiotic resistance disease burden in humans and offer insight into potential strategies to control the spread of ARGs.

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compartments, including soil,8−10 groundwater,11 surface water,12,13 and sediment.12,13 In particular, animal feeding operation lagoons and wastewater treatment plants receive both excreted antibiotics and resistant gastrointestinal flora,14 while possessing their own distinct microbial ecologies that drive gene exchange,15−17 and thus are prime candidates of interest as nodes of dissemination to the greater environment.4,18−21 Although broad patterns of human activity related to amplified levels of antibiotic resistant bacteria and ARGs in the environment have been noted;7−9,11−13 precise sources, activities, and abiotic and biotic phenomena driving dissemination have not previously been quantified. Others have observed clear influences of human activities on ARG levels in rivers,6,12 but the landscape complexity has precluded identification of quantitative relationships. The South Platte River Basin, including the South Platte (SP) and Cache la Poudre (Poudre) (PR) rivers, represents an ideal system for gaining insight into geospatial factors

INTRODUCTION Antibiotic resistance represents a serious human health challenge and threatens the present and future effectiveness of antibiotics to treat life-threatening infections. Antibiotic resistance has been identified as a key factor in emerging infectious disease.1 The emergence of multiantibiotic resistant “superbugs”, such as those carrying the New Delhi metallobetalactamase-1 blaNDM−1 gene,2 is cause for particular concern. Antibiotic resistance is encoded by antibiotic resistance genes (ARGs), which, as has been well-illustrated in the case of blaNDM−1, can be readily shared even among unrelated bacteria.3 Thus, ideally, strategies for the containment of antibiotic resistance may benefit from a focus directly on the ARGs that confer resistance. Surface water pathways may represent a key route of dissemination of ARGs.4−6 However, the lack of a defined quantitative relationship between ARGs and human-influenced watershed features has shed doubt on the importance of such pathways. Recently, human population density1 and construction of roads to remote villages7 have been demonstrated to contribute to the emergence and spread of antibiotic resistant bacteria. It is also known that human activities alter the distribution and magnitudes of ARGs in various environmental © 2012 American Chemical Society

Received: Revised: Accepted: Published: 11541

July 2, 2012 October 4, 2012 October 4, 2012 October 4, 2012 dx.doi.org/10.1021/es302657r | Environ. Sci. Technol. 2012, 46, 11541−11549

Environmental Science & Technology

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Figure 1. Map of the South Platte River Basin, corresponding land cover, and distributions of animal feeding operations (AFOs), wastewater treatment plants (WWTPs), and fish hatchery and rearing units (FHRUs). (A) Poudre River (PR) and South Platte River (SP) sampling sites indicated. (B) Distribution and capacities of AFOs, WWTPs, and FHRUs.

contributing to the dissemination of antibiotic resistance (Figure 1). In addition to being well-zonated in terms of adjacent urban and agricultural land-use and possessing few tributaries, the PR is characterized by a pristine origin in the Rocky Mountains devoid of major human activities. 22 Metagenomic investigations of other pristine environments, such as Arctic soil,23,24 have brought attention to an ancient array of resistance elements comprising the background antibiotic resistome25 that must be taken into account when assessing human influences.5 Thus, in this study, sampling in the pristine region of the PR provided contrast for identifying anthropogenic sources of ARGs. The aim of this study was to evaluate potential correlations between riverine ARG magnitudes and upstream capacities of animal feeding operations, wastewater treatment plants, and fish hatchery and rearing units while taking into account their spatial distances from river monitoring points. Suspended and bed sediment were sampled spanning river sites classified as pristine (PR0a, PR0b, and PR1), increasingly impacted (PR2, PR3a, PR3b, and PR4), and highly impacted (PR5, SP2, and SP3). Notably, SP receives up to 90% of its flow from metropolitan Denver wastewater treatment plant discharge. To characterize the relationship between geospatial features and ARGs, an extensive geospatial database was developed including locations and capacities of eighty-nine wastewater

treatment plants, one hundred animal feeding operations (fifty beef, forty-seven dairy, and three sheep), and three trout fish hatchery and rearing units (Figure 1). The database enabled precise delineation of runoff and stream discharge pathways upgradient of the river sampling sites. Quantitative polymerase chain reaction (Q-PCR) was used to directly measure the target ARGs, including extracellular forms and those harbored by unculturable bacteria, a characteristic feature of the vast majority of environmental microbes.26 The analysis included four time points spanning a range of hydrologic and climatic conditions (Supporting Information Figure S1). sul1 was chosen as a tracer of anthropogenic sources based on recent examination of the watershed, where sul1 was detected by PCR in 100% of wastewater treatment plant (N = 11) and animal feeding operation lagoon (N = 47) samples, but only in 4% of pristine samples (N = 24).27 To explore the extent to which a similar correlation may exist for a more broad geographically distributed ARG, the tet(W) tetracycline ARG was quantified. tet(W) had previously been observed to most strongly correspond with pristine sites, relative to eleven other tet and sul ARGs investigated.27 It was hypothesized that a significant portion of the variation in sul1 in river samples could be explained by upstream capacities of animal feeding operations and wastewater treatment plants, while tet(W) would not alter with these geospatial factors. Overall, the experimental 11542

dx.doi.org/10.1021/es302657r | Environ. Sci. Technol. 2012, 46, 11541−11549


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Pollutant Discharge Elimination System permits issued. Because Colorado law does not require animal feeding operations to obtain permits, beef feedlots and dairies were confirmed within the watershed via satellite imagery and manually digitized as polygons using the Environmental Risk Assessment Management System (eRAMS) tool (Department of Civil Engineering, Colorado State University) available online: http://erams9.engr.colostate.edu. Polygon area was calculated using ArcGIS, version 9.2 (ESRI, Inc., Redlands, CA). Surface water distances along overland flow paths, irrigation ditches, and streams from upstream animal feeding operation, wastewater treatment plant, and fish hatchery and rearing unit sources were also determined using terrain analysis. Geospatial drivers considered were (a) “capacity of upstream animal feeding operations” in number of animals, (b) “capacity of upstream wastewater treatment plants” in million gallons per day of effluent discharge (a proxy for human population density), (c) “capacity of upstream fish hatchery and rearing units” in number of fish, (d) “overland flow distance from upstream sources to the river sampling site” in kilometers, and (e) “channelized flow distance from upstream sources to the river sampling site” in kilometers. Regression Analysis. The quantities of sul1 and tet(W) genes (normalized to 16S rRNA genes) were modeled as a function of capacities of upstream wastewater treatment plants, animal feeding operations, and fish hatchery and rearing units as the explanatory variables using a GLR model with logtransformed response variables [i.e., sul1 or tet(W)]. An exhaustive set or GLR models comprising various combinations of the capacities of animal feeding operations, wastewater treatment plants, and fish hatchery and rearing units, with and without inverse distance weighting, was evaluated. Akaike information criteria were used to evaluate the trade-off between bias and variance, determine the relative goodness of fit, and select the most appropriate model. The lack of fit F-test was used to test the overall significance of the regression models and whether the GLR functions were appropriate response surfaces. Models with P-values less than 0.05 were judged significant. Coefficient of multiple determination (R2) and adjusted coefficient of multiple determination (Adj R2) were computed to judge and compare the strength of the GLR models. The Shapiro−Wilk test was used to examine the normality of the error terms. The constancy of the error variance (i.e., homoscedasticity) was assessed using the Brown−Forsythe test statistic. The randomness of the errors was tested using the Durbin−Watson test. The variance inflation factor (VIF) was used to identify multicolinearity in the matrix of predictor variables for each GLR model. Models with the largest VIF value among all predictor variable in excess of 10 or mean VIF values considerable larger than 1 were considered inappropriate for explaining the relationship between ARGs and geospatial factors. The correlation between ARGs and geospatial variables was investigated using a GLR model, with the log transformed quantities of sul1 and tet(W) normalized to 16S rRNA genes as response variables and the inverse distance weighted capacities of upstream wastewater treatment plants, animal feeding operations, and fish hatchery and rearing units, as the explanatory variables.

approach provided a means to quantitatively assess the relationship between ARGs and human-influenced watershed features and to compare the behavior of two ecologically distinct ARGs.

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METHODS Site Selection and Sample Collection. River sampling locations were selected prior to the data collection campaign to encompass a gradient of anthropogenic influences from pristine sites devoid of major human activities, impacted by small animal feeding operations, impacted by small wastewater treatment plants, and heavily impacted by both animal feeding operations and wastewater treatment plants. Bed and suspended sediment grab samples were collected from ten river sites within the South Platte River Basin (Figure 1) on October 20, 2006; February 23, 2007; May 22, 2007; and October 23, 2007. Sampling dates encompassed hydrologic conditions ranging from high flow to low flow conditions (Supporting Information Figure S1). Activated sludge samples from three conventional wastewater treatment plants and sediment from one fish hatchery and rearing unit within the watershed were also sampled. Measurement of ARGs. DNA was extracted from sediments, suspended sediments concentrated onto 0.22 μm filters, or sludge samples using an UltraClean Soil DNA Kit (MoBio Laboratories, Inc.) according to manufacturer instructions. 16S rRNA genes, sul1 and tet(W) were quantified in triplicate Q-PCR reactions on a 7300 Real-Time PCR System (Applied Biosystems, Foster City, CA) including negative controls and seven point standard curves in each run, as described previously.28 Several control measures were taken to ensure the quality of the Q-PCR data. Dilution series were carried out on random subsets of each sample type (10% of samples) to identify the optimal dilution for minimizing threshold cycle suppression by inhibitors for that sample type (typically 1:10 to 1:50). To verify positive detections, melt curve analysis was included in every run and a subset of Q-PCR products (10% selected at random, plus any samples with anomalous melt curves) were further analyzed by gel electrophoresis to verify expected size. Measurements were excluded from further analysis if they did not pass these screening tests. To validate the quantitative value of the data, the template amplification efficiencies of positive samples were compared to those of standards using the LinReg approach 29 and were not found to be significantly different. Variance among triplicate estimates in gene quantities greater than 20%, or failure of one or more of the triplicates to amplify, triggered reanalysis of that sample. Two or more positive detections were scored as positives and the mean value of all positive replicates were employed in subsequent analyses, ARGs were normalized to 16S rRNA genes throughout this study. Geospatial Analysis. For each river site, the boundary of the corresponding drainage area was delineated using terrain analysis with the ArcHydro toolbox in ArcGIS version 9.3 (ESRI Inc., Redlands, CA), which entailed processing of a 30-m digital elevation model (DEM) from national elevation data set (NED) of the U.S. Geological Survey (USGS). The locations and capacities of animal feeding operations, wastewater treatment plants and fish hatchery and rearing units within each drainage area were identified from the Envirofacts Warehouse tool available from the EPA’s Web site (http:// www.epa.gov/enviro/), queried to obtain a list of National

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RESULTS Relationship Between sul1 and Landscape Features. A striking trend of sul1 amplification was observed from pristine regions to downstream areas highly impacted by human 11543



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Figure 2. ARGs in the South Platte River (SP) and the Poudre River (PR). (A) Sites classified according to relative level of upstream human impact, with ARGs in bed and suspended sediments averaged over the four sampling dates and plotted according to upstream distance from the most heavily impacted SP3. (B) Boxplot of ARG measurements within each classification group, including both suspended and bed sediment measurements. Observed mean of sul1/16S rRNA genes were significantly different among the classes (ANOVA, F = 6.3, P = 0.0044, df = 39).

Table 1. Key General Linear Regression Models Illustrating ARG Transport in the Watersheda

a

AFO = Animal feeding operation. WWTP = Wastewater treatment plant. See Supporting Information Table S1 for full list of GLR models investigated.

Figure 3. General linear regression (GLR) model with log transformed sul1/16S rRNA gene magnitude response variable with (A) upstream inverse distance-weighted (IDW) animal feeding operation (AFO) animal counts predictor variable (F = 64.4, P < 0.001, df = 38, R2 = 0.63); (B) upstream AFO animal counts predictor variable (F = 20.1, P < 0.001, df = 38, R2 = 0.35); and (C) upstream IDW wastewater treatment plant (WWTP) capacity predictor variable (F = 19.2, P < 0.001, df = 38, R2 = 0.34). Each data point represents the average of bed and suspended sediment ARG magnitude at a sampling site (Figure 1). Each data point represents the average of triplicate Q-PCR measurements of a single DNA extract. The solid lines represent the GLR model and the dashed lines represent 95% confidence intervals.

summarized in Table 1. It was noted that sul1 displayed a strong and significant relationship with upstream animal feeding operation capacities weighted for inverse distances along surface water pathways, including overland flow, ditches, and stream segments [GLR model #3 F = 64.4, P < 0.001, df. =

activities (Figure 2). To explore correlations between landscape features and ARGs, forty general linear regression (GLR) models were evaluated, which are described in Supporting Information Table S1. Key GLR models that best illustrated the role of geospatial factors in the dissemination of ARGs are 11544



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Figure 4. Measured and simulated ordinary least-squares data for the full general linear regression (GLR) models. GLR model (log(y) = aX + ε) reported in Table 1, model 9. ARG measurements averaged between suspended and bed sediment samples over four events served as response variables and inverse distance-weighted (IDW) upstream capacities of animal feeding operations (AFOs) and wastewater treatment plants (WWTPs) as explanatory variables. (A) sul1/16S rRNA genes. (B) tet(W) /16S rRNA genes.

38, R2 = 0.63] (Figure 3A). Animal feeding operation capacity upstream of each sampling location by itself was a predictor of sul1, but not as strong (GLR model #1 F = 20.1, P < 0.001, df = 38, R2 = 0.35) (Figure 3B). The analysis also indicated a rise in sul1 with increasing upstream wastewater treatment plant capacity (GLR model #5 F = 19.2, P < 0.001, df = 38, R2 = 0.34) (Figure 3C), but weighting for inverse distances did not improve model performance (GLR model #6) (Supporting Information Table S2). Capacities of trout fish hatchery and rearing units did not alter sul1. Effect of Normalizing Across Seasons and Hydrologic Regimes. To normalize the effect of variation in season and hydrologic regime and emphasize the influence of geospatial factors, ARG measurements were averaged between the bed and suspended sediment and across the four sampling events. This analysis yielded a very strong correlation between riverine sul1 quantities and inverse distance weighted capacities of upstream animal feeding operations and wastewater treatment plants (GLR model #9 F = 40.2, P < 0.001, df = 7, R2 = 0.92) (Table 1, Figure 4A, Supporting Information Table S3). This indicates that geospatial features control the variability of sul1 in the watershed under varying climatic and hydrologic conditions. Sampling Dates, Matrices, and Correlation with Antibiotics. sul1 correlations with upstream capacities of animal feeding operations and wastewater treatment plants were examined over each individual sampling date in both sediment and water matrices. The correlations were consistently upheld, except February 2007 bed sediment and October 2007 suspended sediment (Supporting Information Tables S4 and S5). Furthermore, a consistent increasing trend in the magnitude of sul1 was observed for three of the four sampling events from upstream to downstream sites (Supporting Information Figure S2). Although the same overall trend was maintained in February 2007, sul1 bed sediment measurements were nearly 2 orders of magnitude greater than the other events. Correlations of sul1 with antibiotics previously measured at the river sites 30 was of interest as an indicator of cotransport and/or selection of ARGs. A strong correlation was identified between sul1 and the total of six sulfonamides

measured previously (R2=0.65, 0.94 for water and sediments, respectively) (Supporting Information Figure S3). Relationship Between tet(W) and Landscape Features and Antibiotics. In contrast to sul1, tet(W) did not exhibit an increasing trend with downstream distance (Figure 2C), and did not correlate with upstream capacities of animal feeding operations or wastewater treatment plants (Supporting Information Table S6), even when normalized across season and hydrologic regime (Figure 4B; Supporting Information Table S7). However, significant correlations were noted in two individual samplings: February 2007 in the bed sediment (Supporting Information Table S8) and October 2007 in the suspended sediments (Supporting Information Table S9). Thus, tet(W) may at times also be subject to significant transport in the watershed. Tet(W) exhibited no correlation (water) or a negative correlation (bed sediment) with the total of six tetracycline antibiotics measured previously 30(Supporting Information Figure S3). Conceptual Mass Balance Model and Ratio of tet(W):sul1. The relative quantities of sul1 and tet(W) were compared in a subset of animal feeding operations and wastewater treatment plants in the watershed to aid in the construction of a conceptual ARG mass balance model (Figure 5, see Supporting Information Table S10). Previously published magnitudes of sul1 and tet(W) were examined in four dairy and two beef cattle lagoons within the South Platte River Basin 31 and additional measurements of these ARGs were made at three wastewater treatment plants. It was noted that tet(W) was higher than sul1 on average in upstream animal feeding operation lagoon and pristine environments, while sul1 was higher in wastewater treatment plants and the fish hatchery and rearing units, suggesting that the tet(W):sul1 ratio may be indicative of the relative contributions of these ARG sources. Plotting these ratios spatially with respect to the SP and PR monitoring points revealed a pattern of predominant animal feeding operation influence consistent with the GLR models (Figure 5). Remarkably, the ratio shifts in favor of sul1 precisely when wastewater treatment plant influence becomes prominent at PR3b, before shifting back to a ratio dominated by tet(W) at the animal feeding operation-dominated SP2 and SP3. 11545



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Figure 5. Conceptual mass balance model of ARGs reaching impacted South Platte River Basin sites. Fate and transport processes that may contribute to amplification, attenuation, or persistence of ARGs upstream from sampling sites are indicated. Primary dissemination mechanisms consistent with selection of ARGs by ambient antibiotics or other factors versus direct ARG transport are also noted. The average tet(W) and sul1 magnitudes and their ratios in the animal feeding operation (AFO) cattle lagoons were determined from a concurrent study 31 as described in Supporting Information Table S10. Wastewater treatment plant (WWTP) magnitudes were determined in this study. Fish hatchery and rearing units are not shown as they did not significantly contribute to the models.

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overland flow paths, irrigation ditches, and stream segments suggests that these are important pathways for the dissemination of ARGs. Furthermore, averaging ARG magnitudes across the four sampling events and between the water and bed sediment matrices resulted in a very strong correlation (R2 = 0.92). Considering that flows, and thus advection of ARGs, will vary significantly with season, the strong correlation achieved when averaged over the seasons highlights the dominant influence of geospatial factors relative to variation in hydrologic regime. It is proposed that a complex interplay of processes govern fate and transport of ARGs in the watershed. Figure 5 illustrates a framework for conceptualizing ARG mass balance in the watershed and the influence of abiotic (e.g., advection, adsorption, dilution, photolysis) and biotic (e.g., selection,

DISCUSSION Antibiotic resistance represents a major human health challenge, and despite diligent efforts in improving antibiotic use practices and hygiene in the clinical realm, rates of antibiotic resistance continue to increase.32 Infections originating outside the clinical realm are also increasing,33 which highlights the need for innovative strategies for minimizing the spread of resistance. Overall, the results bring to light the water environment as an important front in the battle against antibiotic resistance. The results of this study unambiguously demonstrate a quantitative relationship between upstream capacities of wastewater treatment plants and animal feeding operations and sul1 in riverine environments. The fact that correlations were strengthened when upstream capacities were weighted for inverse distance between these features along 11546



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significance of all models examined in this study were strengthened when applied to the normalized ARG data, relative to the absolute measurements (data not shown). This suggests that the relationship between landscape and ARGs truly was best reflected in the gene ratios. Gene ratios serve as a proxy for the proportion of bacteria carrying ARGs, although it is an imperfect indicator because bacteria range in the numbers of 16S rRNA gene copes and ARG copies that they carry. Important to note is that 16S rRNA genes did vary among the DNA extracts, tending to be lower in concentration in the pristine samples and increasing downstream by factors ranging from about 2× to 10×. Because normalizing ARGs by the smaller number in pristine samples acted to boost the normalized ARG calculation, normalizing to 16S rRNA genes was a conservative approach to tracking relative influence of human land-use on ARGs. Normalizing to 16S rRNA genes also likely aided in correcting for minor variations in DNA extraction efficiency, assuming that the efficiencies of extraction of ARGs and 16S genes were comparable. The present study suggests that animal feeding operations are a more dominant source of sul1 to the PR and SP, with modest contribution from wastewater treatment plants. This river system was recently examined using a qualitative molecular signatures approach, which indicated that ARG molecular signatures were more similar to those of wastewater treatment plants at all impacted sites, except SP3, which aligned with animal feeding operations.38 The ARG molecular signature applied in the previous study was based solely on tet ARGs [combined tet(W) phylogenetics and frequency of detection of tet(C), tet(E), and tet(O) (wastewater treatment plant) versus tet(H), tet(Q), tet(S) and tet(T) (animal feeding operation)] and did not consider sul ARGs. This suggests that sourcetracking approaches employing tet ARGs may be biased toward wastewater treatment plants relative to animal feeding operations. Such a bias would be particularly interesting, given that tet(W) was observed to be dominant relative to sul1 in the cattle lagoons in this study. It is suggested that tet(W) may be more sensitive than sul1 to attenuating processes, which are of greater influence when emanating along flow-paths from nonpoint source animal feeding operations. Nonetheless, tet ARGs persist in riverine environments and remain traceable to their original sources.27 Overall it is suggested that standardized approaches for tracking anthropogenic sources of ARGs will benefit from consideration of an array of ARGs representing various classes, horizontal gene transfer capabilities, and relative distribution in the background. The need for such a method is receiving increasing attention4,5,20 and could potentially be addressed with metagenomic approaches. This study is the first to reveal a quantitative relationship between animal feeding operation and wastewater treatment plant sources and riverine ARG magnitudes. The findings highlight the importance of proximity and hydrologic transport pathways between animal feeding operations and streams as drivers of ARG dissemination. It is expected that dominant processes governing ARG transport vary across watersheds, particularly with respect to regional climactic conditions. Also, the extent of antibiotic use, relative hospital influence, etc., will vary widely with respect to regional and local factors. Therefore, the relative influence of animal feeding operations and wastewater treatment plants observed in this study cannot be directly extrapolated to other watersheds. Likewise, it is important to point out that the fish hatchery and rearing units present in this watershed, which did not have measurable

amplification, persistence, and attenuation) processes and the expected characteristics. Consistent with this model, weighting upstream capacities of wastewater treatment plants with inverse distance did not improve correlations with sul1, likely because attenuating processes are less of an influence for point-sources (e.g., wastewater treatment plants) than along flow paths of nonpoint sources (e.g., animal feeding operations). The distinct distribution of sul1 versus tet(W) in the South Platte River Basin provides new insight into the underlying phenomena driving amplified ARGs in human-impacted environments. If the processes driving the correlation between ARGs and landscape were solely abiotic, then tet(W) and sul1 should have yielded similar overall trends, which was not the case. This highlights inherent differences in the biotic responses of these two ARGs to the environment. An obvious distinction between tet(W) and sul1 is that sul1 is carried within the 3′ conserved region of class 1 integrons, which adeptly transfer among a broad range of host bacteria, including both Gram positives and Gram negatives.34 Such an association may facilitate the establishment of sul1 within native environmental bacterial populations and enhance its persistence. The association of sul1 with class 1 integrons also subjects it to greater coselective action by other antibiotics or pollutants, such as heavy metals, which may act directly on genes present within cassette regions. Collectively, these factors could contribute to sul1 maintaining the strong correlations with landscape features observed in this study. Although tet(W) has been estimated to be the third most widely distributed tet ARG and also tends to be associated with promiscuous genetic elements, particularly conjugative transposons,35 the results suggest that its promiscuity may pale in comparison with that of sul1. Indeed, class 1 integrons are recognized as the most widespread among clinical isolates and most known ARG cassettes belong to this class.34 While the present study focused on two model ARGs, future studies would benefit from application of metagenomic approaches to more broadly capture the array of ARG behaviors in the watershed. It was observed that the February sediment sul1 magnitudes were about an order of magnitude higher than the other events. Interestingly, previous researchers noted that the highest concentrations of all six sulfonamides and six tetracyclines monitored in the Poudre river also occurred in February30 (Supporting Information Table S11). February is typically characterized by low flow at the Poudre Canyon mouth, while downstream flows are augmented by irrigation return flows (Supporting Information Figure S1). This may contribute to antibiotic and ARG transport, while incurring less antibiotic dilution from snowmelt,30 enhancing likelihood of selection of resistant strains. The strong correlation between sul1 and sulfonamides measured previously (Supporting Information Figure S3) suggests that selective pressures may have been at play (Figure 5), which is possible even at subinhibitory concentrations.4,36,37 Intriguingly, tet(W) and tetracyclines actually exhibited a negative correlation, clearly indicating that these two entities have distinct, and surprisingly even antagonistic, transport behavior in the environment. This result demonstrates that antibiotic and ARG transport are not always directly linked and illustrates the utility of devoting specific attention to the transport of ARGs as the “contaminants” of interest. In this study, the ARG data presented were normalized to the corresponding number of 16S rRNA gene copies, which are housekeeping genes present in all bacteria. Interestingly, the 11547



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influence on riverine ARGs, were intended for the rearing of trout stocks for game. Thus, they were not representative of intensive aquaculture ponds that have attracted attention as sources of ARGs in other studies.39 Antibiotics also likely play an even stronger selective role in extremely antibiotic-polluted environments, such as rivers in India where exceptional levels of antibiotics, ARGs, and gene transfer elements have been documented.40 Similar to sul1, blaNDM‑1 is also associated with a class 1 integron, has been observed to be highly mobile,2,34 and also has recently been found in human-impacted surface waters.41 This suggests that sul1 may serve as an appropriate surrogate foreshadowing the transport of resistance elements of imminent human concern and provide an important baseline understanding of ARG transport in watersheds. Furthermore, sulfonamide and tetracycline antibiotics themselves, continue to serve as a front-line of defense against methicillin-resistant Staphylococcus aureus infections.42 Advanced understanding of the human antibiotic resistome (e.g., ref 14) will also further advance knowledge of human sources and receptors. The findings described in this study suggest that appropriate actions are warranted at wastewater treatment plants and animal feeding operations to curtail the dissemination of ARGs to the water environment. Potential options include vigilant management of runoff from animal feeding operations and more aggressive disinfection measures that remove or destroy ARGs at wastewater treatment plants.43

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Figures summarizing river flow conditions (Figure S1), individual sampling dates (Figure S2), and correlations with antibiotics (Figure S3), and tables reporting GLR models (Tables S1) and the sul1 results for all bed and sediment measurements individually (Table S2), both averaged over all sample dates (Table S3), bed sediment (Table S4), and suspended sediment (Table S5), as well as summaries of the same information for tet(W) (Tables S6−S9), tet(W)/sul1 ratios (Table S10), and previously reported antibiotic data (Table S11). This material is available free of charge via the Internet at http://pubs.acs.org.

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Article

AUTHOR INFORMATION

Corresponding Author

*Address: Environmental and Water Resources Program, Via Department of Civil and Environmental Engineering, 418 Durham Hall, Blacksburg, VA 24061. Phone: (540) 231-3980. Fax: (540) 231-7916. E-mail: [email protected]. Author Contributions §

These authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Funding for this research was provided by the Colorado Water Resources Research Institute, the USDA Agricultural Experimental Station at Colorado State University, the Virginia Tech Institute for Critical Technology and Applied Science Award TSTS 11-26, and the National Science Foundation CBET CAREER award # 0547342. The findings do not represent the views of the funding sponsors. 11548



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