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Published March 1, 2016 Journal of Environmental Quality SPECIAL SECTION ANTIBIOTICS IN AGROECOSYSTEMS: STATE OF THE SCIENCE Culture-based Methods for Detection of Antibiotic Resistance in Agroecosystems: Advantages, Challenges, and Gaps in Knowledge Jean E. McLain,* Eddie Cytryn, Lisa M. Durso, and Suzanne Young M ultiple culture-based methods are used to assess Abstract antibiotic resistance in environmental samples. Culture-based methods commonly involve isolating target bacteria on general or selective media followed by assessing growth in response to specific concentrations of antibiotics. Target bacteria are commonly pathogens of clinical or public health concern (e.g., Campylobacter, Salmonella) or easily detected bacterial indicators that signify the presence of fecal contamination (e.g., Escherichia coli, total coliforms) (Davies and Davies, 2010). In addition, target bacteria often include those that have the propensity to acquire resistance genes horizontally and to develop resistance to a broad spectrum of last-resort antibiotics (e.g., methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus) (Poole, 2002). Although highthroughput methods are increasing processing capabilities, data production using culture-based methods can be time consuming. Nevertheless, the value of these data cannot be underestimated. Isolation of bacteria is key to understanding and studying phenotypic and genotypic characteristics of individual isolates. Although identification of resistance genes in bulk DNA samples is possible using modern molecular methods, culture and isolation of individual target pathogens carrying the gene of interest is essential for determining antibiotic resistance phenotypes and is therefore integral to national and international antibiotic resistance surveillance and tracking efforts. As such, culture-based studies provide an important link between antibiotic resistance measured in the environment and antibiotic resistance detected in human clinical settings. Various culture-based methodologies are used in assessment of antibiotic resistance in samples collected in agroecosystems. Culture-based methods commonly involve isolating target bacteria on general or selective media and assessing growth in response to specific concentrations of antibiotics. The advantages of culture-based methods are multifold. In particular, isolation of bacteria is key to understanding phenotypic characteristics of isolates and their resistance patterns, and most national and international antibiotic resistance monitoring projects are isolate based. This review covers current knowledge of bacterial groups and antibiotics commonly targeted in resistance studies using bacterial culture and discusses the range in methods used, data interpretation, and factors supporting and confounding the use of culture-based methods in assessment of antibiotic resistance. Gaps in knowledge related to study design and resistance databases are discussed. Finally, a case is made for the integration of culture-based and molecular methods to better inform our understanding of antibiotic resistance in agroecosystems. Core Ideas • Culture-based methods provide reproducible results with minimal error. • Culture-based methods enable isolation of specific target organisms. • These methods often involve screening at a range of antibiotic concentrations. • Culture-based methods provide key data on multiple antibiotic resistance. • Both molecular and cultivation methods are needed for full insight into resistomes. Microbial Classifications for Antibiotic Resistance Classification of a clinical isolate as “susceptible,” “resistant,” or “intermediate” to an antibiotic relies on growth of the bacterium at defined in in vitro antibiotic concentrations, referred to as “breakpoints” (Silley, 2012; Simjee et al., 2008). Clinical breakpoints incorporate data on the dose of a drug needed to obtain a specific concentration at the infection site of the patient and J.E. McLain, Water Resources Research Center, Univ. of Arizona, 350 N. Campbell Ave., Tuscon AZ 85719; E. Cytryn, Institute of Soil, Water and Environmental Sciences, Volcani Center, Agricultural Research Organization, P.O. Box 6, Bet Dagan 50-250, Israel; L.M. Durso, USDA–ARS, Agroecosystem Management Research Unit, Room 121 Keim Hall, Univ. of Nebraska Lincoln, Lincoln, NE 68583; S. Young, Univ. of South Florida, Dep. of Integrative Biology, 4202 E. Fowlers Ave SCA110, Tampa, FL 33604. Assigned to Associate Editor John Brooks. Copyright © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. 5585 Guilford Rd., Madison, WI 53711 USA. All rights reserved. J. Environ. Qual. 45:432–440 (2016) doi:10.2134/jeq2015.06.0317 Freely available online through the author-supported open-access option. Received 27 Jun 2015. Accepted 9 Dec 2015. *Corresponding author ([email protected]). Abbreviations: ECOFF, epidemiological cut-off; MAR, multiple antibiotic-resistant; MIC, minimal inhibitory concentration; VBNC, viable but nonculturable. 432 empirically gather information measuring treatment success of the antibiotic for in vivo eradication of a specific bacterial infection (Simjee et al., 2008). Clinical breakpoints are set using minimal inhibitory concentration (MIC) measurements, defined as the lowest concentration of an antimicrobial that will inhibit the visible growth of a microorganism after overnight incubation (Andrews, 2001). These MIC values are incorporated into pharmacological and treatment data to determine official clinical breakpoints (Turnidge and Paterson, 2007). Thus, an isolate classified as “susceptible” would be inhibited by the MIC at the site of infection, whereas a classification of “resistant” implies that an isolate would not be inhibited by this achievable concentration (CLSI, 2015). The “intermediate” category includes isolates that display a relative resistance by exhibiting growth at the MIC of an antimicrobial but are in fact susceptible to a concentration above the MIC (CLSI, 2015). Clinical breakpoint methodologies (e.g., drug delivery systems) and MIC interpretative criteria can change over time, and, for this reason, published clinical breakpoints are updated on a regular basis. Although criteria for many bacteria and drug combinations have remained the same for decades (Drummond et al., 2003), numerous studies suggest that selective pressure on clinically relevant bacteria arising from antibiotic use by humans is increasing breakpoint concentrations (Steinkraus et al., 2007; Turnidge and Paterson, 2007). Furthermore, clinical breakpoints may differ between the United States and Europe, with standards in the United States published by the Clinical and Laboratory Standards Institute (CLSI, 2015) and European criteria published by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, 2015). These breakpoints and their interpretive criteria are updated regularly by CLSI and EUCAST based on data generated by working groups to ensure that new methodology is incorporated into published databases and that any changes in clinical breakpoints are addressed. Researchers must therefore use the most up-to-date standards in interpreting data arising from culture-based studies (Schwarz et al., 2010) and report which database was used for interpretation in peer-reviewed publications. Martinez et al. (2015) recently proposed three main classifications of antibiotic resistance: clinical, epidemiological, and operational. Operational resistance examines the concept of resistance from the perspective of the potential for microbial transfer of resistance genes and is not covered in this review. Clinical resistance refers to that specifically associated with defined pathogenic species and is most commonly measured using the CLSI and EUCAST standard methods. The main use of clinical resistance measurements is to guide decisions on patient treatment. Clinical resistance methods measure only the expression of antibiotic resistance genes under strictly controlled parameters, not whether or not the bacteria that carry these genes are capable of causing disease in humans, although clinical breakpoints may also inherently provide information on virulence and virulence potential because they are based not only on MICs but also on pharmacological data and treatment outcomes. Although clinical resistance is the endpoint of interest for treatment and for many risk assessments, these measurements are considered to be inadequate when assessing resistance outside of clinical settings (Martinez et al., 2015; Berendonk et al., 2015). Clinical breakpoint interpretive criteria, when applied to environmental isolates, strongly imply that the isolate can and does have the potential to cause disease in humans, even though this potential is not actually measured as part of the procedure. To address these concerns, the concept of “microbiological resistance” was introduced in the 1990s and is the basis for current interpretations of epidemiological resistance. The main use of epidemiological resistance measurements is to monitor the development of bacteria with reduced susceptibility to a target drug (Simjee et al., 2008) and is tracked using “epidemiological cut-off ” (ECOFF) values (EUCAST, 2015). Epidemiological cut-off values are defined exclusively on the basis of the normal distribution of MICs in a given bacterial species. All isolates that have MICs inside this distribution are considered as wild-type (and, using the terminology of clinical resistance, are considered susceptible), and those presenting MICs above this value are considered non–wild-type, or as displaying reduced susceptibility (Levy, 2001; Morrissey et al., 2014). Due to the complexity of resistance definitions and their terminology, it has been suggested that the term “resistance” be reserved for clinical resistance breakpoints only (Bywater et al., 2006; Silley, 2012). Epidemiological cut-off values ae defined by measuring the range of MIC values for a large number of isolates (Kronvall, 2010) and can therefore be used for any antibiotic, including those that do not have defined clinical breakpoints (Kahlmeter, 2014). However, ECOFF values have been determined primarily using strains isolated from sick humans and animals, and henceforth the distributions are biased toward clinical, and not environmental, isolate MIC values (Walsh and Duffy, 2013). The foundation for determining ECOFF values is a collection of genetically diverse isolates representing the entire population of the species to be measured (Kahlmeter et al., 2003), including those from different niches (human, animal, or environment) that can harbor unique subpopulations of bacteria due to individual selection pressures (Cook et al., 2011; Walk et al., 2007, 2009). Thus, it is essential that environmental isolates be included when assessing ECOFF values (Martinez et al., 2015; Berendonk et al., 2015). At this time, databases produced using clinical bacteria are the best available and continue to be the most relevant related to human health risk. However, comprehensive ECOFF databases produced using nonpathogenic commensal bacteria from human and animal hosts as well as bacteria isolates from soil, water, and biosolids under a range of growth conditions represent a critical need in accurate assessment of antibiotic resistance in agroecosystems. Further, such information will be critical to prediction of the transfer of resistance from the environment into the clinical realm or into the human food chain. Bacterial Groups and Antibiotics Commonly Targeted in Culture-based Resistance Studies The clinical relevance of a microbial species is the most common predictor for their selection as targets in agroecosystem studies. Common microbial groups for study include E. coli, Enterococcus, Salmonella, and Staphylococcus, although Berendonk et al. (2015) suggest including less commonly targeted microbial groups Aeromonas, Klebsiella pneumoniae, and Pseudomonas aeruginosa. In addition to their clinical relevance and ease in Journal of Environmental Quality433 culturing, these bacteria are used as indicators of water quality (specifically E. coli, Enterococcus, and Aeromonas). Escherichia coli has been proposed as a representative Gram-negative indicator for antibiotic resistance surveillance (Reinthaler et al., 2003; Collignon, 2009), and K. pneumoniae has been proposed as a model microbial organism due to its presence in both the environment and in animal guts and its proposed evolutionary role in the development and spread of resistance (Tzouvelekis et al., 2012; Berendonk et al., 2015). Enterococcus and Staphylococcus are widely distributed in the environment (Hayes et al., 2004; Schulz et al., 2012), where they have surfaced as organisms of importance due to the emergence of multiple antibiotic-resistant (MAR) strains that are currently responsible for approximately 20% of all nosocomial infections in the United States (Kayser, 2003; Shorr, 2007; Magill et al., 2014). Furthermore, both Enterococcus and Staphylococcus possess a demonstrated capability for transfer of resistance to neighboring bacteria through plasmids and other mobile genetic elements (Wielders et al., 2001; Palmer et al., 2010). Their ubiquity in human and animal digestive tracts and in the environment, medical importance, frequent MAR, and capacity for horizontal gene transfer make staphylococci and enterococci ideal bacterial groups for investigating the ecology of antibiotic resistance development. Selection of bacterial targets for assessment of antibiotic resistance in agroecosystems, whether indicator organisms or pathogens, should consider the capacity of individual strains to survive in corresponding environments (Martinez et al., 2015; Durso et al., 2012). For example, floroquinolone resistance genes in beef cattle feces can be carried by Clostridia, which are thickwalled, spore-forming bacteria that are able to survive outside of the animal for extended periods of time (Mueller-Spitz et al., 2010). The ability to survive environmental conditions heightens the potential of Clostridia to transmit resistance genes outside of agroecosystems. Likewise, Salmonella have been found to be highly robust in multiple sectors of the environment, in the human gut, and in the clinical realm, enhancing the utility of this bacterial group in the study of the transfer of antibiotic resistance (Roszak and Colwell, 1987). Culture-based studies of resistance in agroecosystems often focus on antibiotics most commonly used in agriculture, including those also prescribed for human clinical use (Table 1). Other considerations include specific mechanisms of action and to what extent they are used for prophylaxis, growth promotion, or treatment of disease in animals. Most commonly studied are b-lactam penicillins, fluoroquinolones, sulfonamides, aminoglycosides, and tetracyclines (Table 1), which together accounted for approximately 45% of antibiotics used in the production of food animals in 2009 (USGAO, 2011). Culture-based Methodologies for Assessment of Antibiotic Resistance Culture of target microorganisms from environmental samples is challenging given that soil and fecal samples generally contain 108 to 1010 microorganisms per gram (Yokoyama and Johnson, 1988; Davis et al., 2005). For this reason, selective enrichment techniques are commonly used. Selective media facilitate the growth of target bacteria while inhibiting the growth of competing organisms. For example, media containing sodium azide and aesculin, in addition to incubation at 42°C, have been used to selectively enrich enterococci (Kemper et al., 2006), and broths containing brain–heart infusion, peptone, egg yolk, and/or sodium chloride (among other growth factors) are used to enrich Staphylococcus (Vos et al., 2011). Selective enrichment methods can also incorporate antibiotics. For example, enrichment media for virulent E. coli may contain novobiocin or vancomycin, both of which have inhibitory effects on competing Gram-positive organisms (Dwivedi et al., 2014). If quantitative results are desired, methods that use the most probable number techniques (Sutton, 2010) can be used in conjunction with enrichment for estimating numbers of target bacteria in the original sample. The relatively recent appearance of multiple commercially available microbiological media targeting specific organisms has simplified the process of identification of target isolates. Since 2002, the USEPA has approved 10 enzyme-based E. coli detection tests for examination of drinking water (Olstadt et al., 2007), most of which are based on the detection of the E. coli–associated enzyme b-D-glucuronidase. Many of the E. coli–selective agars contain a metabolic dye that is cleaved by b-D-glucuronidase, causing target colonies to appear a deep blue. Media that identify a specific microbial group based on enzymatic activities inducing Table 1. Antibiotics commonly used in culture-based antibiotic resistance studies of samples collected in agroecosystems. Antibiotic Amoxicillin, ampicillin Ceftiofur, cephapirin Ciprofloxacin Erythromycin, tylosin Gentamycin, streptomycin, neomycin Lincomycin Sulfadimethoxine, sulfamethazine Tetracycline, chortetracycline, oxytetracycline Antibiotic class Mechanism of action beta-lactam penicillin disruption of peptidoglycan layer of cell wall cephalosporin disruption of peptidoglycan layer of cell wall fluoroquinolone inhibits DNA gyrase macrolide inhibition of protein synthesis aminoglycoside inhibits ribosomal protein synthesis inhibition of protein synthesis lincosamide sulfonamide tetracycline inhibition of bacterial growth and replication inhibits protein synthesis Relevant target pathogens Gram-negative (Escherichia coli, Salmonella) Gram-negative (Pseudomonas aeruginosa, E. coli); Gram-positive (Staphylococcus aureus) Gram-negative (E. coli, Salmonella, Enterobacteraceae, Klebsiella pneumoniae) mainly Gram-positive (Streptococcus, Staphylococcus) Gram-negative (E. coli, Campylobacter, Salmonella) mainly Gram-positive (Streptococcus, Staphylococcus) Gram-negative (E. coli); Gram-positive (Streptococcus) Gram-positive (Enterococcus); Gram-negative (E. coli, Enterobacter) References† 25 12 15 23 15 14 11 21 † Number of publications cited in peer-reviewed literature, 2013 and 2014. 434 Journal of Environmental Quality a growth of a specific color or colonies exhibiting a target fluorescence are termed “chromogenic media” and have been developed for the detection of a wide range of bacteria, including Klebsiella, Pseudomonas, and Salmonella (CHROMagar). Many of the available selective media can have high (50–75%) “false-positive” rates (i.e., isolates that present characteristics of the target species but actually are not that species) when used to identify environmental bacteria (Peplow et al., 1999; Roberts et al., 2010; McLain et al., 2011). Consequently, confirmation of isolate identity is required using complementary methods, which may be culture based or molecular. Culture-based confirmation will use methods specific to detecting an activity specific to that bacterial group (e.g., expression of the urease enzyme, iron reduction, and/or gas production). Molecular methods for confirmatory analyses range from polymerase chain reaction targeting a unique region of the 16S rRNA gene to characterization of multiple genes through multilocus sequence typing. Once target bacteria have been isolated and their identity confirmed, antimicrobial resistance testing can proceed. Commonly used methods are broth dilution, agar disk diffusion, agar breakpoints, and E-tests (Fig. 1). Broth and Agar Dilution Assays Broth dilution methods are used to estimate the MIC of bacterial isolates (Wiegand et al., 2008). This involves exposing a single isolate to sequential 2-fold dilutions of the target drug and, following a proscribed incubation period, measuring the optical density of the broth, with the lowest concentration of each antibiotic that inhibits visible growth of organisms designated as the MIC (Fig. 1a) (Turnidge and Paterson, 2007; Wiegand et al., 2008). The ranges in concentrations tested vary with the antibiotic and with the identity of the isolate being tested but should encompass the concentration used to define the organism as resistant and the ranges of expected MICs for reference quality control organisms ( Jenkins and Schuetz, 2012). This procedure is now commonly performed on microplates and can be performed manually (for detailed protocol see Wiegand et al. [2008]) or via a number of automated and high-throughput platforms (Felmingham and Brown, 2001), including some that are designed for measuring MICs from environmental isolates (Rahman et al., 2004). The commonly used Sensitire (Trek Diagnostic Systems, Thermo Fisher Scientific) and VITEK 2 (bioMérieux) diagnostic systems are based on broth dilution. The CLSI and EUCAST guidelines for broth dilution assays are highly specific in defining growth medium, volume in each well or tube, concentration of bacterial inoculum, and incubation times and temperatures (Silley, 2012). Adherence to these protocols is required because results of broth dilution tests will differ depending on test conditions. For example, formulations of Mueller-Hinton broth, often used for cultivation of Grampositive organisms for Sensititre analysis, vary in cation content and can affect MIC results (Barry et al., 1992; Girardello et al., 2012). Thus, it is imperative that researchers use a MuellerHinton formulation that conforms to antibiotic susceptibility standards. The CLSI and EUCAST guidelines do, however, offer some flexibility in that they offer alternatives for the supplementation or replacement of Mueller-Hinton broth for accurate testing of fastidious organisms ( Jenkins and Schuetz, 2012). The agar dilution approach is similar in theory to broth dilution in that 2-fold serial dilutions of the target antibiotic are used for resistance screening but are added to molten agar before solidification. The target isolate is spread onto the prepared plate, Fig. 1. (a) Setup of a broth dilution experiment displaying two columns (A and B) of a 96-well microplate. Antibiotics A and B are added in Columns A and B at target dilutions (mg mL−1). After antibiotic addition to the plate, a single isolate in growth media (broth) is added to each well. After incubation, isolate growth (grey shading) is measured at an absorbance wavelength (600 nm) using a plate reader, and the minimal inhibitory concentration (MIC) is determined to be the lowest concentration of antibiotic inhibiting the growth of the isolate. In this example, the MIC for isolate to Antibiotic A is 4 mg mL−1, and the MIC for Antibiotic B is 16 mg mL−1. (b) Agar disc diffusion, with agar plate inoculated with target isolate (left plate), followed by discs impregnated with concentrations (mg mL−1) of Antibiotic A (top) and Antibiotic B (bottom) (center plate). After incubation, a zone of inhibition (right plate) indicates susceptibility or resistance to an antibiotic. In this example, the isolate is susceptible to Antibiotic A below 4 mg mL−1 and is susceptible to Antibiotic B below 0.5 mg mL−1. (c) E-test assay, with agar plate inoculated with target isolate (left plate), followed by E-test strips impregnated with a gradient (mg mL−1) of Antibiotic A (top) and Antibiotic B (bottom) (center plate). After incubation, zone of inhibition (right plate) indicates MIC for an antibiotic. In this example, the MIC for Antibiotic A is 16 mg mL−1, and the MIC for Antibiotic B is 0.5 mg mL−1. Journal of Environmental Quality435 and MIC is identified as the lowest concentration of each antibiotic that inhibits visible growth. Although preparation of agar plates is labor intensive, this method is well standardized and reproducible and facilitates the efficient testing of large numbers of isolates ( Jenkins and Schuetz, 2012). Agar Disk Diffusion Disk diffusion methods measure visible bacterial growth on agar plates containing commercially prepared paper disks impregnated with a specific concentration of the target drug (Fig. 1b). The Kirby–Bauer disk diffusion assay (Bauer et al., 1966) is used in both clinical and research settings (Felmingham and Brown, 2001) and has been widely used to characterize antibiotic resistance for environmental isolates (Sayah et al., 2005; Kumar et al., 2013; Zhang et al., 2014). Both EUCAST and CLSI have standardized protocols for disk diffusion methods. Briefly, disk diffusion methods require that bacteria be spread across the surface of an agar plate, followed by placement of the antibiotic-impregnated disks on top of the bacteria (Fig. 1b). The drugs diffuse from the disks into the agar, establishing a gradient with more of the target antibiotic compound in the agar closer to the disk and less further from the disk. The target isolate will grow on the agar to a point at which the drug is concentrated enough to impede growth. The inhibitory zone diameter is influenced by the diffusion rate of the target antibiotic through the agar, a function of the molecular sizes and hydrophilicities of the compounds ( Jenkins and Schuetz, 2012). The diameter of the zone of inhibition is measured in millimeters either manually or using an automated system and then is compared with standardized EUCAST- or CLSI-interpretive criteria to designate the isolate as sensitive or resistant to the drug (Turnidge and Bell, 2005; Silley, 2012). As with broth dilution, composition of microbiological media and other test parameters can affect disk diffusion results (D’Amato and Thornsberry, 1979; Kluge 1975), and thus adherence to standardized protocols, publicly available through EUCAST and the World Health Organization (EUCAST, 2015; WHO, 2003), is of extreme importance. If a research study does not require identification of MICs but only entails separation of resistant from susceptible isolates, each of the above methods can be used in a breakpoint assay. The methods for breakpoint assays are similar, but each isolate is exposed to only the CLSI or EUCAST breakpoint concentration of the target antibiotic rather than to a wide range of concentrations. Breakpoint assays provide several advantages in that they are far less labor intensive and are more amenable to high throughput than MIC-focused assays. E-Tests E-test assays (AB Biodisk, bioMérieux) (Fig. 1c) combine the quantitative functionality of obtaining MICs via broth dilution with the ease and accessibility of disk diffusion methods (Brown and Brown, 1991). Each assay consists of a commercially prepared plastic strip containing a gradient of a specified antibiotic. The strip is placed onto the surface of an agar plate inoculated with the isolate; after incubation, results are based on reading an elliptical zone of clearing (inhibition) around the strip. The MIC of the bacterial isolate can be directly determined by identifying where the zone of inhibition intersects the precalibrated strip. Comparison of Culture-based Methods Comparative testing has shown that the results of microdilution, agar disk diffusion, and E-test assays are reliable and comparable (Table 2) (Baker et al., 1991; Joyce et al., 1992). Luber et al. (2003) compared methods for detecting antibiotic resistance in Campylobacter jejuni and Campylobacter coli from poultry and human samples and reported that, although results from all three methods were comparable, broth dilution methods were easiest and most reliable. Some suggest that agar-based methods for detection of antibiotic resistance are more reliable than broth dilution methods due to the inherent ability of agars to detect inoculum contamination ( Jenkins and Schuetz, 2012). Most culture-based testing approaches are labor intensive, with time-consuming steps required to prepare agar or 96-well plates, isolate and maintain pure cultures of bacteria, and/or Table 2. Summary of culture-based methods comparisons for antibiotic resistance testing (isolates from clinical samples). Methods† Target organisms BMD, E-test Staphylococcus aureus BMD, E-test S. aureus; Enterococcus BMD (CLSI), VITEK2 (automated) Enterobacteriaceae BMD (CLSI), VITEK2 (automated) Staphylococcus; Enterococcus BMD, ADD, E-test Salmonella; Campylobacter ADD, E-test Campylobacter BMD (CLSI), VITEK2 (automated), Staphylococcus ADD, E-test ADD, E-test Campylobacter BMD, ADD, E-test Campylobacter ADD, E-test Campylobacter BMD, ADD Campylobacter BMD, ADD E. coli Standards comparison many ADD: automated vs. manual many Superior performance‡ E-test BMD and E-test comparable comparable BMD and ADD ADD, E-test needs more standardization BMD E-test E-test comparable BMD comparable CLSI and EUCAST are comparable ADD results can be improved with automated readers Reference Khatib et al. (2013) Riedel et al. (2014) Bobenchik et al. (2015) Bobenchik et al. (2014) Karlsmose et al. (2012) Ge et al. (2002) Drew and Paulus (2014) Valdivieso-Garcia et al. (2009) Ge et al. (2013) McGill et al. (2009) Halbert et al. (2005) Benedict et al. (2013) Polsfuss et al. (2012) Hombach et al. (2013) † ADD, agar disk diffusion; BMD, broth microdilution. ‡ The Superior Performance column is general; most studies do not make a clear or definite recommendation, stating that the methods are comparable but may require additional standardization. 436 Journal of Environmental Quality create accurate serial dilutions of antibiotics in specified solvents and diluents. Up-front costs can be excessive: broth dilution requires specialized laboratory equipment (a plate reader), and all methods require maintenance of a sterile work environment (requiring autoclaves and biosafety cabinets). E-tests also can be expensive, costing as much as $20 per antibiotic strip. A comparison of costs of all methods places labor as the largest investment for culture-based methods of resistance detection, and no one testing method is far less costly than others. Data Interpretation and Study Design in Culture-based Studies Broth microdilution methods provide quantitative data using an MIC50 value (i.e., the concentration of antibiotic at which ³50% of the isolates in a test population are inhibited). The significance of MIC50 values increases with the number of isolates screened, and care must be taken when interpreting results from small test populations (10–30 isolates) (Schwarz et al., 2010) because under such conditions a few strains with high MICs will have a disproportionately high influence on the MIC50. One advantage to the presentation of data as a MIC distribution is that it allows for simple re-evaluation of the original data if interpretive criteria change over time. Studies using agar disk diffusion generally report percentages of isolates that are susceptible and resistant. However, zones of inhibition surrounding disks can be reported in a quantitative manner, also allowing for reinterpretation under changing criteria. A significant question inherent in any study analyzing microbial parameters in heterogeneous agroecosystems is that of representative sample, which is fundamental to the design of the sampling scheme (Persoons et al., 2011). Often the sample size is limited by practical and financial constraints. Davison et al. (2000) provide the example of estimating the prevalence of cattle carrying resistant E. coli in a herd of 200 cattle to within ±5% with 95% confidence. To accomplish this, 132 randomly selected cattle would need to be sampled if the expected prevalence is 50%. However, such sample size estimations rely on resistance testing with 100% sensitivity and 100% specificity and rely on previous knowledge regarding the prevalence of resistance. In addition, given that the number of potential target isolates in a single sample can number in the many thousands, how many isolates per sample must be examined to reach an accurate conclusion about antibiotic resistance? It is not known whether few hosts with high burdens of resistant bacteria are more or less important than many hosts with low burdens of resistant bacteria (Davison et al., 2000). If enrichment techniques are used to increase the microbial target, it may be advisable to analyze only a single isolate from that sample to reduce the chance of skewing the data through multiple analyses of the same bacterium. If only a single isolate is examined per sample, can these results be compared with studies where multiple isolates per sample were assayed? The measurement of resistance in the environment is highly complex, and the development of study designs that result in accurate and representative datasets is currently a critical knowledge gap (Wray, 1986; Corpet, 1993), but few studies to date have attempted to quantify the effects of environmental variations in study design. Culture-based versus Molecular Methods Culture-based methods are the foundation of international surveillance efforts to monitor antibiotic resistance, and each of the methods described above has been shown in multiple studies to provide reproducible results (Table 2) (Mayrhofer et al., 2008). Multiple studies have compared culture-based with molecularbased methods for testing of antibiotic-resistant bacteria, often reporting that the two methods do not provide interchangeable results (Virolainen et al., 1994; Jorgensen and Ferraro, 2009; Nordmann et al., 2012; Garcia-Armisen et al., 2013). This is not surprising given that the two approaches measure different parameters, with culture-based approaches measuring the response of single isolates to specific antibiotic concentrations and molecular methods generally focusing more on description of the entire microbial population within a sample. The decision about which type of method, or group of methods, is best to use in a study of environmental resistance depends on the specific research question. Is the presence of MAR bacteria of interest? If so, microbial isolation is the most direct way to assess the presence of multidrug-resistant bacteria. Although there is no universally accepted definition of “multidrug resistance,” it is recognized as the resistance to two or more classes of antimicrobial agents (Khurana and Wojack, 1994; Cabrita et al., 1999). Multiple antibiotic-resistant organisms are of intense clinical concern, and one advantage of obtaining a bacterial isolate is that multidrug resistance profiles can be obtained, and the isolate can then be characterized both phenotypically and genotypically. Thus, methods using bacterial cultivation are valuable in their ability to detect expression of multiple resistance genes in a single isolate, rather than just gene presence. Factors Confounding the Use of Methods Based on Bacterial Cultivation There are several factors that confound the use of methods using bacterial cultivation to assess antibiotic resistance. One is the inability of culture-based methods to identify cells that may have entered the viable but nonculturable (VBNC) state. Potential pathogens in the VBNC state, including E. coli and Salmonella, are capable of returning to the actively metabolizing state with the potential to initiate disease (Oliver, 2000). Wastewater disinfection methods, including chlorination, have been shown to induce VBNC (Oliver et al., 2005), clouding assessment of the effects of wastewater discharge on environmental antibiotic resistance. Oliver et al. (2005) reported that a mixture of free and combined chlorine, as occurs under typical wastewater disinfection, was an effective agent against E. coli and Salmonella typhimurium, with >99% loss of culturability. However, 0.4% of cells survived in the VBNC state, a number equating to ~104 CFU mL-1. A potential limitation to culture-based studies may be introduced by repeated culturing of isolates on nonselective media, as recent work suggests that this can contribute to losses in antibiotic resistance. Ludwig et al. (2012) report the observation of vancomycin-susceptible revertant mutations after repeated passages on nonselective media, raising concerns around interpretation of studies conducted on isolates after extended periods of storage. Journal of Environmental Quality437 It has been suggested that culture-based methods for assessment of antibiotic resistance are inherently biased due to the small percentage of environmental microbes that are actually culturable under laboratory conditions. This is an issue related to sample complexity and is not confined to culture-based methods because molecular studies targeting antibiotic resistance genes can also misrepresent the microbial community. It has long been known that the presence of genes alone cannot predict the functionality and metabolism of a microbial community. The interpretation of resistance data based on culture-based methods must always include an awareness of an inherent culture bias, and ideally culture-based studies can be strengthened with the integration of molecular methods. Integrating Culture-based and Molecular Data There is an increasing realization that comprehensive understanding of microbial ecology requires a combination of culture-based and molecular-based methods, and this is especially true for antibiotic resistance in agroecosystems. Luby et al. (2016) outline the application of quantitative polymerase chain reaction and other molecular methods for assessing relative abundance and diversity of antibiotic resistance genes in agroecosystems. Pairing these with culture-based methods allows the ability to determine if screened genes are functional and furthermore enhances identification of microbial taxa harboring individual resistance genes. Molecular methods are extremely useful for obtaining information on diversity and presence of antibiotic resistance genes. When combined with screening of individual strain libraries for resistance, a more comprehensive understanding of how the target gene is expressed within and between individual microbial phyla will provide information on critical control targets for management of antibiotic resistance. Increasingly, novel methods combining culture with molecular methods are enhancing efforts in resistance profiling. Functional metagenomics has become a vital tool for identifying genes that confer resistance to selected antibiotics in environmental samples (Donato et al., 2010; Rondon et al., 2000; Forsberg et al., 2014). This method involves the cloning of total community DNA into an expression vector, followed by transformation of the entire library into a host easily grown in the laboratory, often E. coli (Pehrsson et al., 2013; Mullany, 2014). The E. coli clones, which maintain the ability to express resistance genes within the cloned DNA, are then screened for their ability to grow in media containing antibiotics. Although not without limitations, functional metagenomics methods have been used in multiple studies to identify known and novel resistance genes in environmental samples (Amos et al., 2014; Su et al., 2014; Pehrsson et al., 2013). Berman and Riley (2013) used these methods to examine microbiota on retail spinach and identified five unique plasmids that increased resistance to antimicrobial drugs, suggesting that food bacteria could serve as an important reservoir for new drug-resistance determinants in human pathogens. Even when working with culture-based methods in tandem with molecular-based methods, there are a number of limitations that must be considered. The greatest of these is that there currently is no group of methods that allows simultaneous assessment of the presence and activities of all microorganisms in 438 a mixed sample. This results in data that inevitably provide an incomplete view. Culture-based methods are designed to target viable cells, the specific subset of which depends on the parameters of the individual assay. Polymerase chain reaction–based methods are designed to target specific sequences of DNA, which are dependent on primer sequence, amplification efficiency, and thermocycling parameters. The insights that have arisen from culture-independent analyses have been phenomenal, but to truly be able to verify these insights, the organisms must be grown in the laboratory. Ultimately, the “holy grail” for assessing antibiotic resistance in the environment will require standardized pipelines that combine culture-based and molecular parameters. These pipelines can then be used to assess the epidemiological and environmental potential of agroecosystem resistomes. Conclusions and Outlook Culture-based methods provide critical information on antibiotic resistant phenotypes, define both clinical breakpoints and epidemiological cut-off values, and allow evaluation of multidrug resistance. Several methods are used in culture-based studies, each shown to provide reproducible results with minimal error. A comprehensive understanding of the ecology of antibiotic susceptibility in the environment requires approaches that integrate cultivation with molecular methods and elucidate the relationship between phenotypic and genotypic measures of resistance. 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