Download Culture-based Methods for Detection of Antibiotic Resistance in

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.
Acknowledgments
This project was supported by Agriculture and Food Research Initiative
Competitive Grant no. 2013-68003-21256 from the USDA National
Institute of Food and Agriculture.
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