Download Dissecting the effects of antibiotics on horizontal gene transfer

Prospects & Overviews
Problems & Paradigms
Dissecting the effects of antibiotics
on horizontal gene transfer:
Analysis suggests a critical role of
selection dynamics
Allison J. Lopatkin1), Tatyana A. Sysoeva1) and Lingchong You1)2)3)
Horizontal gene transfer (HGT) is a major mechanism
responsible for the spread of antibiotic resistance.
Conversely, it is often assumed that antibiotics promote
HGT. Careful dissection of the literature, however, suggests
a lack of conclusive evidence supporting this notion in
general. This is largely due to the lack of well-defined
quantitative experiments to address this question in an
unambiguous manner. In this review, we discuss the extent
to which HGT is responsible for the spread of antibiotic
resistance and examine what is known about the effect of
antibiotics on the HGT dynamics. We focus on conjugation,
which is the dominant mode of HGT responsible for
spreading antibiotic resistance on the global scale. Our
analysis reveals a need to design experiments to quantify
HGT in such a way to facilitate rigorous data interpretation.
Such measurements are critical for developing novel
strategies to combat resistance spread through HGT.
.
Keywords:
antibiotic resistance spread; horizontal gene transfer
DOI 10.1002/bies.201600133
1)
2)
3)
Department of Biomedical Engineering, Duke University, Durham, NC, USA
Center for Genomic and Computational Biology, Duke University, Durham,
NC, USA
Department of Molecular Genetics and Microbiology, Duke University
Medical Center, Durham, NC, USA
*Corresponding author:
Lingchong You
E-mail: [email protected]
Abbreviations:
CFU, colony forming unit; HGT, horizontal gene transfer; MGE, mobile genetic
element; MOB, mobility genes; MPF, mating pair formation; PFU, plaque
forming unit.
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Introduction
Lateral or horizontal gene transfer (HGT) refers to the transfer
of genetic material within or between species, separate from
the vertical descent of genes from parent to offspring. Since
its initial discovery in 1928 [1], HGT has increasingly been
appreciated as a driving force in microbial evolution [2, 3]. It
is estimated that up to 17% of the Escherichia coli genome is
derived from previous HGT events, and up to 25% of genomes
of other bacterial species [4].
In bacteria, HGT occurs through one of three major
mechanisms: conjugation, transformation, and transduction [5]. In conjugation, DNA is transferred from donor to
recipient through direct cell-to-cell contact [5–7]. Conjugation
occurs either through plasmid transfer or transmission of
chromosomally integrated conjugation elements (ICEs) [8],
including conjugative transposons (CTns) [9]. Conjugative
plasmids can replicate autonomously. They are self-transmissible when carrying both the genes required for transfer
machinery, and the replication origin recognized by the
transfer machinery. Conjugative plasmids that are not
transmissible are called mobilizable; these plasmids do not
encode the conjugation machinery, but they are transferrable
by the machinery encoded elsewhere [10]. Collectively,
conjugative plasmids are estimated to account for about half
of all plasmids [11]. Unlike plasmids, integrated conjugative
elements (ICEs) reside in the host chromosome but can be
excised and transferred by conjugation [12]. It is thought
that conjugation is responsible for the majority of antibiotic
resistance spread, since there is a high prevalence of
plasmids, plasmids can have broad host ranges [13], and
many plasmids carry antibiotic resistance genes [14–16].
In transformation, naked DNA is taken up by bacteria from
the environment and incorporated into the genome [17, 18],
while in transduction, gene transfer is mediated by bacteriophages [19]. Transduction and transformation can transfer
a wide range of sequences including antibiotic resistance
cassettes [20–24]. Transduction occurs following specific
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A. J. Lopatkin et al.
recognition between the phage and its cognate receptor on
the bacterial surface. This specificity results in a limited
host range for many phages, and is therefore believed to
have contributed less to the spread of antibiotic resistance
compared to conjugation [11]. Natural transformation often
occurs in a transient physiological state of competence
induced by environmental cues such as nutrient access or cell
density [25]. Although bioinformatic analyses reveal that
the majority of bacteria possess genes homologous to known
competence genes [26], it is unclear whether these bacteria
undergo transformation [27, 28]. Thus, studies that suggest
few naturally transformable organisms exist (i.e. [29]) typically assume that transformation does not play a significant
role in spreading of antibiotic resistance genes, though further
investigation is warranted.
Horizontally transferred genes can encode diverse functions, including metabolic traits [9, 30], virulence factors [31,
32], and antibiotic resistance [33–35]. Mobile genetic elements
(MGEs) carrying resistance are members of the communal
gene pool accessible to diverse microbes [10]. Since HGT can
cross phylogenetic boundaries, it can greatly accelerate the
spread of resistance genes [36]. Indeed, it has been suggested
that HGT has enabled the spread of resistance to most
antibiotics commercially available [37–39]. Conversely, the
use of antibiotics can potentially influence dynamics of HGT,
and thus, the spread of antibiotic resistance. However, an
antibiotic can affect the efficiency of HGT at the single-cell
level, modulate the overall HGT dynamics through selection,
or both. Elucidation of these effects requires rigorous design
of experiments to tease apart these different effects.
Conjugative spread of antibiotic
resistance
HGT and antibiotic selection both play critical roles in shaping
the global resistance landscape [40, 41]. HGT specifically
enables the local dispersal of resistance genes from a source
species to other strains or species within a local context
such as the gut of a patient [42, 43]. Carriers of newly
resistant bacteria (such as people, animals, or products)
continuously move and interact. This facilitated dispersal
enables dissemination of the newly resistant bacteria on a
geographic scale. In new environments, resistant bacteria
further propagate the resistance determinants through HGT.
Antibiotic use can magnify the overall effect of HGT by
enriching for the resistant progeny. Selection can increase the
abundance of cells capable of HGT, making further HGT
events more likely [44]. Together, this has likely enabled the
global spread of resistance [45, 46].
This is best exemplified by the spread of b-lactamase
(Bla) variants. Bla enzymes confer resistance to b-lactams,
which are one of the oldest and most widely prescribed
antibiotic class [38]. Bla variants, including extendedspectrum b-lactamases (ESBLs) [47], are often encoded on
plasmids [48, 49], and many bla genes likely spread via
conjugation [50, 51]. For example, MGE-associated CTX-M (a
common bla variant) in Enterobacteriaecae species shares
adjacent gene sequences to those from Kluyvera species [52],
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suggesting lateral acquisition. Similarly, the diversity of
species carrying sequence-identical blaNDM-1 (a metallo- blactamase conferring resistance to carbapenem drugs) on
conjugative plasmids implicates the role of conjugation.
Indeed, from analyses of sewage and tap water in New Delhi,
one study found the presence of blaNDM-1 in over 20 diverse
strains, many of which were never before associated with
ESBL resistance; every isolate could transfer its plasmid via
conjugation under certain experimental conditions [53]. The
intense b-lactam selection pressure over the past several
decades has likely facilitated the prevalence of such examples.
Other common horizontally transferred resistance genes
are similarly linked to antibiotic exposure. For example,
vancomycin is typically considered a last-resort drug for
methicillin-resistant Staphylococcus aureus (MRSA) infections. Although VanA vancomycin resistance is typically
associated with the transposable element Tn1546 common
to vancomycin-resistant Enterococcus (VRE) [54], VanAexpressing vancomycin-resistant S. aureus (VRSA) has begun
to emerge. Analysis of clinical samples shows that VRE indeed
can transfer VanA-encoding Tn1546 to MRSA by conjugation.
Similarly, despite fluoroquinolone (FQ) resistance initially
driven by mutations that cause target modification [55] or
increased efflux activity [56], researchers have recently
identified a new form of FQ resistance mediated by
plasmid-encoded Qnr protein. Qnr binds to DNA gyrase and
topoisomerase IV [57, 58], conferring moderate protection of
these components against FQ [59]. Studies have since reported
increasing prevalence of qnr and its variants on conjugative
plasmids isolated from the clinic [60, 61], often associated
with multi-resistant phenotypes such as ESBL producers.
Our gut microbiota harbors an abundance of resistance
genes [62, 63], and can act as a reservoir by which other
commensal or pathogenic bacteria can acquire resistance
through HGT [64–66]. Antibiotics can provide a favorable
environment for transfer by enriching for resistance already
present at low levels [42, 44]. Such selection can also influence
the abundance of resistance to other antibiotics if the two (or
more) genes are physically linked on the same MGE. For
example, one study detected high levels of qnr-mediated FQ
resistance in the gut flora of children that were treated with
non-FQ antibiotics [67]. Different factors may drive resistance
landscapes depending on the environment; however, one
study concluded that bacterial community composition, not
HGT, is the major determinant in shaping the resistance
landscape in natural soil environments [68].
Given the prevalence of resistance spread through
conjugation, it is tempting to assume that use of antibiotics
promotes HGT rates. Undoubtedly, application of antibiotics
in human medicine and animal husbandry has changed
the ecological landscape of resistance [69], and has likely
influenced HGT through modulating the abundance of species
carrying resistance located on MGEs [70]. It is even speculated
that antibiotics may modulate the tempo of HGT by selecting
for cells with higher transfer efficiencies [45]. However, the
spread of antibiotic resistance is not always correlated with
antibiotic use. For example, ESBL genes had evolved and
spread through conjugation millions of years before the
modern practices of antibiotics [71, 72]. In general, the extent
to which antibiotics modulate the HGT rate is a nuanced
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Reaction kinetics of conjugation
At the population level, conjugation can be approximated as a
biomolecular reaction between the donor (D) and the recipient
(R), resulting in the transconjugant (T), where D, R, and T
each represents the respective cell densities [73]. The rate of
generation for T by the two parents can be written as:
dT
¼ hRD
dt
ð1Þ
where h is the rate constant for the interaction, which we term
as conjugation efficiency.
During a short time window of conjugation (Dt), if both R
and D are roughly constant and if T is primarily generated by
the two parents (i.e. secondary conjugation by T is negligible),
T
the conjugation efficiency can be calculated as: h DRDt
. In
past studies, the conjugation readout is often described as
the relative frequency, defined as T per either parent (T/D or
T/R), the relative increase, defined by T per either initial
parent density (T/Di or T/Ri), or the ratio of T between two
experimental conditions (e.g. with and without antibiotics) [74]. Regardless of the terminology, comparisons between
the conjugation readouts under different conditions are used
to determine whether specific factors promote the observed
increase in T.
As evident from equation (1), however, these alternative
metrics can confound different factors that affect T. In
particular, T is generated either through conjugation, or cell
division. Thus, a change in T can also result from different
experimental conditions (i.e. varying D or R initial values, Dt)
or antibiotic-mediated selection (growth/death of D, R, or
T). These effects should be excluded or minimized when
determining if a factor (e.g. antibiotic) indeed affects the
conjugation efficiency h.
T
Simple simulations illustrate that DRDt
, as the estimate of
h, is the most robust for quantifying efficiency compared
with the other metrics (Fig. 1). We assume for this example
that one parent is sensitive to an antibiotic A, and the other
parent and transconjugant are resistant to the antibiotic
tested (Fig. 1a). Upon conjugation, the sensitive parent
becomes resistant to the antibiotic. When the true conjugation
efficiency (h) is independent of antibiotic concentration
(Fig. 1b), the alternative metrics can either over- or underestimate h, depending on whether the donor, recipient, or
initial density is used for the rate calculation. This bias is
T
magnified with greater mating times ðDtÞ. Instead, DRDt
closely
reflects the true conjugation efficiency (h) even in the
presence of growth (Fig. 1b, iv), though it is most accurate
with small Dt. When the true conjugation efficiency increases
100-fold with antibiotic (Fig. 1c) other metrics may falsely
represent the effect of antibiotic depending on how the
efficiency is defined and the sensitivities of each strain to the
antibiotic.
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Quantifying effects of antibiotics on
conjugation
When interpreting effects of antibiotics on conjugation, it is
critical to distinguish a change in h and that in R, D, and T.
The latter can result from a combination of a potential effect
of antibiotics on h and the subsequent selection dynamics
[75–78]. Antibiotics may promote h directly or indirectly
(Fig. 2). When transfer machinery expression itself is
independent of antibiotic exposure, antibiotics have been
speculated to elicit a global cellular level response that
indirectly stimulates or suppresses conjugation. In direct
induction, antibiotics can stimulate a cascade of molecular
events that result in the expression of transfer machinery [79,
80].
Past studies on these issues have resulted in apparently
contradictory conclusions (i.e. see [81]), such as the same or
similar antibiotics having opposing effects depending on the
experimental context. Typically, donors and recipients are
mixed in the presence of an antibiotic (or other stressors), and
transconjugants are measured after a period of time Dt to
determine effects of the antibiotic. By measuring T/Ri, one
study showed that combinatory treatment of sub-inhibitory
kanamycin (Kan) and streptomycin (Strep) concentrations
promoted conjugation for three different conjugative plasmids
in E. coli [82]. However, critical evaluation of the results
suggest that conjugation efficiency may not have likewise
increased, but rather the measure T/Ri reported on the timedependent expansion of the transconjugant population.
In particular, the antibiotics could have caused significant
impact on the growth rates. These differences are magnified
as cells proliferate over time. Indeed, when the effects of
antibiotic-mediated selection were likely minimal (during
the first 4 hours of conjugation), there were negligible
differences in the readout with and without treatment. Other
studies have similarly attributed an increase in frequency
to indirect induction of conjugation by the antibiotic [82–84].
Whether the conjugation efficiency also increased is less clear.
Similarly, reported suppression of conjugation may not
reflect a decrease in the conjugation efficiency either. One study
reported that increasing antibiotic concentrations did not
increase the total number of transconjugants (T) of an ESBL
plasmid after 24 hours, and sufficiently high concentrations
reduced T [85]. In these experiments, a slow transconjugant
growth rate could mask an increase in conjugation efficiency,
appearing as an overall decrease in HGT rate.
Given these caveats, the importance of accounting for time
to reduce selection effects has been recognized, although it
does not completely eliminate bias in data interpretation
without also accounting for growth effects. For example, one
study tested tetracycline (Tc) effect on the transfer of drug
resistant determinants from a complex bacterial mixture of
treated sewage effluent to an E. coli strain [86]. The results
showed that the shortest time for reliable detection of the
transfer effects is 3 hours. These experiments demonstrated
modest increase in transconjugant number in the presence
of Tc concentrations over 100-fold lower than the MIC for
recipient E. coli cells. Nevertheless, the study did not assess
the effects of Tc on growth of donors given that it is hardly
possible in the complex and uncharacterized microbial
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question that depends on several factors. To elucidate this
question, it is critical to clarify the definition of the HGT rate,
and to properly design experiments to generate conclusive
results.
Problems & Paradigms
A. J. Lopatkin et al.
Figure 1. Challenges in quantifying conjugation efficiency.
A: Growth rates of three populations, the donors (D, blue), recipients
(R, red), and transconjugants (T, green). The x-axis represents the
concentration of a generic antibiotic to which R is sensitive,
normalized with respect to the IC50 value, and both D and T are
resistant to. The y-axis is growth rate of the three populations. This
arbitrary selection regime was chosen as a representative sample of
the potential limitations in quantification. B: The true conjugation
efficiency h does not depend on sub-inhibitory antibiotic (dotted
black line). The x-axis is time, and the y-axis is the conjugation
metric for four different scenarios, corresponding to the title of each
panel: (i) relative frequency as defined by DT ; (ii) relative frequency as
defined by TR; (iii) relative increase as defined by PTi , where P is either
parent since the initial density is equal; and (iv) the conjugation
T
efficiency h ¼ DRDt
. Red lines indicate the metric in the absence of
antibiotic, and blue lines indicate the metric in the presence of
antibiotic. C: The true conjugation efficiency h increases 100-fold
with the antibiotic administered (dotted line, red no antibiotic, and
blue with antibiotic). The x-axis is time, and the y-axis is the
conjugation metric for four different scenarios described in b. Red
lines indicates the metric in the absence of antibiotic, and blue lines
indicate the metric in the presence of antibiotic.
community from the effluent, and therefore, the source of the
fourfold increase in HGT events remains unknown. Consistent
with prior discussion in this work, authors show that higher
Tc concentrations and longer experimental times result in
antibiotic selection rather of transconjugants and sometimes
decrease HGT frequencies.
Nevertheless, the importance of accounting for growth
kinetics for quantifying the transfer rate has been previously
recognized [73, 77, 87–89]. In doing so, the effect of various
factors on the conjugation process can be fairly compared.
For example, Johnsen et al. decoupled selection from growth
by inhibiting growth during conjugation in micro titer
wells (using auxotrophic strains and minimal media), and
then eliminated the parents to selectively grow transconjugants [76]. Using a sufficiently large number of well replicates,
the number of discrete transfer events (x, denoted t in the
original publication) could be statistically inferred. Specifically, the distribution of transconjugant cells in individual
wells after the mating period follows the Poisson distribution,
and therefore, x can be quantified based on the combined
probability of positive (growth) and negative (no growth)
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wells. As x represents transfer events only, it is not indicative
of the kinetic interpretation h. However, in the absence of
selection, changes in x can be conclusively attributed to the
experimental factor being tested. Indeed, mercury did not
affect x at low concentrations, but suppressed transfer at
higher concentrations.
Another study limited bacterial growth to decouple
selection dynamics from conjugation and address prior
limitations. Using an engineered conjugation system based
on the derepressed F plasmid, Lopatkin et al. showed that 10
different antibiotics covering major classes in terms of mode of
action did not increase the conjugation efficiency (determined
T
as h DRDt
) when measured in the absence of growth [77]. At
high concentrations, some antibiotics even slightly reduced
the conjugation efficiency. The same conclusion held for
other conjugation plasmids tested. In particular, antibiotics
did not promote the conjugation efficiency for nine native
conjugative systems, including those of ESBL pathogens.
The study further showed that the selection dynamics alone
could explain the increase or decrease in the fraction of
transconjugants during prolonged experiments (up to 12 hours).
As such, even if antibiotics did change the conjugation
efficiency, contribution of the change was negligible in
comparison with the influence of selection. Both this study
and that by Johnsen et al. found that other factors, such as
energy availability and physiological state, more strongly affect
the conjugation efficiency.
Quantifying HGT in complex settings
As environments become more complex, such as natural soils,
toxic water, or biofilms, quantifying both HGT, and the effect
of antibiotics on HGT, becomes increasingly challenging [90,
91]. Indeed, quantification of conjugation in these environments faces several hurdles, such as the inability to carefully
estimate the influence of a drug on growth rates, and the
involvement of spatial distribution and motility in the
gene transfer process. Proper control experiments must
be established to tease apart different effects of antibiotics.
Without these data, reports may be misleading, such as
studies suggesting antibiotics may promote the conjugation
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Problems & Paradigms
Figure 2. Potential ways in which antibiotics influence conjugation
dynamics. A: In the absence of antibiotics, conjugation occurs at a
certain rate, and the resistant progeny grow normally but are not
selected for. B: In the presence of antibiotics, there are two
proposed scenarios as to how antibiotics modulate conjugation
efficiency. In the first, antibiotics are thought to indirectly modulate
the conjugation efficiency. In this case, antibiotics elicit an overall
global response in the cell. For these cases, it is possible that
either (i) the efficiency has increased, or (ii) the efficiency has not
increased compared to (a), but after a period of growth and
selection all three scenarios result in the same outcome. In the next
case (iii), antibiotics directly induce the expression of conjugation
machinery (illustrated here by excision of resistance gene from the
chromosome into a circularized plasmid). This scenario is a common
mechanism for conjugative transposons and integrative conjugated
elements (ICE). In that case, the conjugation efficiency increases
even before selection has occurred.
efficiency in pure and activated sludge cultures [92], and
reduce the efficiency in sewage water [93]. Taken together,
such reports suggest that HGT rates may differ depending on
the environment. Another study showed enhanced conjugation in biofilms of plasmids containing Kan resistance, when
exposed to sub-inhibitory concentrations of Kan (as opposed
to imipenem, a b-lactam) [94]. However, the authors do not
distinguish an effect on the conjugation efficiency from
the antibiotic-induced changes on the biofilm development
dynamics. Growth rate estimates were not accounted for
in the quantification of conjugation, which was further
confounded by interchangeable use of different metrics of
conjugation. Thus, the conclusion that bacteria can “sense the
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antibiotics to which they are resistant,” and in return, can
facilitate its horizontal spread of resistance genes, is
premature. Instead, the results in these studies may reflect
effects of antimicrobials on biofilm formation or community
composition rather than the influence on gene swapping.
New experimental approaches and quantitative measures
should be implemented to determine how antibiotics affect
the conjugation dynamics in these environments by taking
into account compounding factors; some studies have indeed
begun to move in that direction [86].
Direct induction of conjugal transfer
Despite the confounding effects of selection dynamics, the
effects of antibiotics on the conjugation efficiency can be
inferred from mechanistic evidence at the molecular level. For
example, it has been shown that antibiotic-induced SOS
response in Vibrio cholerae and E. coli can alleviate the
repression for transcription of transfer genes, and induce
transfer of SXT, an ICE naturally found in V. cholerae [95]. In
particular, SOS response activation of RecA protein induces
autocleavage of SetR repressor, and hence, upregulates
expression of tra genes, leading to enhanced SXT transfer
through conjugation. Although only the relative frequency is
reported (T/D), antibiotic influence on conjugation frequency
of strains with non-cleavable SetR mutants was negligible;
this result suggests specificity in the antibiotic effect on
SXT transfer via the SOS pathway. Similarly, excision and
conjugative transfer of CTnDOT, a conjugative transposon
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A. J. Lopatkin et al.
carrying Tc resistance, is inducible by Tc [96–98] by 100–
1,000-fold [99]; in the absence of Tc there was no observed
transfer [100]. Again, although the conjugation readout was
reported as T/R, the authors showed that increasing Tc
concentration elevates the circularized plasmid concentration
in the cell and upregulates expression of the conjugation
machinery [101].
In these examples, however, the precise extent to which
antibiotics modulate the conjugation efficiency remains
unclear. In either study, effects of selection were not
accounted for when quantifying conjugation. Utilizing only
one parent for quantification could create an apparently
biased increase: these experiments were performed in
conditions where bacterial growth likely occurred (37˚C,
and anywhere from 1 to 16 hours on filter paper). Further,
comparisons between species are invalid in the first case,
due to differences in experimental conditions (e.g. incubation
time), as evidenced by simulations (Fig. 1, comparing the
change in relative frequency as a function of time).
Prospects & Overviews
scales are required for different systems, this should be
adjusted for when comparing efficiencies.
Effects of antibiotics on transformation
Quantification of transformation and transduction often face
similar challenges as quantification of conjugation, including
a lack of generally accepted metric and the confounding
effects of antibiotic-mediated selection. Often the transformation efficiency is defined as the number of transformants,
which is then normalized to the DNA amount or to total viable
cell counts (transformation frequency). However, a quantification framework, similar to that for conjugation, may be
applicable here, as it was done for analyses of transformation
in Azotobacter vinelandii [108]. Transformation can be
approximated by mass-action kinetics between the recipient
(R) and DNA concentration (N), generating a transformant (T):
dT
¼ hR NR;
dt
Experimental platforms to measure
conjugation
Choosing an appropriate experimental platform may
facilitate quantification by reducing bias inherent in some
experimental methodology. Conjugation can be quantified
using selection, by choosing markers (such antibiotic or
heavy metal resistance that typically reside on plasmids)
that can distinguish transconjugants from parental strains
[76, 102–104]; colony-forming units (CFU) are used for
quantification. If the antibiotic being tested is similar to the
one used for plating, its effects on the conjugation process
should be readily distinguishable by ensuring complete
separation between conjugation and selection, as described
previously. In comparison, phenotypic readouts are advantageous because higher throughput platforms can be used,
such as plate readers and flow cytometry for fluorescence [105]. With proper reporters, quantification using
microscopy opens doors for single-cell and microfluidics
analysis [106]. Indeed, a microfluidics platform was recently
used to quantify conjugation dynamics by using fluorescently labeled donors, recipients, and transconjugants [77].
However, the detection must be sensitive enough to capture
events occurring at low frequencies. Nucleic acid measurements, such as qPCR, can also be used in measuring the
conjugation efficiency in certain systems [107]; this type of
quantification does not require transconjugant enrichment
(e.g. by selection), and could circumvent biases introduced
by growth.
Regardless of the platform, experiments should be designed
to ensure comparable results. For example, the effects of
antibiotics among different systems introduce additional
compounding factors, such as different sensitivities of
donors and recipients to antibiotics. It is critical to understand
what concentrations were used in each particular study
with respect to minimal inhibitory concentration of a drug
(MIC) or its IC50 value, and whether the antibiotic tested
corresponds to the resistance transferred. If different time-
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ð2Þ
where hR is the rate constant of the transformation reaction.
The transformation efficiency can be approximated as hR ¼
T
RNDt under appropriate conditions, and should be evaluated
case-by-case (for examples, see [109–111]). For example,
the majority of known naturally transformable organisms
have inducible competence machinery with rare exceptions
(Neisseria spp. and to a certain extend Helicobacter pylori) [28,
112, 113]. The induction of competence is strictly regulated,
often involving cell density-dependent quorum sensing
regulation [114, 115], and only a fraction (fC) of the bacterial
population (R) becomes proficient in DNA acquisition.
Therefore, the transformation efficiency can be estimated as
a hR when competent cell density, RC ¼ fCR, is taken into
account. Also, the DNA used in transformation assays will not
always contain the sequences necessary for transformation
or transformant’s survival, such as for transformation with
chromosomal DNA and quantification by selective plating.
In this case, only a small portion of DNA is encoding the
selective marker, dramatically reducing effective DNA concentration; quantification should be adjusted accordingly.
This is further complicated by various DNA properties (source,
topology [circular, linear, strandedness], size, and sequence
distributions) and in bacteria that have sequence-specific DNA
uptake machinery (i.e. see [116]).
Despite the difficulties in direct comparison of transformation efficiency, antibiotics were shown to elicit a stress response
that induces or enhances expression of competence genes in
specific organisms [114, 117, 118]. Effects of antibiotics on
transformable bacteria are often measured as changed expression level of competence proteins, assayed by reporter-fusions,
estimating RC, hR , or their combination [118–120]. Several
studies revealed that certain antibiotics, in particular those
affecting DNA replication or causing DNA damage, enhance
transformation by increasing the fraction of competent cells,
while other antibiotics have no effect on transformation
efficiency [118–120]. More detailed analyses of competence
induction in Streptococcus pneumoniae revealed that stressinduced replication stalling was responsible for increased
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Effects of antibiotics on transduction
In transduction, antibiotic presence can affect two types of
bacterial populations – phage producers and phage recipients. These two populations can be separated both temporally
and spatially, and therefore, the two effects are usually
measured individually.
Antibiotics may affect HGT dynamics by promoting
prophage excision and host cell lysis [80]. This effect can
be measured by quantifying the increased titers of phage in
culture or increased transcription of phage genes in the host
cells. For example, one study showed that b-lactam-mediated
induction of S. aureus prophage results in high-frequency
transfer of staphylococcal pathogenicity islands [122]. FQs and
agriculture antibiotic carbadox induced prophage activity in
Salmonella [123, 124]. Another study demonstrated that certain
antibiotics significantly increased prophage induction, transcription, and production of Shiga toxin 2 (Stx2)-carrying
phage in enterohemorrhagic E. coli O104:H4 strain while other
drugs strongly inhibited its production or did not have an
effect [125].
In these examples, differences in growth rates due to
antibiotic exposure may confound data interpretation. In the
last case, supernatant was collected from cultures grown for
15 hours and population density was not accounted for [125].
Even with sub-inhibitory concentrations, comparing the foldchange between treatment conditions may unfairly bias a
faster growing culture. Conversely, the effects of antibiotics
that significantly increased the transduction efficiency may
have been under-estimated due to this difference as well.
Antibiotics may also influence transduction by affecting
the recipient strain. This process can be approximated by
mass-action kinetics of the interaction between a free phage
particle (P) and a recipient cell (R), generating a transductant
(T) [126, 127]:
dT
¼ hT PR;
dt
ð3Þ
where hT describes the rate constant of the transduction
reaction. Similar to the metrics above, the transduction
T
. In most previous
efficiency can be quantified as hT ¼ PRDt
studies, the transduction efficiency is defined as the number of
transductants that acquired a phage-associated selective
marker per volume of phage lysate [128], or by the multiplicity
of infection (MOI), defined by the ratio of infectious agents
to the target host cells [129]. As with the previous two
mechanisms, both metrics are subject to bias, as they do not
account for effects of time, nor the selection environment.
For example, one study showed that different biocides can
either promote or reduce the transduction efficiency by
Bioessays 38: 0000–0000, ß 2016 WILEY Periodicals, Inc.
including the biocide in the recovery media, affecting the
recipient state [130].
Concluding remark
Examination of reported antibiotics influences on the HGT
rate reveals that antibiotics-promotion of gene transfer
might not be as common as previously thought. The major
limitations come from various metrics that do not account
for time, and thus, selective pressure of the antimicrobials.
The question to what extent antibiotics contribute to the
spread of resistance through modulating HGT warrants
future studies. We emphasize the need for carefully designed
quantitative studies to draw definitive conclusions, as well as
standardized definitions of HGT efficiency for each mode of
transfer. To this end, understanding how different selection
regimens influence multiple populations engaging in HGT
will enable proper quantification, especially in more complex
environments either with additional populations or spatial
constraints. Understanding how antibiotics modulate HGT
efficiency parameters is paramount to designing effective
treatment strategies aimed at minimizing the spread of
resistance. Such understanding can improve the use of
existing antibiotics as we wait for development of novel
antimicrobials, some of which potentially even targeting
HGT [131–135].
Acknowledgments
Related work in the You lab was partially supported by the
Office of Naval Research (N00014-12-1-0631), National Science
Foundation (LY), Army Research Office (LY, #W911NF-14-10490), National Institutes of Health (LY: 1RO1GM098642,
1RO1GM110494), a David and Lucile Packard Fellowship (LY).
The authors have declared no conflict of interest.
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