Download Distinguishing between resistance, tolerance and

PERSPECTIVES
OPINION
Distinguishing between resistance,
tolerance and persistence to
antibiotic treatment
Asher Brauner, Ofer Fridman, Orit Gefen and Nathalie Q. Balaban
Abstract | Antibiotic tolerance is associated with the failure of antibiotic treatment
and the relapse of many bacterial infections. However, unlike resistance, which is
commonly measured using the minimum inhibitory concentration (MIC) metric,
tolerance is poorly characterized, owing to the lack of a similar quantitative
indicator. This may lead to the misclassification of tolerant strains as resistant, or vice
versa, and result in ineffective treatments. In this Opinion article, we describe recent
studies of tolerance, resistance and persistence, outlining how a clear and distinct
definition for each phenotype can be developed from these findings. We propose a
framework for classifying the drug response of bacterial strains according to these
definitions that is based on the measurement of the MIC together with a recently
defined quantitative indicator of tolerance, the minimum duration for killing (MDK).
Finally, we discuss genes that are associated with increased tolerance — the
‘tolerome’ — as targets for treating tolerant bacterial strains.
The isolation and genetic characterization
of antibiotic-resistant bacterial strains has
uncovered many molecular mechanisms
of resistance1, including mutations in
the drug target, enzymatic activity that
directly inactivates the antibiotic and the
activation of efflux pumps that pump
out the antibiotic2. The genes that are
involved in these mechanisms are termed
the ‘resistome’ (REF. 3). However, as long
ago as 1944 it was observed that bacteria
were able to survive extensive antibiotic
treatments without acquiring resistance
mutations4,5. The terms ‘tolerance’ (REF. 6)
and ‘persistence’ (REF. 4) were coined to
distinguish these modes of survival from
‘resistance’, but the definitions of these
different terms, and their distinction from
one another, have remained somewhat
ambiguous7,8. ‘Resistance’ is used to describe
the inherited ability of microorganisms
to grow at high concentrations of an
antibiotic2, irrespective of the duration
of treatment, and is quantified by the
minimum inhibitory concentration (MIC)
of the particular antibiotic (FIG. 1a),
whereas ‘tolerance’ is more generally used
to describe the ability, whether inherited or
not, of microorganisms to survive transient
exposure to high concentrations of an
antibiotic without a change in the MIC,
which is often achieved by slowing down
an essential bacterial process (FIG. 1b). In
this Opinion article, we follow the tolerance
terminology defined by Kester and Fortune8,
namely that tolerance enables bacterial cells
to survive a transient exposure to antibiotics
at concentrations that would otherwise be
lethal9. For example, tolerance to β‑lactams
may occur when bacteria grow slowly 10,
which is associated with slower cell wall
assembly. As β‑lactams require active cell
wall assembly to kill bacteria, slower growth
will result in a longer minimum treatment
duration to achieve the same level of killing,
regardless of the concentration of the
antibiotic. Dormancy may be viewed as an
extreme case of slow growth, and dormancy
that leads to tolerance may also be termed
‘drug indifference’ (REF. 11).
320 | MAY 2016 | VOLUME 14
Tolerance may be acquired through
a genetic mutation or conferred by
environmental conditions11; for example,
poor growth conditions have been shown
to increase tolerance to several classes of
antibiotic. This tolerance was exploited by
Lederberg and Zinder to isolate auxotrophic
mutants, as only non-growing auxotrophs
are able to survive when a mutagenized
bacterial population is exposed to
penicillin in the absence of an amino acid12.
A non-growing state that leads to tolerance
can also be induced by the antibiotic itself.
This drug-induced tolerance subsequently
protects the bacteria from the lethal activity
of the antibiotic9.
In contrast to resistance and tolerance,
which are attributes of whole bacterial
populations, ‘persistence’ is the ability
of a subpopulation of a clonal bacterial
population to survive exposure to high
concentrations of an antibiotic13. Persistence
is typically observed when the majority
of the bacterial population is rapidly
killed while a subpopulation persists for
a much longer period of time, despite the
population being clonal. The resulting
time–kill curve will be biphasic14, owing to
the heterogeneous response of persistent and
non-persistent subpopulations. The slower
rate of killing of the persistent subpopulation
is non-heritable: when persistent bacterial
cells are isolated, regrown and re-exposed
to the same antibiotic treatment, the same
heterogeneous response to the drug will be
observed as in the original population, with
the division of the population into persistent
and non-persistent subpopulations4
(FIG. 1c). The first direct observations of
persistence at the single-cell level showed
that slow growth, as well as dormancy, of
a small subpopulation of bacterial cells
can underlie the high rate of survival
of a whole population14. Additional,
generally dose-dependent, mechanisms of
persistence that also display biphasic killing
have been observed subsequent to these
initial observations15.
Experimental discrimination between
the different strategies used by bacterial
cells for survival during exposure to
antibiotics is important for several reasons.
First, these survival strategies, despite
superficial similarities, differ in their
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Figure 1 | Characteristic drug responses of resistance, tolerance and
persistence. The survival strategies of resistance, tolerance and persistence to antibiotic treatment each manifest as a characteristic drug
response. a | The minimum inhibitory concentration (MIC) for a strain of
bacteria that is resistant to an antibiotic is substantially higher than the
MIC for a susceptible strain. Coloured wells represent bacterial growth,
whereas wells in which the antibiotic concentration is high enough to kill
the bacteria are in light brown. b | The MIC for a tolerant strain of bacteria
basic mode of action, which means that a
treatment will often be ineffective if it is
applied irrespective of the survival strategy.
Second, the underlying mechanisms, and the
experiments that are required to investigate
them, may be very different for each strategy.
Third, the range of antibiotics that is affected
by the drug response can differ according
to the survival strategy. For example,
tolerance by slow growth will often confer
an advantage to several classes of antibiotic,
whereas most resistance mechanisms are
specific to one class of antibiotics. Finally,
the quantitative measurement of resistance,
tolerance or persistence requires different
metrics and experimental procedures for
each survival strategy.
In this Opinion article, we discuss the
current basis for and the strategies used to
distinguish between resistance, tolerance
and persistence to antibiotics in bacterial
strains, without any a priori knowledge
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| Microbiology
for killing (MDK; for example for 99% of bacterial
cells in the
population
(MDK99)) for a tolerant strain is substantially higher than the MDK99 for a
susceptible strain. c | A persistent strain of bacteria has a similar MIC and
a similar MDK99 to a susceptible strain; however, the MDK for 99.99% of
bacterial cells in the population (MDK99.99) is substantially higher for a
persistent strain than the MDK99.99 for a susceptible strain. Concentrations
and timescales are chosen for illustration purposes only.
of the molecular mechanisms that are
involved. These terms have often been
used interchangeably in the literature, but
we propose a clear and distinct definition
for each term, and an experimental
framework for distinguishing between these
phenotypes that uses a standardized and
measureable metric to detect tolerance to
drug exposure — the minimum duration
for killing (MDK). We hope that the
combination of the MIC and the MDK may
be used as standards for the in vitro characterization of sensitivity to antibiotics, which
ultimately may lead to better treatments for
recalcitrant infections.
Resistance or tolerance?
Resistance. Resistance to antibiotics, which is
typically caused by inherited mutations,
is associated with numerous molecular
mechanisms that have been comprehensively
reviewed elsewhere16,17. It is important to
NATURE REVIEWS | MICROBIOLOGY
note that mechanisms of bacterial resistance
decrease the effectiveness of the antibiotic;
that is, a higher concentration of the
antibiotic is required to produce the same
effect in a resistant strain as is produced
in a susceptible strain18 (FIG. 1a). Resistance
is quantified by the MIC, which can be
defined as the minimum concentration
of an antibiotic that is required to prevent
net growth of the culture. In practice, the
MIC is measured by exposing a bacterial
population to increasing concentrations
of the antibiotic in a standardized growth
medium. This enables the measurement of
the minimum concentration at which growth
is not detected, typically after 16–20 hours
of exposure to the antibiotic19. The range of
concentrations that is tested in a clinical
microbiology laboratory is usually limited
to the concentrations of the antibiotic used
in the clinic. The MIC that is determined by
these tests is viewed as a convenient metric
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for resistance, and a bacterial strain with
a higher MIC than another strain will be
regarded as more resistant2. Measurements
of the MIC that indicate total insusceptibility
to an antibiotic may be viewed as an extreme
case of resistance.
The MIC has two major limitations as a
general metric for measuring the response of
a bacterial strain to an antibiotic. First, it is
not informative for bacterial strains that are
tolerant, rather than resistant. Second, the
MIC measured in vitro can vary according
to the experimental conditions that are
used, which may affect the usefulness of this
metric as a predictor of the effectiveness
of the antibiotic in vivo20. However, the
ease of measuring the MIC means that it is
currently the only metric that is routinely
used to inform treatment decisions for
bacterial strains isolated in the clinic21.
Tolerance. Tolerance is generally understood
to be the ability of a bacterial population to
survive a transient exposure to antibiotics9,
even at concentrations that far exceed the
MIC. Unlike resistance, tolerance applies
only to bactericidal antibiotics and not to
bacteriostatic antibiotics, as all bacteria
are expected to survive transient exposure
to bacteriostatic antibiotics8,9, which
are not lethal and instead merely arrest
growth. Therefore, all discussion of drug
exposure in this Opinion article should
be assumed to refer to concentrations of
bactericidal antibiotics.
Importantly, a longer exposure to an
antibiotic, rather than a higher concentration
of an antibiotic, is required to produce the
same level of killing in a tolerant strain as
is produced in a susceptible strain (FIG. 1b).
As tolerant bacteria can have the same
MIC as non-tolerant bacteria, the MIC
is not informative as a metric to evaluate
tolerance22,23. One suggested approach
for the evaluation of tolerance is the
measurement of time–kill curves at different
concentrations of an antibiotic9. However,
without a standard method for interpreting
these curves the results that are obtained
in different laboratories are difficult to
compare24. Another measure of tolerance
that has been proposed is the MBC/MIC
ratio25, where MBC represents the minimum
bactericidal concentration, namely the
concentration of an antibiotic that is required
to kill ≥99.9% of cells in a bacterial culture,
typically after 24 hours of incubation. For
cases in which concentrations of antibiotic
that are near the MIC cause only growth
arrest but the MBC results in death, the
MBC/MIC ratio will produce a large value.
Therefore, this metric may accurately
evaluate the level of drug-induced tolerance
but was shown to correlate poorly with other
forms of tolerance22,23,26,27.
Recently, the MDK was described as a
quantitative measure of tolerance that can be
extracted from time–kill curves, based on the
notion that a tolerant bacterial strain requires
more exposure time to be effectively killed
than a susceptible strain. The MDK is defined
as the typical duration of antibiotic treatment
that is required to kill a given proportion of
the bacterial population28 at concentrations
that far exceed the MIC (that is, when
the rate of cell death is independent of the
concentration of antibiotic). For example,
the minimum duration of treatment that is
required to kill 99% of a bacterial population
(MDK99), which can be extracted from a
time–kill curve (FIG. 1b). The assumption
that underlies the MDK as a measure of
tolerance is that the killing effect reaches
saturation at high concentrations of an
antibiotic so that it is almost insensitive to
concentration and dependent only on the
duration of exposure29.
Similarly to the MIC, which can be
used to compare the level of resistance
between bacterial strains, the MDK can
be used to compare the level of tolerance
between strains. In contrast to the killing
rate (that is, the rate at which survival
decreases exponentially), which can only
be extracted from exponential killing
curves, the MDK simply integrates all of
the different factors that together underlie
a faster or slower overall killing efficacy,
such as delays in killing or killing curves
that are not exponential. Therefore, this
quantification of tolerance is not dependent
on any particular molecular mechanism. We
argue that the MDK should be the preferred
metric for the measurement of tolerance, as
it enables a quantitative comparison between
different bacterial strains and conditions.
Furthermore, an evaluation framework that
measures both the MDK and the MIC would
enable a clear distinction to be made between
resistance (an increase in the MIC) and
tolerance (an increase in the MDK).
Reports of tolerance in the literature
are generally associated with slow growth
and reduced metabolism14,30–33. As in the
β‑lactam example, the slowing or complete
cessation of growth results in a reduced or
diminished susceptibility to many antibiotics.
This is a direct result of the evolution of these
antibiotics in microorganisms competing
for resources, in which the production of
antibiotics that target fast growing bacterial
cells, which are the most competitive for
322 | MAY 2016 | VOLUME 14
resources, is selected for 34. Different classes
of antibiotic have evolved to target different
processes that are required for growth and it
is sometimes possible to artificially decouple
the efficacy of the antibiotic from the growth
rate (that is, decouple target production and
growth), once the process that is targeted
by the antibiotic is known. For example, in
Escherichia coli cells that are growth-arrested
by the stringent response, treatment with
chloramphenicol enables cell wall assembly
to resume without the full resumption of
cell growth. As a result of the resumption
of cell wall assembly, the bacterial cells
are sensitive to β-lactams, even though
they remain essentially growth-arrested35.
However, reports that E. coli cells can be
killed by β‑lactams during growth arrest are
often based on experiments that measure
the growth arrest of the batch culture, which
means that a dynamic balance of growth
and death — in which β‑lactams only target
growing cells but the overall growth of the
culture is stationary — cannot be ruled out.
Although some studies have assayed growth
arrest in single cells, these studies assayed
the absence of growth at the beginning of the
treatment36 and cannot rule out that growth
occurred during treatment.
Which form of tolerance?
We identify two main forms of tolerance,
which we term ‘tolerance by slow growth’
and ‘tolerance by lag’. Although these two
forms of tolerance share an increased MDK
compared with susceptible bacterial cells, the
mathematical description and measurement
protocol differ between them. The distinction
arises because tolerance by slow growth
occurs at steady state, whereas tolerance
by lag is a transient state that is induced by
starvation or stress.
Tolerance by slow growth. Conditions that
decrease the rate of growth have long been
known to increase tolerance to numerous
antibiotics10,37–40, as the mechanisms of
action of these drugs share a requirement
for growth. For example, the mechanism of
action of β‑lactams relies on the disruption
of bonds in the peptidoglycan layer that
occurs during bacterial growth. β‑lactams
exploit this process by preventing the
reassembly of the peptidoglycan bonds, which
eventually leads to cell lysis6. Therefore, the
number of defects in the peptidoglycan layer
increases in proportion with the growth rate.
Indeed, the killing rate of bacteria that are
exposed to β‑lactams has even been shown
to be proportional to the growth rate, which
demonstrates the strength of correlation
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between slow growth and tolerance for
these antibiotics10. Similarly, the exposure of
bacterial cells to DNA gyrase inhibitors, such
as fluoroquinolones, results in DNA damage
and killing at a rate that is proportional
to the growth rate41. Indeed, plotting the
MDK99 versus the growth rate, using values
extracted from time–kill curves published in
the literature10,29,37–40, confirms the positive
correlation of killing activity and growth rate
for several species of bacteria and different
classes of antibiotic (FIG. 2a).
Tolerance by slow growth can either
be inherited, when a bacterial species or
strain has an inherently slow growth rate,
or non-inherited, when slow growth occurs
because the conditions for growth are poor.
Species with inherently slow growth rates
include Mycobacterium tuberculosis, which
has a doubling time in nutrient-rich medium
of approximately 24 hours42. This doubling
time is approximately 40 times longer than
that of E. coli and, not surprisingly, the MDK99
of M. tuberculosis strains is also approximately
40 times longer than the MDK99 of E. coli.
Auxotrophs and other bacterial strains with
mutations that reduce their intrinsic growth
rate also show inherited tolerance12.
Non-inherited tolerance by slow growth
occurs when bacterial growth is impaired,
such as by poorer growth conditions43,
the location of a cell within a biofilm
or exposure to inhibitors44. When the
antibiotic is added in the presence of these
growth-reducing conditions, killing will
be reduced. Note that dormancy can be
viewed as the extreme case of slow growth,
in which the growth rate is zero. Importantly,
a decrease in growth rate has been observed
for intracellular bacteria when within a
host cell. For example, Salmonella enterica
subsp. enterica serovar Typhimurium cells
with arrested growth have been detected
in infected macrophages31. Accordingly,
infections by intracellular pathogens are
notoriously difficult to eradicate, even when
treated with antibiotics that readily penetrate
host cells. The notion that tolerance rather
than resistance underlies the resilience of
these infections is supported by in vitro assays
that showed that intracellular Staphylococcus
aureus45 treated with dicloxacillin had a
fivefold increase in the MDK, but no change
in the MIC, compared with extracellular
S. aureus, which suggests that tolerance
enables this intracellular pathogen to survive
treatment with dicloxacillin.
A special case of tolerance by slow growth
is drug-induced tolerance, which occurs
when bacterial cells respond to antibiotic
exposure by reducing or arresting their
growth. This effect has been observed in
a
various Gram-negative and Gram-positive
bacteria when exposed to antibiotics,
including an antibiotic that inhibits cell
wall synthesis9, whereby the growth arrest
was mediated by the defective induction
of autolysins9,46, and the fluoroquinolone
ciprofloxacin, whereby the growth arrest
was mediated by the induction of a
stress response47.
In summary, non-inherited tolerance
can be triggered by external stress factors
that include starvation48, host factors42 and
even the antibiotic itself 47,49. As might be
expected, tolerance by slow growth also
occurs when antibiotics are added at the
stationary phase of growth, in which the net
growth rate of the bacterial population is zero
(but conditions are permissive for a balance
between the growth and death of individual
cells). In addition, in what may be viewed as
an extreme case of tolerance by slow growth,
tolerance at the stationary phase can occur
when the growth rate of individual bacterial
cells is zero50, which can produce an extremely
long MDK51. The protective effect of growth
arrest as a passive survival strategy can be
enhanced by the activation of stress response
mechanisms that provide further protection
from antibiotic stress52. Some of these
additional protective mechanisms, such as the
production of efflux pumps, may also reduce
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Figure 2 | Tolerance arises from slow growth or lag phase. a | The minimum duration for killing (MDK) for 99% of bacterial cells in a population
(MDK99) is plotted against doubling time for several combinations of bacterial strain or species and antibiotic, as extracted from time–kill curves in
the literature10,26,28,29,37,38,40,56. The dashed line shows the best fit for the
relationship between the MDK99 and the doubling time for strains of bacteria that are tolerant by slow growth, which demonstrates the correlation
between these two variables. The shaded area highlights the distribution
of bacterial strains that are tolerant by lag; these strains were detected by
exposure to the drug directly on dilution from the stationary phase.
b | A schematic growth curve that shows the importance of subculturing
Nature Reviews | Microbiology
to reach strictly exponential growth. An initial
1 in 100,000 dilution of a
bacterial population from a culture in the stationary phase of the growth
cycle is followed by serial 1 in100 dilutions; in each instance, the colony is
grown until the population density reaches 107 colony forming units (CFU)
ml –1 before dilution. Each dilution reduces the number of residual
non-growing bacterial cells — that is, cells in the lag phase — in the population and several dilution steps may be required until the population is
composed only of cells in the exponential growth phase, with no cells
remaining in the lag phase.
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the effective concentration of the antibiotic,
which increases the MIC and results in a
mixed phenotype of resistance and tolerance.
Tolerance by lag. In addition to the stationary
phase, another phase of the bacterial growth
cycle during which bacteria do not grow, and
may therefore be transiently protected from
killing by antibiotics, is the lag phase. The
lag phase is defined as the time it takes for
growth-arrested bacterial cells (for example,
under starvation conditions) to resume
exponential growth when adjusting to an
environment that is permissive for growth
(for example, when starved bacterial cells
are diluted into fresh nutrient conditions).
The typical mean lag time of E. coli K-12
populations when diluted from an overnight
culture is 30 minutes, but this time can be
substantially longer when a culture has been
in the stationary phase for several days53,54
before dilution into fresh nutrients. Although
these two growth phases are often thought
to be similar to each other, the lag phase has
now been shown to be a distinct metabolic
state from the stationary phase30,55, in which
bacterial cells must first adapt to the increase
in nutrient concentration before resuming
exponential growth55. Therefore, tolerance by
lag differs from the extreme case of tolerance
by slow growth that occurs at the stationary
phase. Similarly to the lag phase that occurs
after stationary growth, transient growth
arrest can also occur during a lag phase
that follows transitions between growth
conditions — for example, when bacterial
cells enter the host environment or switch
between different niches32. Importantly,
tolerance by lag is a transient phenotype
that is not sustained when the culture has
sufficient time to fully adjust to the new
conditions. Therefore, tolerance by lag is
modelled as a decay process28, whereas
tolerance by slow growth is a steady-state
phenotype that is characterized by a reduction
in killing rate. Thus, the mathematical
descriptions of these two forms of tolerance
are inherently different28.
Tolerance by lag occurs when the antibiotic
treatment is shorter than the duration of
the growth arrest14,56. The protective effect
of the lag phase on the survival of the
bacterial population is very broad, as it can
enable tolerance to different antibiotics23,28,
in addition to other stresses, including
exposure to the host immune system57 and
the induction of prophages58. Despite the
transience of growth arrest at the lag phase,
tolerance by lag can be very effective, reaching
an MDK of many hours or days. For example,
it has been shown that the intermittent
exposure of E. coli to a β‑lactam antibiotic
can select for a lag phase that is 10 times
longer than the lag phase of the ancestral
population, reaching an MDK99 of more
than a day 28. Remarkably, the duration of
the lag phase evolved to match the duration
of antibiotic treatment in as few as eight
exposures to the drug. Owing to the tolerance
that was conferred by the extended lag time,
antibiotic treatment eventually became
ineffective, even when changing the class
of antibiotic, as long as the duration of the
treatment was the same28. Several genes were
repeatedly mutated in these populations
(BOX 1), which led to an inherited tolerance by
lag. By contrast, no change in the MIC was
detected, which suggests that the phenotype
of tolerance by lag may evolve more rapidly
than the emergence of resistance. The rapid
evolution to tolerance by lag that was
observed in this in vitro assay, in which
bacterial populations adapted to the duration
of the treatment rather than to its chemical
composition, calls for an evaluation of the
importance of the evolution of tolerance in
the host environment.
Measuring tolerance. It is important to
realize that the experimental protocol for
the in vitro measurement of tolerance differs
according to whether the measurement is for
tolerance by slow growth or tolerance by lag
(BOX 2). To measure tolerance at the lag phase,
exposure to the antibiotic must occur directly
on transition to the lag phase (generally, when
diluting from a stationary-phase culture).
If the culture is instead first diluted into fresh
medium for an undetermined period of
time before exposure to antibiotics, a mixed
population of exponentially growing and
non-dividing bacterial cells arises (FIG. 2b).
The proportion of the bacterial population
that survives exposure to the antibiotic will
then depend on the time between dilution
into fresh medium and exposure to the
drug, owing to the complex population
dynamics of the exit from the lag phase,
which is heterogeneous at the single-cell
level59. The dependence on experimental
parameters that can be challenging to control
may result in non-reproducible results and
ambiguous measurements of tolerance or
persistence (see below).
By contrast, tolerance by slow growth
should be measured in a steady-state culture
during exponential growth, ensuring that
no lagging bacterial cells are carried over
from the stationary phase. This steady-state
culture can be achieved in chemostats or in
cultures that are sub-cultured several times
during the exponential phase. Note that true
324 | MAY 2016 | VOLUME 14
exponential growth ends long before the
transition to the stationary phase that occurs
at high cell density, typically already at an
OD600 (optical density at 600 nm) of 0.1 in
rich medium60.
Persistence and heterogeneity
For those antibiotic treatments that effectively
kill the majority of the bacterial population,
subpopulations that are not killed by the
antibiotic can nevertheless emerge4,61, even
in clonal cultures. When these surviving
subpopulations are grown in the presence
of the same antibiotic, the heterogeneous
response is repeated51,62. This phenomenon
is termed ‘bacterial persistence’ and the
surviving bacterial cells are referred to as
persisters. We note that ‘persistence’ is also
used more generally to describe infections
that are not cured effectively and persist in the
host63, including those infections that may be
unrelated to the definition of persistence used
in this Opinion article to denote the presence
of a subpopulation of persisters in a clonal
population of bacteria.
As opposed to tolerance and resistance,
persistence only occurs in a subpopulation of
bacterial cells. Persistence can be detected by
the presence of a bimodal (or multimodal)
time–kill curve that deviates from the simple
decay expected from a uniform bacterial
population13. In the simple case of two
coexisting subpopulations, persistence is
characterized by a switching between two
phenotypes — susceptible and persistent.
Persisters constitute the less numerous
subpopulation (typically less than 1%) and
are killed at a slower rate than the susceptible
cells13. We propose that the first step towards
characterizing the heterogeneity of bacterial
populations under antibiotic treatment is
to determine whether persisters survive
the exposure to the antibiotic because they
are transiently more resistant or because
they are transiently more tolerant than the
majority of the population (BOX 2).
Time-dependent persistence.
Time-­dependent persistence is characterized
by the presence of a subpopulation of
tolerant bacteria, which typically has either
a longer lag time (tolerance by lag) or slower
growth rate (tolerance by slow growth)
than the majority of the population. These
two types of persistence have very different
dynamics and were previously defined as
Type I persistence and Type II persistence,
respectively 14. All of the characteristics of
tolerance that are described above for a whole
bacterial population can also be applied
to a subpopulation with time-dependent
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Box 1 | The tolerome: genetic factors that increase the MDK
An increase in the minimum duration for killing (MDK) occurs when the
killing rate of bacterial cells that are exposed to antibiotics is slowed
down by one or more of numerous mechanisms. Non-inherited tolerance
can be triggered by external stress factors, such as starvation48, low
temperature11, host factors42 and even the antibiotic itself47,49. However,
we use the term ‘tolerome’ to describe the genetic factors that have been
repeatedly shown to increase tolerance or time-dependent persistence.
For the β-lactam and fluoroquinolone classes of antibiotic, the stringent
response, which inhibits bacterial growth, has been shown to have a
central role in tolerance. During nutritional stress, the decrease in the
availability of amino acids leads to an accumulation of uncharged tRNAs
that triggers the production of guanosine tetraphosphate (ppGpp), an
alarmone stress signal that mediates the stringent response89. The first
high-persistence mutants to be isolated in the laboratory66 were
Escherichia coli mutants found to have mutations in the hipBA
toxin–antitoxin module, which encodes HipA, a toxin, and its cognate
antitoxin, HipB. HipA was later shown to inactivate an essential
amino-acyl tRNA synthetase, glutamate–tRNA ligase (GltX)90,91, thus
producing high levels of ppGpp owing to the accumulation of uncharged
tRNAs. When hipA is expressed above a threshold set by the abundance of
HipB, a stringent response is induced. Importantly, the stringent response
involves the induction of a lag phase (that is, a transient growth arrest), as
has been shown in both Gram-negative90 and Gram-positive bacteria92.
As the expression of hipA increases, the lag phase becomes longer, which
results in a longer MDK and a phenotype of tolerance by lag64. Aside from
hipA, the overexpression of other toxin genes in toxin–antitoxin modules
can also produce similar tolerance phenotypes93.
The tolerome has been studied using mutational screens for tolerance,
which have identified numerous mutations that are related to the
activation of the stringent response, including mutations in hipA66,
hipB94 and methionine–tRNA ligase (metG)94, as well as many global
regulators95 and metabolic genes, such as glycerol‑3‑phosphate
dehydrogenase (glpD)54,94,96. A study that used experimental evolution also
identified metG as a gene that is associated with tolerance, as well as
prsA and the toxin–antitoxin module vapBC28. High expression of other
toxins97 and virulence factors82 may also transiently arrest growth to
trigger a phenotype of tolerance by lag97. A recent study found that genes
persistence. Indeed, the only difference
between time-dependent persistence
and tolerance is the fact that only part of
the bacterial population is responsible
for the slower killing that is observed in
persistence (FIG. 1c). Therefore, the molecular
mechanisms that lead to tolerance are
expected to be relevant for time-dependent
persistence (BOX 1). For example, inducing
the expression of toxins in toxin–antitoxin
modules results in either the growth arrest
of a subpopulation of induced bacterial cells
(that is, time-dependent persistence), when
expressed at low levels, or in dormancy of
all induced bacterial cells, when expressed at
high levels64,65.
A well-characterized example of
time-dependent persistence is the hipA7
allele of the hipA gene, the presence of
which produces a high-persistence mutant66
that generates two subpopulations with
very different lag time distributions to one
another 64. The subpopulation with the longer
with functions that are related to amino acid synthesis and genes that
encode toxin–antitoxin modules were among hundreds of genes
implicated in tolerance to a drug that belongs to the aminoglycoside class
of antibiotics98. Interestingly, the number of genes identified for the
tolerome is substantially larger94 than the number of genes identified for
the resistome3, which suggests that the evolution of increased tolerance
may occur faster than the evolution of increased resistance, as observed
in an experimental evolution study based on intermittent in vitro
exposures to a β-lactam antibiotic28.
The molecular mechanisms of tolerance that slow down killing by
antibiotics are also associated with time-dependent persistence, which
applies to a heterogeneous clonal bacterial population in which tolerance
is present in a subpopulation but not in the majority of bacterial cells.
However, persistence poses an additional intriguing question: how can a
clonal bacterial population spontaneously differentiate into
subpopulations with different tolerance levels? The role of molecular
noise in generating variability that leads to persistence has been reviewed
elsewhere99,100 but can be briefly summarized as stochastic fluctuations in
the concentration of cellular factors that affect growth. These
fluctuations may be the outcome of changes in production and
degradation rates or uneven partitioning following cell division101, and
may then be further amplified by regulatory feedback circuits102. For
example, toxin–antitoxin modules can contribute to persistence through
a threshold mechanism that amplifies noise64,103 to result in stochastic
activation of the stringent response33,90. Accordingly, the deletion of
toxin–antitoxin modules104,105 or stringent response genes65 leads to a
decrease in persistence. Examples of persistence that arise from
fluctuations that are produced by asymmetric cell division have been
reported in mycobacteria106–108.
Note that the tolerome does not include genes that are associated with
dose-dependent persistence, such as those encoding efflux pumps69,109 or
catalase–peroxidase (katG)15, as dose-dependent persistence is
associated with genes that are implicated in resistance rather than
tolerance. prsA110 has been reported to belong to both the resistome and
the tolerome, but future work will be required to carefully determine
whether it is a bona fide genetic determinant of both the minimum
inhibitory concentration (MIC) and the MDK.
lag time will not be detected in standard
measurements of the culture lag time, as the
exit of the culture from the lag phase will be
dominated by the subpopulation with a short
lag time59. However, the heterogeneity of
the lag times between the two subpopulations
translates into a bimodal killing curve
(FIG. 1c), which in our proposed framework is
termed ‘persistence by lag’. The key features
of the time–kill curve for time-dependent
persistence, whether by lag or by slow
growth, are bimodality and insensitivity to
the concentration of antibiotic (assuming
that the concentration is substantially higher
than the MIC). Time-dependent persistence
can be measured by extracting the MDK from
the time–kill curve obtained from a bacterial
population that is exposed to a concentration
of antibiotic that is high enough to reach
saturation. Importantly, for the heterogeneous
bacterial populations that are relevant to
persistence, the measurement will also rely on
the percentile of cells killed that is chosen to
NATURE REVIEWS | MICROBIOLOGY
define tolerance. For example, the MDK99 is
only sensitive when it is applied to bacterial
populations in which more than 1% of
cells are persisters; therefore, the detection
of smaller subpopulations may require a
different choice of percentile for the MDK
measurement, such as the MDK99.99, which
is the duration of treatment that is required to
kill 99.99% of a bacterial population (FIG. 1c).
Similarly to the measurement of tolerance
by lag and tolerance by slow growth, it is
important to note that a different protocol
is required for the in vitro measurement
of persistence depending on whether the
measurement is for persistence by lag or
persistence by slow growth67 (BOX 2).
Dose-dependent persistence. Although
most studies of persistence relate to
time-dependent persistence (BOX 1),
subpopulations of persisters have also
been reported that instead have a transient
decrease in antibiotic sensitivity. For example,
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Box 2 | Framework for the measurement of resistance, tolerance and persistence
We have developed an experimental framework for the use of batch
culture measurements to distinguish between the various possible
strategies for survival under antibiotic stress. The framework is based on a
flowchart (see the figure) that classifies each bacterial strain as more
resistant, tolerant or persistent than a wild-type reference strain, and
further classifies tolerant and persistent strains into the subtypes of
tolerance by lag, tolerance by slow growth, time-dependent persistence
by lag, time-dependent persistence by slow growth and dose-dependent
persistence. The individual steps of the flowchart are, for the most part,
not very different from existing protocols, but are organized together into
a single framework. The framework is designed to distinguish between
the survival strategies without accounting for the very different molecular
mechanisms that may be involved, and we hope that this will enable the
comparison of results between different laboratories, or even stimulate
the development of improved definitions to those proposed in this
Opinion article.
The framework involves up to five experimental tests (see the figure, in
orange). First, the minimum inhibitory concentration (MIC) is measured for
both a susceptible reference strain of bacteria and for a strain of interest
(for example, a wild-type strain and a mutant strain, respectively). In
common with the standard approach for identifying resistant strains of
bacteria in the clinic, this step classifies a mutant strain as resistant if the
MIC is substantially higher in the mutant strain than in the wild-type strain.
Strains of bacteria that have the same MIC as the reference strain are
further characterized in a second step that measures the minimum
duration for killing (MDK) for 99% of the population (MDK99), which is a
value extracted from a time–kill curve at a concentration at which the
killing efficacy of the antibiotic reaches saturation. The choice of 99% for
the percentile that is measured in this step is
designed to evaluate the tolerance level of the
bulk population, as the percentile is low
enough to be relatively insensitive to persister
Strain to
subpopulations
characterize
(unless they are
highly enriched in
the population) but
MIC
Resistant
high enough to
High
capture the
MIC
Low
MIC
MDK99
Low
MDK99
MDK99.99
Low
MDK99.99
Susceptible
dynamics of effective killing by the antibiotic. A strain with an MDK99 that
is substantially higher than the reference strain, but with an equal MIC, is
characterized as tolerant. For these tolerant strains, a third step is then
used to distinguish between tolerance by lag and tolerance by slow
growth. In this step, survival under treatment with an antibiotic is
compared between a bacterial culture that is inoculated from the
stationary phase and a bacterial culture that is inoculated from a strictly
exponential phase (FIG. 2b). For strains in which the MDK99 is high only for
the culture inoculated from the stationary phase, and thus the duration
of killing is dependent on the duration of the lag phase but not on the
rate of growth, the form of tolerance is classified as tolerance by lag. By
contrast, when the MDK99 is high for both cultures, tolerance by lag can be
ruled out and the form of tolerance is thus classified as tolerance by
slow growth.
For strains of bacteria with both an equal MIC and an equal MDK99 to the
reference strain, an alternative third step is used to establish whether
persistence is present in a subpopulation of bacteria too small (less than
1%) to be detected by the MDK99 measurement. In this step, higher
percentiles are used to measure the MDK. For example, for a bacterial
population in which 0.2% of cells are persistent, an increased exposure
time to the antibiotic is required to kill 99.99% (MDK99.99) of the population
than 99.99% of the reference strain population; therefore, a strain with a
substantially longer MDK99.99 than the reference strain, but with both an
equal MIC and an equal MDK99 to the reference strain, will be classified as
persistent (FIG. 1c).
For strains of bacteria that have been identified as persistent, a fourth
step is required to distinguish between dose-dependent persistence (owing
to a resistance mechanism transiently present in a subpopulation of
bacteria) and time-dependent persistence (owing to a tolerance
mechanism transiently present in a subpopulation of bacteria). For
dose-dependent persistence, the higher MDK values in the previous two
steps are not caused by the presence of slow growth or lag phase persisters,
but by the presence of a subpopulation of bacterial cells that transiently
express a resistance factor that better enables them to survive the
concentration of the antibiotic used to measure the MDK. Therefore,
the MDK99.99 measurements are repeated at a concentration of antibiotic
that is increased twofold compared with the previous step, to determine
whether the high MDK values are due to dose-dependent persistence,
which is indicated by a strong dependence on the concentration of
antibiotic, or time-dependent persistence, which is indicated by a
weak dependence on the concentration of antibiotic.
Tolerant
High
Finally, as with tolerant strains of bacteria, for strains that are
MDK99
shown to have time-dependent persistence, a further step is
required to determine whether time-dependent persistence is due
Concentration
Time-dependent
to a subpopulation with a long lag phase (persistence by lag; also
dependency
persisters
High
known as ‘Type I’ persistence) or due to a
Low
MDK99.99
dependency
subpopulation that has slow growth
Stationary
(persistence by slow growth; also known as
versus exponential
High
High MDK
‘Type II’ persistence). In this fifth step, the same
High
MDK
inoculum
dependency
only in
in both
test is used to distinguish between the two
stationary
phenotypes as is used to distinguish between
Dose-dependent
Tolerance (or persistence) Tolerance (or
tolerance by lag and tolerance by slow growth
persisters
persistence) by lag
by slow growth
(see above).
Nature Reviews | Microbiology
this can occur when a resistance factor,
such as an efflux pump, is transiently
overexpressed in a subpopulation of
bacterial cells68,69. The overexpression of a
resistance factor in these subpopulations
of persisters causes a reduction in the
effective intracellular concentration
of the antibiotic and thus results in a
lower antibiotic sensitivity, as a higher
concentration is required to achieve the same
rate of killing. When the bacterial population
is exposed to a concentration of antibiotic
that is high enough to reach saturation for
the majority of cells in the population but
not for the subpopulation of persisters, a
bimodal time–kill curve will be observed.
We propose that these persisters are classified
as dose-dependent persisters.
326 | MAY 2016 | VOLUME 14
The difference between resistance
and dose-dependent persistence resides
in the transient heritability of the
overexpression of the resistance factor. When
dose-dependent persisters are regrown to
a full population, the resistance factor will
only be overexpressed in a subpopulation of
bacterial cells in the new population, so that
the new population is also heterogeneous
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with regard to antibiotic sensitivity. A
decrease in the effectiveness of antibiotics that
we attribute to dose-dependent persistence
has been associated with the transient
overexpression of efflux pumps69 and the
multiple antibiotic resistance (marRAB)
operon68, as well as the transient decrease
in expression of an enzyme that activates
the antibiotic isoniazid15. In this latter work,
persisters in a population of Mycobacterium
smegmatis had decreased levels of pulsed
expression of catalase–peroxidase
(katG)15, and were therefore able to grow
in the presence of isoniazid. Consistent
with dose-dependent — rather than
time-dependent — persistence, growth
rate and survival were not correlated in this
population, which suggests that a decrease in
the growth rate was not responsible for the
increase in persistence.
Similarly to time-dependent persistence,
dose-dependent persistence can be detected
by the presence of a bimodal time–kill
curve (FIG. 1c), which is the hallmark of
persistence, as well as by the transiency of the
survival effect (as regrowth will produce
another heterogeneous population of
bacterial cells rather than a population
that is uniformly resistant to antibiotics).
It should be noted that dose-dependent
persistence, when inherited for a sufficient
number of generations for colonies to
become visible on plates that are treated with
antibiotics, has sometimes been referred
to as heteroresistance61,70. Heteroresistance
has mostly been described in S. aureus but
requires further characterization, as recently
reviewed elsewhere71.
The defining feature of dose-dependent
persistence is that higher doses of
the antibiotic decrease survival more
effectively than a longer duration of
exposure, in contrast to the longer
treatment duration that is required to
decrease survival in populations with
time-dependent persistence, such as
those with a subpopulation of dormant
persisters (BOX 2). In addition, as with
resistance, dose-dependent persistence
typically increases survival to a specific
class of antibiotic and, furthermore, is often
independent of the rate of cell growth.
By contrast, time-dependent persistence
provides a more general protection against
several classes of antibiotic that target
mechanisms associated with cell growth,
such as β‑lactams and quinolones23,51.
A systematic classification of which
antibiotics are more prone to dose-dependent
or time-dependent persistence awaits further
characterization of drug responses.
Conclusion and future prospects
In this Opinion article, we propose
that bacterial survival under antibiotic
stress is characterized by two major
factors — resistance and tolerance.
We suggest that these factors can be
quantitatively estimated through the
measurement of two parameters: the MIC
for resistance and the MDK for tolerance.
Finally, we propose a classification
framework that we argue will enable not
only the identification of resistant and
tolerant bacterial strains but also the
clarification of complex cases that include
at least one tolerant or transiently resistant
subpopulation of bacterial cells — for
example, persisters in heterogeneous
clonal populations. We predict that this
classification will provide a useful approach
to identify and distinguish between the
different survival strategies. Furthermore,
it may help to define a ‘tolerome’ that is
composed of gene targets that have been
shown to affect the MDK (BOX 1). In the
clinic, these insights may be useful for
establishing more effective treatment
regimens that are tailored to the specific
survival strategies used by the infecting
pathogen. For example, dose-dependent
persistence might be targeted by known
inhibitors72 of resistance, such as efflux
pump inhibitors, whereas time-dependent
persistence might be countered by an
extension of the treatment duration. For
strains with tolerance or time-dependent
persistence, the MDK can provide clear
predictions of the duration of treatment
that is required to treat an infection, and
could thus be combined with current
pharmacokinetic and/or pharmacodynamic
models to guide treatment regimens. Indeed,
current practice has empirically extended
the duration of treatment for bacterial
strains that are notoriously slow growing 2.
However, in the case of tolerant strains of
bacteria in which the MDK is very high,
the toxicity of the antibiotic to the host
may limit the duration of treatment2. In
addition, ambulatory treatments are rarely
capable of maintaining a constant level of
antibiotic concentration in the body, as this
would require constant administration for
the majority of antibiotics that are typically
removed from the serum within a few hours
after administration73. Therefore, alternative
strategies against tolerant bacterial pathogens
are required74.
One avenue to be explored is the use
of existing antibiotics for which the drug
response has been found to be less prone
to tolerance, such as daptomycin, which is
NATURE REVIEWS | MICROBIOLOGY
effective even when applied to stationary-­
phase cultures39. Systematic screens have
been carried out to search for compounds
that are more effective against tolerant
strains. For example, a recent screen
for US Food and Drug Administration
(FDA)-approved compounds that remain
effective at the stationary phase has led to
the identification of promising candidates
for treating tolerance by slow growth in
Borrelia burgdorferi 75. Other systematic
screens have searched for new compounds
that can be used in combination with
conventional antibiotics to decrease
tolerance. Several compounds have been
identified in these screens that are effective
against time-dependent persisters76
or against tolerance in biofilms77; however,
the effectiveness of these compounds has
not yet been assessed in the clinic. As an
alternative to systematic screening, targeted
treatment design can make use of recent
insights into the major pathways that lead to
tolerance and into the metabolism of tolerant
cells (BOX 1). For example, understanding the
role of the protein degradation pathway in
persistence has already led to the targeting
of this pathway, showing promising results
both in vitro and in vivo78.
Although we expect our proposed
framework, which simplifies the characterization of time–kill curves under antibiotics
to two main parameters, to be powerful
for distinguishing between resistance and
tolerance, more subtle effects may not be
fully captured by the MIC and the MDK
metrics. For example, heteroresistance,
which we briefly mentioned above, can be
considered to be dose-dependent persistence
that is heritable for sufficient generations
to enable colony growth. The characteri­
zation of hetero­resistance requires additional
measurements to those in our framework;
in particular, the switching rate, namely the
number of generations over which the
resistance is heritable, is an additional
key parameter that needs to be evaluated
to predict the outcome of treatment for
heteroresistant bacterial strains. Finally,
we note that drug-induced tolerance (or
drug-induced persistence), namely the
ability of some microorganisms to arrest
growth in response to antibiotic stress47,49,
can result in an MDK that is very long
and therefore difficult to measure. The
challenge of drug-induced tolerance is that
the non-growing state can be induced for
the duration of the exposure to the antibiotic.
For antibiotics that do not kill non-growing
bacteria at all, the MDK may become too
long to measure for practical reasons.
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An important question has been raised
as to whether the in vitro study of tolerance
or persistence is relevant to the failure of
antibiotic treatments in vivo8. The question
can be applied to our framework by asking
whether an extended MDK in vitro is
relevant to the likelihood of treatment
failure for the same bacterial strain in vivo.
Methods that have been developed for the
study of single cells (BOX 3), specifically
for the detection of single persisters with
a high MDK in vitro30,31,79, have recently
been applied to the detection of persistence
in vivo in mouse models of infection, which
demonstrated that dormant persisters are
present in infections with S. Typhimurium31
or M. tuberculosis 42. Interestingly, both
persistence by slow growth and persistence
by lag can be detected in the same bacterial
infection42,80. Dormancy and the resulting
phenotype of persistence by slow growth
were attributed to stresses that are induced
by host factors and nutrient deprivation.
Infections with S. Typhimurium showed a
clear correlation between single-cell growth
rate and survival under treatment with
Box 3 | Single-cell measurements of persistence
In contrast to the measurement of resistance and tolerance across whole populations of bacterial
cells, which has been possible since the 1940s, the measurement of the heterogeneous response to
antibiotics observed in populations with persisters was only made possible with the development,
in the past two decades, of single-cell techniques111. The first direct identification of persistence at
the single-cell level used a microfluidic device to study the hipQ and hipA7 mutants of Escherichia
coli that had been identified in genetic screens for persistence to antibiotic treatment14.
The bacterial cells were grown in the device, which is able to keep single cells within the
observation field of the microscope, and exposed to a transient antibiotic treatment. By tracking
individual bacterial cells, microscopy images before exposure to the antibiotic could be matched
to the small number of bacterial cells that survived treatment with the antibiotic (that is,
persisters), which revealed that persisters were either slow growing14 or had a long lag phase
before treatment with the antibiotic. Therefore, these persisters were a subpopulation of tolerant
bacterial cells (that is, time-dependent persisters); specifically, persisters in the hipQ mutant
population were tolerant by slow growth, whereas persisters in the hipA7 population were tolerant
by lag. In subsequent work, microfluidics was used in combination with dynamic fluorescence
microscopy to identify the window of time in which the differentiation into persisters fully
develops, by using the abundance of an induced fluorescent protein that was expressed from a
synthetic promoter as a proxy for metabolic activity30. In another example of time-dependent
persistence, an imaging study that used a microfluidic device known as the mother machine112
showed that the expression of virulence genes was correlated with a decrease in growth rate, and
a higher minimum duration for killing (MDK), in a subpopulation of Salmonella enterica subsp.
enterica serovar Typhimurium cells expressing a fluorescent marker for virulence82. Microfluidics
was also used to observe time-dependent and dose-dependent persistence in Mycobacterium
smegmatis15,106. Using a reporter for the activator of the antibiotic, it was shown that persistence to
isoniazid was associated with variations in the concentration of the activator between bacterial
cells, possibly leading to variations in the effective concentration of the antibiotic. A
non-microscopy method that has been developed for the detection of dose-dependent
persistence is a high-throughput assay that uses femtolitre droplets formed on a hydrophilic-in‑­
hydrophobic micropatterned surface to enclose single bacterial cells pre-incubated with
fluorescein-­di‑β‑d‑galactopyranoside (FDG)113. FDG, which is a precursor of the fluorescent dye
fluorescein, is hydrolysed inside the cell by β‑galactosidase; however, efflux pumps can efficiently
export FDG before hydrolysis can occur. By measuring the fluorescence signal, it was possible to
quantify the activity of efflux pumps in individual bacterial cells, and thereby infer variability in the
expression of efflux pump genes, which can lead to dose-dependent persistence. Time-dependent
persistence can also be measured without microscopy, as the duration of the lag phase in single
cells can be measured using the ScanLag technique28,53.
High throughput can be obtained by fluorescence-activated cell sorting (FACS), which has been
used to enrich for time-dependent persisters that are persistent by lag79. To enrich for these cells,
the expression of a fluorescent protein is induced in all cells. When the cells are moved into an
inducer-free medium, in which the expression of the fluorescent protein is repressed, non-growing
cells will maintain a high level of fluorescence, whereas the fluorescent proteins will be diluted in
growing cells. This method has been used to show that non-replicating (that is, time-dependent)
persisters arise in populations of S. Typhimurium upon internalization by macrophages31,105.
Persistent subpopulations enriched by FACS can be further analysed by microarray114 or
phenotypic assays, which may shed light on the underlying metabolism of each form of
persistence115. FACS can also be used to identify dose-dependent persistence at the single-cell
level; for example, fluorescent antibiotics were used to detect dose-dependent persistence in
methicillin-resistant Staphylococcus aureus (MRSA) cells116.
328 | MAY 2016 | VOLUME 14
fluoroquinolones80, which is indicative of
persistence by slow growth or tolerance
by slow growth. In addition, small
subpopulations of cells with very long lag
phases were detected42, which is indicative
of persistence by lag. Finally, evidence
for dose-dependent persistence was also
observed in M. tuberculosis under treatment
with isoniazid.
The MDK may, in principle, be a
useful indicator of which subpopulation
of persisters is the dominant factor in
treatment failure or relapse; however,
measuring the MDK in vivo is technically
extremely challenging, owing to the difficulty
of controlling parameters, such as the level of
antibiotic over time and spatial homogeneity,
and of accurately determining the size of
the bacterial population. An alternative
to directly measuring the MDK in vivo is to
determine the correlation between in vivo
pharmacokinetic and pharmacodynamic
measurements and in vitro measurements
of the MDK, as has been done for the
MIC81. For example, a study in S. aureus
showed that strains that were identified as
tolerant in vitro, with an MDK99 extended to
24 hours, were most effectively killed in vivo
by a longer treatment duration rather than
a higher antibiotic concentration45, which
indicated that these strains were also tolerant
in vivo. Therefore, the routine determination
of the MDK of pathogens isolated in the
clinic may help to direct more effective
therapies, even when the measurement is
made in vitro. However, the evaluation of
the MDK currently requires the labour-­
intensive measurement of time–kill curves;
thus, more practical methods to evaluate this
metric would make our framework more
amenable to clinical use (and would also be
beneficial to the study of bacterial survival
strategies in the research laboratory).
A final caveat is that the in vitro
evaluation of the MDK of pathogens in the
host is limited to inherited tolerance, which
arises from mutations that increase tolerance.
Non-inherited tolerance in the host may
be due to environmental factors, such as
the complex interactions between bacterial
pathogens and host cells80 and the immune
system82, or the presence of biofilms83 and
interactions with other bacterial species84.
A major challenge would then be to develop
in vitro assays that recreate the conditions
that induce tolerance in vivo. Alternatively,
direct measurement of the MDK in vivo may
become possible, owing to the development
of sequencing technologies that may enable
the inference of time–kill curves from in vivo
sequencing data.
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The definitions used in the framework
we introduce in this Opinion article are
formulated to describe the response of
bacterial populations to antibiotic stress;
however, we propose that they are also
applicable to a wide range of stresses and
biological systems. For example, it has
been shown that cancer cells can exhibit
drug responses that, in our framework,
would be categorized as tolerance by slow
growth85 or dose-dependent persistence86.
Finally, the survival of a bacterial population
under conditions that are designed to kill
may have far-reaching consequences for
the subsequent emergence of resistance.
For example, treatment with numerous
antibiotics has been shown to increase
the mutation rates of bacterial genomes; the
survival of bacterial populations by tolerance
may therefore constitute fertile ground for
the subsequent development of resistance
to the antibiotic. Understanding the bacterial
survival strategies operating in different
experimental systems should lead to a better
understanding of how pathogens evolve
resilience to treatment with antibiotics87,88.
Asher Brauner, Ofer Fridman, Orit Gefen and
Nathalie Q. Balaban are at the Racah Institute of
Physics and the Harvey M. Kruger Family Center for
Nanoscience and Nanotechnology, Edmond J. Safra
Campus, The Hebrew University of Jerusalem,
Jerusalem 91904, Israel.
Correspondence to N.Q.B.
[email protected]
doi:10.1038/nrmicro.2016.34
Published online 15 Apr 2016
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Acknowledgements
The authors thank N. Shoresh for illuminating discussions
regarding this manuscript, and the members of the Balaban
laboratory, I. Kaspy and G. Glaser for comments and suggestions. This work is supported by the Minerva Center for
Stochastic Decision Making in Microorganisms, a European
Research Council (ERC) Starting Grant (260871) and the
Israel Science Foundation (492/15).
Competing interests statement
The authors declare no competing interests.
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