Bacterial resistance tolerance persistence

Prévia do material em texto

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) 
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 
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 slowly10, 
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).
O P I N I O N
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.
320 | MAY 2016 | VOLUME 14 www.nature.com/nrmicro
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Nature Reviews | Microbiology
0 2 4 6 8 10
10–4
10–2
100
100
10–1
10–2
10–3
0 2 4 8 16 32 62 1280 2 4 8 16 32 62 128
0 2 4 8 16 32 62 128
0 2 4 8 16 32 62 128
0 2 4 8 16 32 62 128 0 2 4 8 16 32 62 128
μg ml–1
μg ml–1
μg ml–1
μg ml–1
μg ml–1
μg ml–1
MIC
MIC
MIC
MIC MIC
MIC
Susceptible versus persistent bacterial strainsSusceptible versus resistant bacterial strains Susceptible versus tolerant bacterial strains
Susceptible 
Resistant
MDK
99.99
MDK
99
MDK
99 MDK99.99MDK99
MDK
99
Fr
ac
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ac
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Time (hours)Time (hours)
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0 2 4 6 8
Susceptible 
Persistent
Persistent
Susceptible 
Susceptible 
Tolerant
Tolerant
Susceptible 
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 
note that mechanisms of bacterial resistance 
decrease the effectiveness of the antibiotic; 
that is, a higher concentration of the 
antibiotic is requiredto 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 
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 charac-
terization 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 
Figure 1 | Characteristic drug responses of resistance, tolerance and 
persistence. The survival strategies of resistance, tolerance and persis-
tence 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 
is similar to that of a susceptible strain; however, the minimum duration 
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. 
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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. 
resources, is selected for34. 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 thatoccurs 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 
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 
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Nature Reviews | Microbiology
M
D
K
99
 (h
ou
rs
)
C
FU
 m
l–1
10–1
101
100
100 101
104
106
108
Doubling time (hours)
a b
Time (hours)
1 in 100,000
1 in 100
1 in 100
Growing
Non-growing
0 2 4 6 8
Tolerance by lag
Tolerance by 
slow growth
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, 
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 itself47,49. 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 
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 
Figure 2 | Tolerance arises from slow growth or lag phase. a | The min-
imum duration for killing (MDK) for 99% of bacterialcells in a population 
(MDK99) is plotted against doubling time for several combinations of bac-
terial 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 bac-
teria 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 
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 pop-
ulation 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 
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, 
respectively14. 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 
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 day28. 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 
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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 
another64. The subpopulation with the longer 
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, 
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 
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 
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 
theresistome3, 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.
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Nature Reviews | Microbiology
Strain to 
characterize
MIC Resistant
MDK
99
Tolerant
MDK
99.99
Concentration
dependency
Time-dependent
persisters
Dose-dependent
persisters
Low
MDK
99.99
Low
MDK
99
High
MDK
99
High MDK 
in both
High MDK 
only in
stationary
High
MDK
99.99
Low
MIC
High
MIC
Susceptible
High 
dependency
Low
dependency
Tolerance (or persistence)
by slow growth
Tolerance (or 
persistence) by lag
Stationary
versus exponential
inoculum
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 
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 
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.
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 
subpopulations 
(unless they are 
highly enriched in 
the population) but 
high enough to 
capture the 
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 arepersistent, 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.
Finally, as with tolerant strains of bacteria, for strains that are 
shown to have time-dependent persistence, a further step is 
required to determine whether time-dependent persistence is due 
to a subpopulation with a long lag phase (persistence by lag; also 
known as ‘Type I’ persistence) or due to a 
subpopulation that has slow growth 
(persistence by slow growth; also known as 
‘Type II’ persistence). In this fifth step, the same 
test is used to distinguish between the two 
phenotypes as is used to distinguish between 
tolerance by lag and tolerance by slow growth 
(see above).
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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.
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 burgdorferi75. 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 characteri-
zation 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 hetero resistance 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.
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 classificationframework 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 growing2. 
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 
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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 
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.
in vivo in mouse models of infection, which 
demonstrated that dormant persisters are 
present in infections with S. Typhimurium31 
or M. tuberculosis42. 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 promoteras 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.
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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. 
nathalie.balaban@mail.huji.ac.il
doi:10.1038/nrmicro.2016.34
Published online 15 Apr 2016
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