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Reassessing the Role of Type II Toxin-Antitoxin Systems in
Formation of Escherichia coli Type II Persister Cells
Frédéric Goormaghtigh,
a
Nathan Fraikin,
a
Marta Putrinš,
b
Thibaut Hallaert,
a
Vasili Hauryliuk,
b,c,d
Abel Garcia-Pino,
a
Andreas Sjödin,
e,f
Sergo Kasvandik,
b
Klas Udekwu,
g
Tanel Tenson,
b
Niilo Kaldalu,
b
Laurence Van Melderen
a
a
Cellular and Molecular Microbiology (CM2), Faculté des Sciences, Université Libre de Bruxelles (ULB),
Gosselies, Belgium
b
Institute of Technology, University of Tartu, Tartu, Estonia
c
Department of Molecular Biology, Umeå University, Umeå, Sweden
d
Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, Umeå, Sweden
e
Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden
f
Department of Chemistry, Computational Life Science Cluster (CLiC), Umeå University, Umeå, Sweden
g
Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm,
Sweden
ABSTRACT Persistence is a reversible and low-frequency phenomenon allowing a
subpopulation of a clonal bacterial population to survive antibiotic treatments. Upon
removal of the antibiotic, persister cells resume growth and give rise to viable prog-
eny. Type II toxin-antitoxin (TA) systems were assumed to play a key role in the for-
mation of persister cells in Escherichia coli based on the observation that successive
deletions of TA systems decreased persistence frequency. In addition, the model
proposed that stochastic fluctuations of (p)ppGpp levels are the basis for triggering
activation of TA systems. Cells in which TA systems are activated are thought to en-
ter a dormancy state and therefore survive the antibiotic treatment. Using indepen-
dently constructed strains and newly designed fluorescent reporters, we reassessed
the roles of TA modules in persistence both at the population and single-cell levels.
Our data confirm that the deletion of 10 TA systems does not affect persistence to
ofloxacin or ampicillin. Moreover, microfluidic experiments performed with a strain
reporting the induction of the yefM-yoeB TA system allowed the observation of a
small number of type II persister cells that resume growth after removal of ampicil-
lin. However, we were unable to establish a correlation between high fluorescence
and persistence, since the fluorescence of persister cells was comparable to that of
the bulk of the population and none of the cells showing high fluorescence were
able to resume growth upon removal of the antibiotic. Altogether, these data show
that there is no direct link between induction of TA systems and persistence to anti-
biotics.
IMPORTANCE Within a growing bacterial population, a small subpopulation of cells
is able to survive antibiotic treatment by entering a transient state of dormancy re-
ferred to as persistence. Persistence is thought to be the cause of relapsing bacterial
infections and is a major public health concern. Type II toxin-antitoxin systems are
small modules composed of a toxic protein and an antitoxin protein counteracting
the toxin activity. These systems were thought to be pivotal players in persistence
until recent developments in the field. Our results demonstrate that previous influ-
ential reports had technical flaws and that there is no direct link between induction
of TA systems and persistence to antibiotics.
KEYWORDS RelE, YoeB, ampicillin, single cell
Received 21 March 2018 Accepted 15 May
2018 Published 12 June 2018
Citation Goormaghtigh F, Fraikin N, Putrinš
M, Hallaert T, Hauryliuk V, Garcia-Pino A,
Sjödin A, Kasvandik S, Udekwu K, Tenson T,
Kaldalu N, Van Melderen L. 2018. Reassessing
the role of type II toxin-antitoxin systems in
formation of Escherichia coli type II persister
cells. mBio 9:e00640-18. https://doi.org/10
.1128/mBio.00640-18.
Editor Gisela Storz, National Institute of Child
Health and Human Development (NICHD)
Copyright © 2018 Goormaghtigh et al. This is
an open-access article distributed under the
terms of the Creative Commons Attribution 4.0
International license.
Address correspondence to Niilo Kaldalu,
niilo.kaldalu@ut.ee, or Laurence Van Melderen,
lvmelder@ulb.ac.be.
F.G. and N.F. contributed equally to this work.
RESEARCH ARTICLE
crossm
May/June 2018 Volume 9 Issue 3 e00640-18 ®mbio.asm.org 1
Type II toxin-antitoxin (TA) systems are small operons encoding a toxic protein and
an antitoxin protein inhibiting the toxin activity by forming a tight complex (for
reviews, see references 1 to 6). The vast majority of toxins are protein synthesis
inhibitors using various molecular mechanisms to target different steps of translation
(7–11). Antitoxin proteins are labile and degraded by ATP-dependent proteases (i.e.,
Lon, ClpXP, and ClpAP) (12–14). The expression of TA systems is tightly regulated at the
transcriptional level (15–17). In steady-state conditions, the toxin-antitoxin complex
acts as a negative transcriptional regulator and binds palindromic sequences located in
the operon promoter. Under conditions in which the toxin level is higher than that of
the antitoxin, autoregulation is alleviated to restore excess antitoxin.
Type II TA systems are widespread and abundant in bacterial genomes (18–21). TA
systems might represent up to 3% of the total predicted open reading frames (ORFs)
in some genomes, with some genomes containing more than 90 TA systems. These
observations raise essential questions: why are there so many TA systems and what are
they for? These questions are mostly unanswered, and the role of chromosomally
encoded TA systems in bacterial physiology is highly debated in the field (22–24).
Type II TA systems were first discovered on plasmids in the mid-1980s. Their function
in that context is to eliminate daughter cells that did not receive a plasmid copy during
cell division and contribute to plasmid maintenance in growing populations (quoted as
addiction [25]). In chromosomes, TA systems are mostly part of the accessory genome
originating from horizontal gene transfer (20, 26). They are detected on prophages,
transposons, and other genomic islands. Their role in such integrated elements is
reminiscent of the addiction function (27–29). Other systems are involved in protection
against mobile genetic elements such as plasmids (antiaddiction function) or phages
(abortive infection) (30, 31). On the basis of their general action on bacterial growth, it
was hypothesized that chromosomal TA systems could be integrated in the host
regulatory networks and involved in stress management. However, an Escherichia coli
strain lacking five TA systems (relBE,yefM-yoeB,mazEF,chpB, and dinJ-yafQ) for which
the toxins are endoribonucleases, had no survival defects in stress conditions (32). In
addition, this strain did not show any fitness disadvantage in competition experiments
with the wild-type strain. These observations questioned the role of TA systems in stress
management. A recent model proposed a direct connection between TA systems and
persistence to antibiotics in vitro. This model became an instant hit in the field,
influencing the research on TA modules and persistence for the last years (33, 34).
Persistence is defined as a stochastic switch that pushes bacterial physiology toward an
increased antibiotic-tolerant state (35–38). The low frequency (10
⫺2
to 10
⫺6
depending
on the bacterial species, strains, experimental conditions, and antibiotics) combined
with the transient nature of persister cells makes them very challenging to study. As a
result, the molecular mechanisms underlying persistence remain largely unclear (39).
The model linking TA modules and persistence initially stemmed from observations
made by the K. Gerdes lab that successive deletions of 10 type II TA systems (later
referred to in the field as the Δ10 strain) progressively decreased the level of persis-
tence to antibiotics (33). Deletion of the gene encoding the Lon protease, thought to
mediate degradation of different antitoxins, had a similar effect. While this model
gained wide acceptance, several independent follow-up studies questioned its validity
(40–43). Nevertheless, the model was further refined in a follow-up work that focused
on the link between TA system activation and persistence at the single-cell level. The
authors reported that stochastic accumulation of (p)ppGpp was the trigger for degra-
dation of antitoxins resulting in activation of TA systems (34). In this work, TA activation
was monitored using transcriptional fusions of the yefM-yoeB and relBE TA operons to
gfp. The intracellular concentration of the (p)ppGpp alarmone was monitored using a
translational fusion between the stationary-phase sigma factor RpoS and mCherry as
proxy. Using these reporters, the authors observed that rare nongrowing fluorescent
cells within the bulk population of nonfluorescent cells were tolerant to high doses of
ampicillin. In some cases, fluorescent cells were able to resume growth after ampicillin
treatment. On the basis of these data, they proposed that accumulation of (p)ppGpp
Goormaghtigh et al. ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 2
inhibits polyphosphatase (encoded by the ppx gene), leading to the accumulation of
polyphosphate (PolyP). In turn, PolyP binds to Lon and stimulates antitoxin degrada-
tion, thereby liberating the toxins from the TA complexes. The resulting free toxins
would then inhibit translation and induce persistence. The K. Gerdes lab subsequently
proposed that the HipA toxin from the type II hipBA system induces persistence
through the activation of the 10 TA systems, reinforcing their role as major effectors of
bacterial persistence (44).
In a major paradigm shift, the authors of the model discovered that the reference
Δ10 strain on which the aforementioned work was performed was severely compro-
mised by infection of
80 prophages. In their revision, they attributed the observed loss
of persistence to these phage infections and disentangled TA systems from persistence
(45), leading to the retraction of the two previous papers (46, 47).
Although the notion of a defective Δ10 strain de facto shatters the model, there are
additional issues that were not addressed in the revision (45). Given how influential this
model has been over the last years, clarifying all these issues remains paramount. It
remains unclear how the phage contamination problems would affect the validity of
some aspects of the original model, notably the stochastic activation of TA systems in
type II persister cells, since these experiments were performed only in the noninfected
wild-type strain (34). The same comment holds for the model in which the HipA toxin
induces persistence via the activation of the 10 TA systems (44). In this context, we
reassessed the roles of type II TA systems by using an independently constructed Δ10
mutant and by testing the fluorescent reporters described in the aforementioned
studies. Our results showed that the previously used methodologies have several
drawbacks that led to misinterpretation of the results. Besides the highly mutated Δ10
strain, we show that the fluorescent reporters that were used failed to report TA system
activation and (p)ppGpp levels. We therefore designed a new fluorescent reporter that
monitors induction of the yefM-yoeB system at the single-cell level using microfluidic
chips coupled with fluorescence microscopy. Interestingly, a small number of type II
persister cells were observed; however, fluorescence of these cells was comparable to
that of the bulk of the population, confirming that there is no direct link between
induction of TA systems and persistence to ampicillin.
RESULTS
Deletion of 10 TA systems does not affect persistence to antibiotics. In parallel
to the work performed in the K. Gerdes lab, we constructed a strain with the same 10
TA systems deleted (Δ10LVM) (48). However, the two strains are different in some key
aspects. First, the methods used to delete the last five TA operons (see Materials and
Methods) in the respective Δ5 strains were different: while we used the
Red method
combined with the FLP-FLP recombination target (FRT) recombinase system to remove
resistance cassettes from successive deletion mutants (49), the Δ10KG strain was
constructed using a counterselection system based on the expression of the type II
ParE toxin (33). ParE-based counterselection allowed for scarless deletions but inevita-
bly increased the risk of mutations and rearrangements, since ParE is a DNA gyrase
inhibitor (50), which induces DNA double-strand breaks and SOS response (51). Second,
while the entire mazEF operon is deleted in our strains, only the mazF toxic gene is
deleted in the strains from the Gerdes lab, allowing the possible expression of the
antitoxin mazE as well as mazG, the third gene of the mazEFG operon.
Persistence was measured for both Δ10 strains during5hoftreatment with
ampicillin (100
g/ml) or ofloxacin (5
g/ml) in steady-state cultures in a chemically
defined medium as described in reference 52. The time-kill curves of the different
strains have a typical biphasic shape, indicative of a small subpopulation of type II
persister cells (see Fig. S1 in the supplemental material). We did not observe any effect
on persister formation in the Δ10LVM mutant after either ampicillin or ofloxacin
treatment (Fig. 1A). These observations are consistent with a recent correction pub-
lished by the K. Gerdes lab in which the authors found no effect on persister formation
in both ampicillin and ciprofloxacin, in a newly constructed Δ10=TA strain (45). However
Revisiting the Link between TA Systems and Persistence ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 3
and as initially reported (33), using the Δ10KG mutant, we observed a 1,000-fold drop
in survival to ofloxacin in the Δ10KG mutant. Survival of the Δ10KG mutant to ampicillin
was comparable to that of the wild-type and Δ10LVM strains (Fig. 1A). This discrepancy
was lately recognized by the authors, as they observed that the difference in persis-
tence to ampicillin between the Δ10 mutant and the wild-type strain could not be
reproduced in better defined experimental conditions when chemically defined growth
medium was used (45). This supports our assertion that proper and defined experi-
mental conditions are of major importance when performing persistence assays (52).
Persistence of these strains was further confirmed by measuring the minimal
duration for killing of 99.9% of the population (MDK
99.9
), an accurate parameter to
assess survival to antibiotics (38)(Fig. 1B). While 99.9% of the wild-type and Δ10LVM
populations were killed by ofloxacin treatment in more than 72 min, this time was
drastically reduced to 9 min for the Δ10KG strain. Ampicillin treatment yielded MDK
99.9
values ranging from 85.2 min for the wild-type strain to 92.4 min for the Δ10KG strain
(Fig. 1B). To conclude, the results obtained with an independently constructed Δ10
strain do not support a role for TA systems in persistence and confirms that the earlier
report based on the Δ10KG strain is an experimental artifact (45).
Whole-genome and proteomic analysis of the ⌬10LVM and ⌬10KG strains.
Whole-genome sequencing was performed on the Δ10KG and Δ10LVM strains, as well
as intermediate deletion strains used to construct the Δ10KG strain (Δ5KG, Δ7KG, Δ8KG,
and Δ9KG) to help retrace the history of phage infections (Fig. 1C; see Table S1 in the
supplemental material). Our analysis confirms that the Δ10KG strain genome is largely
rearranged (42, 45). In agreement with the Gerdes lab (45), we found that the Δ5KG
strain contains an insertion of a
prophage at the attB site and a
80 prophage located
at the canonical integration site, between yciI and kch (
80-1).
These two phages are detected in all subsequent deletion strains. In addition to
these two phages, the Δ7KG and subsequent deletion strains contain another
80
prophage (
80-2) located between glgS and ygiJ. Finally, the Δ10KG strain contains a
third
80 prophage (
80-3) located between yeeJ and yeeL. The presence of the three
Δ10KG
yafNO 10kbp
A B
C
-8
-6
-4
-2
0
log 5 h survival
0
0.4
0.8
1.2
1.6
MDK 99,9 (h)
-8
-6
-4
-2
0
h5retfaetarlavivrusgoL
0
0.4
0.8
1.2
1.6
2
MDK 99.9 (h)
MG1655
dinJ-yafQ
hicAB
relBE
yefM-yoeB
mazEF
higBA
prlF
-yhaV
mqsRA
chpB
oriC
Δ10LVM
oriC
φ80-1
oriC
φ80-3
λ( )
φ80-2
Ampicillin Ofloxacin Ampicillin Ofloxacin
FIG 1 Deletion of 10 type II TA systems has no effect on type II persister cell formation. (A) Surviving
fraction of bacteria after5hofampicillin (100
g/ml) (left) or ofloxacin (5
g/ml) (right) treatment. Values
are the means from at least 3 independent experiments. Error bars indicate standard deviations. (B)
Minimum duration for killing (MDK) 99.9% of the population during ampicillin (100
g/ml) (left) or
ofloxacin (5
g/ml) (right) exposure. Values are the means from at least three independent experiments.
Error bars indicate standard deviations. (C) Genome maps of the E.coli MG1655, Δ10KG, and Δ10LVM
strains. Deleted TA loci, phage insertions, and large deletions are annotated in gray, blue, and green,
respectively. Colored arrows represent intergenic regions between TA modules in the forward direction.
Chromosomal inversions and rearrangements in strain Δ10LVM are represented by dashed lines and
arrows, respectively.
Goormaghtigh et al. ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 4
lysogenic
80 phages in the Δ10KG strain was further confirmed by PCR using specific
primers (Fig. S2A). Polylysogen formation by
80 at these noncanonical sites was
previously reported in another context (53). In their recent correction, the K. Gerdes
group failed to detect the
80-2 and
80-3 phages but identified a
80-
hybrid
lysogenic phage (45) that we failed to detect. Our data indicate that these phages were
progressively acquired during the successive TA deletions, which could be responsible
for the progressive drop of persistence observed by the authors during these succes-
sive deletions (33). However, we found Δ7KG, Δ8KG, and Δ9KG strains to be genetically
identical aside from TA deletions, while the authors showed a progressive drop of
survival from the Δ7KG strain to the Δ9KG strain upon antibiotic treatment (33). We
thus checked whether ofloxacin treatment induces prophage-dependent lysis of the
Δ10KG strain by monitoring turbidity during treatment. We did not observe a drop in
turbidity in the Δ10KG culture, suggesting that, despite the 1,000-fold decrease in
survival, massive phage-dependent lysis did not occur (Fig. S2).
The Δ10KG strain also contains a 10-kbp deletion encompassing 10 genes. In
addition, the Δ5KG strain and its derivatives seem to contain numerous mutations in
three of the MG1655 cryptic prophages (DPL12, Rac, and Qin/Kim) as shown by Shan
et al. (42). However, reads containing these mutations can also be matched to
80,
suggesting that these polymorphisms might be assembly artifacts due to the presence
of
80 prophages in the Δ10KG strain.
The Δ10LVM strain is devoid of any contaminant prophages (as well as the Δ5LVM
strain; data not shown) but shows large chromosomal inversions most likely due to
the presence of multiple FRT scars at the deletion sites, allowing for FLP-dependent
site-specific and/or homologous recombination between these loci (Fig. 1C). Neverthe-
less, these rearrangements neither affect growth or sensitivity nor persistence to
ampicillin or ofloxacin treatments (Fig. 1A and B and Fig. S1).
We performed label-free quantification mass spectrometry (LFQ-MS) of whole-cell
proteomes to compare the Δ10 strains to the wild-type strain (Table S2). In agreement
with genomic data, GltI, GltL, and RihA are not detected in the Δ10KG strain, which is
deleted for 10 kbp encompassing these genes. The TabA protein level was decreased
in the Δ10LVM strain, probably due to a single nucleotide polymorphism (SNP) located
upstream of the tabA ORF (Table S1). Proteomic analysis also revealed differences in
expression of MazG. As mentioned above, in the Δ10KG strain, only mazF was deleted,
leading to a derepression of the mazEFG operon and to higher levels of MazE and MazG
(10- and 64-fold, respectively). In the Δ10LVM strain, as expected, MazE and MazG are
not detected. It is noteworthy that overexpression of MazG, a nonspecific nucleotide
triphosphate pyrophosphohydrolase, has been reported to inhibit growth, prevent
(p)ppGpp accumulation, and therefore reduce survival to various stresses (54). How-
ever, the persistence rate of a single mutant deleted only for mazF is comparable to
that of the wild-type strain (33), indicating that overproduction of MazG alone is not
responsible for the persistence defect.
Expression of the rpoS-mcherry translational fusion is likely to report carryover
cells from stationary phase in exponentially growing cultures. Maisonneuve et al.
hypothesized that stochastic synthesis of (p)ppGpp was responsible for toxin activation
and growth arrest, therefore contributing to persister formation in exponentially grow-
ing cultures (34). In order to test this, they used an RpoS-mCherry translational fusion
as a proxy for (p)ppGpp concentration at the single-cell level (34, 44). The authors
observed rare fluorescent cells that were persistent to ampicillin, i.e., cells that did not
lyse in the presence of ampicillin and were able to resume growth after treatment. They
concluded that stochastic induction of (p)ppGpp synthesis leads to persistence to
ampicillin. However, the use of an rpoS fusion to report (p)ppGpp may be problematic.
Regulation of rpoS occurs at multiple levels (transcription, translation, degradation, and
activity) and involves many regulators besides (p)ppGpp (cAMP, small RNAs, RssB
adaptor, ClpXP protease, antiadaptors) (55). Moreover, while (p)ppGpp is involved in
basal regulation of rpoS expression, it does not appear to play a major role in rpoS
Revisiting the Link between TA Systems and Persistence ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 5
expression in stationary phase. Strains devoid of (p)ppGpp show full induction of rpoS
in stationary phase but with a 2- to 3-h delay compared to the wild-type strain (56).
To test the validity of the reporter, we transformed the rpoS-mcherry reporter strain
constructed by the Gerdes group with a plasmid (pETgfpmut2) carrying a gfp reporter
under control of the inducible ptac promoter to monitor proliferation of individual cells
(57)(Fig. 2A). Bacteria were grown to stationary phase with isopropyl-

-D-thiogalacto-
pyranoside (IPTG) to induce gfp expression, washed, diluted in fresh medium without
IPTG, and grown for 150 min to mid-exponential phase, allowing green fluorescent
protein (GFP) to be diluted by successive divisions. As expected, most stationary-phase
cells displayed both green and red fluorescence. After dilution and growth to expo-
nential phase, both the GFP and RpoS-mCherry fluorescence dropped in the majority of
the cells (Fig. 2B). However, some cells (2.38% of the population) retained high red
fluorescence concomitantly with high GFP signal, indicating that these cells are carry-
overs from stationary phase. A small proportion of RpoS-mCherry-positive cells showed
no GFP fluorescence (0.20% of the population), indicating that in these cells, rpoS might
indeed be induced stochastically. Examination of the RpoS-mCherry-positive cells by
microscopy showed that, in some cells, the fusion protein was distributed uniformly
(Fig. 2A), similarly to the previously published microscopic images (34, 44). However, in
many cells, the red fluorescence was localized in dense bodies at the cell poles (Fig. 2A
and Fig. S3), which is typical of inclusion bodies and aggregates of misfolded proteins
(58). Formation of inclusion bodies suggests that the fusion protein is prone to
aggregation and might not be symmetrically distributed during divisions, as previously
A
BStaonary phase Growing culture
0.4 0.8 1.2 1.6 0.4 0.8 1.2 1.6
0.5
0.4
0.3
0.2
0.1
Log GFP fluorescence (AU)
Log RpoS- yrrehC
m)UA(ecnecseroulf
0.204 % 2.38 %
90.0 % 7.41 %
0.611 % 98.6 %
0.321 % 0.460 %
Phase GFP RpoS-mCherry Merge
Staonary phase
(+IPTG) 0 min
Growing culture
(-IPTG) 150 min
FIG 2 RpoS-mCherry reports nongrowing cells in exponentially growing cultures. (A) Illustration of the
GFP dilution system used (57). E.coli MG1655 rpoS-mcherry cells transformed with pETgfpmut2 were
grown to stationary phase with IPTG to induce gfp expression, washed, diluted in fresh LB medium
without IPTG, and grown for 150 min. GFP will be diluted in dividing cells while it will be retained in
nongrowing cells. Fluorescence microscopy images of both stationary-phase and exponential-phase cells
are shown. (B) Fluorescence microscopy population analysis of cells prepared as in panel A. A total of
35,185 (stationary phase; left) and 29,469 (exponential phase; right) cells from two independent repli-
cates were identified by CellProfiler. Log median red and green fluorescence values for each cell were
measured and plotted. Fluorescence is shown in arbitrary units (AU). The percentage of cells in each
quadrant is indicated.
Goormaghtigh et al. ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 6
described for mCherry fusions (59). Thus, the cells with polar RpoS-mCherry signal and
low GFP signal in both stationary-phase and growing cultures might be dead or dying
cells that have leaked out the soluble GFP but retained the aggregated polar RpoS-
mCherry, which accumulated during stationary phase. The inclusion bodies of RpoS-
mCherry formed as well when the cells did not contain the gfp reporter plasmid
(Fig. S3). In addition, we checked that stationary-phase cells of the reporter-free control
have no red autofluorescence, showing that the red fluorescence is indeed caused by
the production of RpoS-mCherry (Fig. S3).
Altogether, these results indicate that the rpoS-mcherry fusion is an inadequate
reporter to study formation of persister cells in exponentially growing cultures, as it
might report carryover cells from a previous stationary phase instead of stochastic
switching to a nongrowing state due to (p)ppGpp fluctuations. However, these results
do not rule out a potential role of (p)ppGpp in persister formation. An important step
toward answering such question would be the design of a sensitive and specific
(p)ppGpp reporter, which to our knowledge, is still missing in the field.
TA::gfp transcriptional fusions do not report stochastic activation of toxin-
antitoxin transcription. Stochastic activation of TA modules in type II persister cells
became the cornerstone of the model linking the rise in (p)ppGpp levels with the
activation of toxins. To test this hypothesis, Maisonneuve et al. monitored the induction
of the yefM-yoeB and relBE TA systems at the single-cell level using transcriptional
reporters (34). In their design, the gfp gene was inserted downstream of the toxin genes
at the TA loci. Green fluorescence was monitored either in microfluidic time-lapse
microscopy experiments or in liquid cultures by taking microscopy snapshots. In both
setups, the authors found a few cells displaying higher green fluorescence than the
bulk of the population. However, the original experiment lacked a necessary control, as
the authors did not compare the fluorescence of these strains to that of cells without
fluorescent constructs (34). Using the same conditions, we compared the strains
carrying the TA::gfp fusions to the wild-type strain lacking the gfp gene (Fig. 3). We were
able to detect green fluorescence heterogeneity with confocal microscopy in the
TA::gfp-carrying strains, with some cells being more fluorescent than the bulk of the
population. However, we were also able to detect rare fluorescent events that stood out
from the rest of the population in the control strain (Fig. 3A). Our results actually show
that the fluorescence of these reporter strains is similar to that of a control strain devoid
of the reporter constructs. Flow cytometry further revealed that fluorescence distribu-
tions are unimodal and similar for the wild-type strain and for both yoeB::gfp and
relE::gfp reporter strains (Fig. 3B). Moreover, we measured fluorescence using excitation
wavelength of 488 nm and recording emission at wavelengths of 530/30 nm and
575/26 nm for the wild-type strain expressing GFP or not expressing GFP and the
TA::gfp strains (data not shown). For the strain expressing GFP, the 530:575 nm ratio is
around sixfold. However, the 530:575 nm ratio of the TA::gfp strains is comparable to
that of the wild-type strain, suggesting that the GFP signal of these reporters is weak
and masked by autofluorescence in the whole population. The higher autofluorescence
of some cells (Fig. 3) may be linked to oxidative stress that has been shown to increase
bacterial autofluorescence caused by oxidized forms of riboflavin and flavin nucleotides
such as FAD and FMN (60). More severe oxidative damage experienced by some
bacteria could explain their nongrowing condition and the nonlysing state during the
ampicillin treatment. Interestingly, the nongrowing cells, which had retained RpoS-
mCherry at the cell poles, also had a high level of green autofluorescence (Fig. S3).
Altogether, these data show that yoeB::gfp and relE::gfp reporters do not report expres-
sion of the relBE and yefM-yoeB systems.
Type II persister cells do not show higher levels of pyefM-yoeB fluorescence
than the bulk of the population. We thus sought to design more sensitive reporters
for TA transcriptional activity. Using a single-copy plasmid, the relBE and yefM-yoeB
promoters were cloned upstream of the mScarlet-I gene encoding a bright red fluo-
rescent protein (61). Fluorescence of exponentially growing cells containing the prelBE
and pyefM-yoeB fusions was analyzed by flow cytometry in the wild-type strain and in
Revisiting the Link between TA Systems and Persistence ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 7
the corresponding TA-deleted strains and compared to the wild-type strain containing
a promoterless vector as a control. Fluorescence of the wild-type cells containing the
prelBE reporter is comparable to that of the control (Fig. 4A) with a normalized mean
fluorescence of 2 arbitrary units (AU) (Fig. 4B). In the ΔrelBE mutant, as expected,
derepression of the system leads to an 11-fold increase in fluorescence (22 AU [Fig. 4A
and B]). For the pyefM-yoeB reporter, fluorescence of the wild-type cells is substantially
higher than that observed for the relBE promoter (138 AU), and a fourfold increase in
fluorescence is observed in the ΔyefM-yoeB mutant as a result of promoter derepression
(576 AU [Fig. 4A and B]). For both fluorescent reporters, a small subpopulation of highly
fluorescent cells is observed, while none was detected with the promoterless fusion,
indicating that high fluorescence is specific to these promoters (Fig. 4A and Fig. S4).
However, the nature of these cells is still uncertain but does not appear to rely only on
TA autoregulation.
Since the pyefM-yoeB::mScarlet-I fusion shows detectable fluorescence levels in the
wild-type cells, we chose to perform time-lapse fluorescence microscopy in a micro-
1
2
3
4
5
6
A
B
TL 488nm
MG1655
relE::gfp
yoeB::gfp
MG1655 relE::gfp yoeB::gfp
8
8
4 xE goLEm )
U
A
(
0
3/
035
Ex 488 Em 493-575
FIG 3 Fluorescence analysis of TA::gfp reporters. (A) Confocal microscopy of E.coli MG1655 and its
derivatives containing yoeB::gfp and relE::gfp grown to exponential phase. The white arrows show cells
with above-average fluorescence levels. TL, transmitted light; Ex, excitation; Em, emission. (B) Flow
cytometry analysis of strain MG1655 in comparison with the yoeB::gfp and relE::gfp reporter strains grown
to exponential phase. Analyses were performed on 1,000,000 events. Three independent biological
experiments were performed, and a representative example is displayed for each strain.
Goormaghtigh et al. ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 8
fluidic chamber with cells containing this reporter. Among the 2.7 ⫻10
5
cells that were
analyzed, we could detect 11 type II persister cells (0.0041%) that regrew within 16 h
after antibiotic removal (Fig. 4C; see the top panels in Movie S1 in the supplemental
material). As far as we know, this is the first direct observation of type II persister cells
in wild-type E. coli cells. None of these persister cells showed a fluorescence level above
the population average at treatment time (Fig. 4D, black circles). A few highly fluores-
cent cells (0.012%) were detected and monitored during ampicillin treatment. About
half of them (47%) did not lyse but were unable to resume growth after removal of the
antibiotic, even 16 h after the end of the treatment (Movie S1, middle panels). Most of
these cells showed a significant loss of contrast 16 h after ampicillin removal, indicating
damage. The other half lysed during the ampicillin treatment (Movie S1, bottom
panels). Altogether, these data show that the type II persister cells we observed did not
show a high level of pyefM-yoeB fluorescence, underscoring that induction of the
yefM-yoeB system is not implicated in the generation of persister cells in steady-state
growth conditions.
A
DB
C
1
2
3
4
5
6
0
100
200
300
400
500
600
700
0
20
40
60
80
100
120
2.2 2.3 2.4 2.5
Log pyefM-yoeB::mSc fluorescence
before treatment (AU)
Cell Count (n=512)
1 h 3 h 7 h 10 h 12 h 24 h
htworgeRnillicipmAhtworG
165xEgoLEm )UA(51/026
MG1655 MG1655 ΔrelBE MG1655 ΔyefM-yoeB
pmSc prelBE::mSc prelBE::mSc pyefM-yoeB::mSc pyefM-yoeB::mSc
MG1655 ΔrelBE MG1655 ΔyefM-yoeB
prelBE prelBE pyefM-yoeB pyefM-yoeB
222
138
575
165
x
EEm )UA(51/026
pyefM-yoeB::mSc
FIG 4 Stochastic expression of yefM-yoeB does not lead to persistence. (A) Flow cytometry analysis of
cells carrying prelBE::mSc and pyefM-yoeB::mSc reporters grown to exponential phase. Three independent
biological experiments were performed counting 1,000,000 events, and a representative example is
displayed for each strain. (B) Population analysis of prelBE::mSc and pyefM-yoeB::mSc expression. Mean
population fluorescence values from panel A were corrected for background fluorescence using the
mean value of the pmSc construct. Error bars represent standard deviations. (C) Time-lapse microscopy
of type II persister cells transformed with the pyefM-yoeB::mSc plasmid. Stationary-phase cells were
grown for 3 h perfused in MOPS medium, challenged with ampicillin (100
g/ml) for 5 h, and regrown
for 16 h with fresh medium. (D) Population analysis of pyefM-yoeB::mSc fluorescence before treatment
from Movie S1 in the supplemental material. Fluorescence was measured for 512 nonpersister cells and
11 persister cells. Persisters are plotted above their respective bins as individual black dots.
Revisiting the Link between TA Systems and Persistence ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 9
DISCUSSION
The biological role of chromosomally encoded type II TA systems has been exten-
sively debated during the last 25 years. The model linking TA systems and persistence
to antibiotics had a major impact in the microbiology community as a whole. Recently,
this model was invalidated (46, 47), reopening the question of the role of these
widespread systems.
In this work, we provide a series of population and single-cell complementary
evidence that further debunk the persistence model previously proposed by the Gerdes
group (33, 34, 44). We show here that a newly constructed strain lacking 10 TA systems
(Δ10LVM) behaves as the wild-type strain and displays similar persistence levels with
ampicillin and ofloxacin. Although this strain contains large genomic inversions, the
growth rate, MIC, and persistence rate are comparable to those observed for the
wild-type strain. We also constructed transcriptional fusions coupling the promoter of
the relBE and yefM-yoeB systems to the mScarlet-I fluorescent reporter. Fluorescence
analysis by flow cytometry showed that the activity of TA promoters is quite low in the
wild-type strain, especially in the case of the relBE system, as expected due to auto-
regulation. In the corresponding TA-deleted strain, an increase in fluorescence was
observed, therefore validating the constructs.
We used the yefM-yoeB::mScarlet reporter to monitor TA system induction at the
single-cell level using a microfluidic system coupled to fluorescence microscopy. Inter-
estingly, we observed that type II persister cells, those that are able to generate viable
progeny after the removal of the antibiotics, did not show high levels of fluorescence.
Thus, our work shows that there is no link or role for the induction of the yefM-yoeB
system in the formation of E. coli persister cells during steady-state growth conditions.
Therefore, the direct outcome of our work reopens a fundamental question involv-
ing TA systems: what is the benefit of having so many systems for bacteria? Another
important question concerns the redundancy of these TA systems. The persistence
model originally arose from the observation that successive deletions of type II TA
systems progressively led to a decrease of persistence to both ciprofloxacin and
ampicillin. This phenotype was not attributable to any specific systems and led to the
erroneous conclusion that TA systems are redundant and have a cumulative effect.
Knowing that TA systems are part of the mobilome and are highly variable from one
isolate to the other, it appears quite unlikely that they all contribute to a common
phenotype. Given the diversity of these systems, their functions might vary depending
on their genomic locations, the type of toxin activity, and their bacterial host. One
might also consider that they are “just” selfish elements that propagate within bacterial
genomes at the expense of their host (22–24).
Several publications implicate (p)ppGpp and type II TA in type II persister formation
(11, 62, 63). However, constraining the quite complex phenomenon of antibiotic
persistence to a single molecular mechanism or a single genetic cascade is extremely
reductive (39). Other (p)ppGpp-independent mechanisms of persister formation impli-
cating factors such as efflux pumps (64), the tisAB type I TA system (65), or the
concentration of ATP (42) have also been reported. A direct correlation between type
II persister cells, (p)ppGpp, and induction of TA systems was considered an alluring
prospect driving the field for many years. This assumption was extrapolated from the
E. coli and the Δ10KG context and used as the template for research in other bacteria
and TA systems. It also gave rise to multiple theoretical models that attempted to
simulate and drive conclusions regarding persister cells based on these misguided
experimental observations (66–68). Consequently, it remains of paramount importance
that such works are reexamined in the light of our results and the current state of
the art.
MATERIALS AND METHODS
Bacterial strains and plasmids. Bacterial strains and plasmids used in this study are listed in
Table S3 in the supplemental material. The Δ10LVM strain was previously constructed from strain
LVM100 (Δ5LVM) (48). mScarlet reporter plasmids were constructed by cloning TA promoters (200 bp
upstream of ATG) between AvrII and NsiI sites in pNF02, a derivative of the single-copy plasmid
Goormaghtigh et al. ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 10
pBeloBAC11. Primers used for the construction of the TA reporters are listed in Table S4. The pNF02
plasmid encodes a codon-optimized mScarlet-I transcriptionally insulated by a lambda T1 terminator in
5=end and a T7 terminator doubled with a two-way LuxIA terminator in 3=end.
Media and growth conditions. Experiments testing rpoS-mCherry,relBE::gfp, and yefM-yoeB::gfp
expression were performed in autoclaved LB to reproduce experimental conditions described in refer-
ence 34. All the other experiments were performed in morpholinepropanesulfonic acid (MOPS)-based
medium prepared as described in reference 69, supplemented with 0.4% glucose.
Persistence assays. Persistence was essentially assayed as described previously (52) with increased
sampling frequency. Sampling was performed every 10 min from time zero to 200 min and every 20 min
from 200 min to 300 min. Ofloxacin was used at 5
g/ml, corresponding to 56-fold the MIC for E.coli
MG1655. Ampicillin was used at 100
g/ml, corresponding to 18-fold the MIC for MG1655. The frequency
of persistence is the ratio of the number of colonies at a given time to the number of colonies at
treatment time. The minimal duration for killing (MDK) was determined by log linear extrapolation
between the two time points bordering 10
⫺3
of survival rate to precisely evaluate the minimal time
required to eliminate 99.9% of the cells (MDK
99.9
) (see Fig. S1D and E in the supplemental material).
Whole-genome sequencing. Genomic DNA was extracted from 2-ml overnight LB cultures using a
DNeasy Power Soil extraction kit (Qiagen, Valencia, CA, USA) according to manufacturer’s protocol. The
extract was then purified with Agencourt AMPure XP magnetic beads (Beckman Coulter, Beverly, MA,
USA) and quantified using the Quantifluor double-stranded DNA (dsDNA) system (Promega, Madison, WI,
USA). We sequenced 6 pM genomic DNA (gDNA) on an Illumina MiSeq instrument using the Nextera
library preparation protocol and the MiSeq reagent kit v3 (Illumina, San Diego, CA, USA), spiking the flow
cell with 1% phiX DNA. Quality of generated paired-end reads were assessed with FastQC and de novo
assembled using Abyss (70), and obtained contigs were polished using Pilon (71) by mapping reads back
to contigs using BWA (72). Generated genome sequences were aligned versus each other using
progressiveMauve (73), and structural variants were visualized with genomeRing (74). Single nucleotide
polymorphism (SNP) differences were characterized using snippy and dnadiff (75). PHASTER (PHAge
Search Tool Enhanced Release) (76) was used to search for potential phage insert in the genome
assembly. All genomic analyses were performed using Snakemake (77) as workflow manager together
with software installations from Bioconda (78). Reads and assemblies for Δ10KG and Δ10LVM have been
deposited in the NCBI BioProject Repository (PRJNA454100).
Analysis of rpoS-mCherry expression. Cultures of E.coli MG1655 and MG1655 rpoS-mcherry
containing the pETgfpmut2 plasmid were grown overnight in LB medium supplemented with chloram-
phenicol (25
g/ml) and 1 mM IPTG to induce gfp expression. Bacteria were harvested by centrifugation,
washed twice with phosphate-buffered saline (PBS) to remove the traces of IPTG, diluted 100-fold in
10 ml of fresh IPTG-free LB, and incubated at 37°C with shaking in a 100-ml flask. Bacteria were sampled
immediately after the dilution and during growth, spotted onto agar pads and imaged with a Zeiss Axio
Observer.Z1 microscope equipped with a 63⫻objective, AuroxCam camera, and filter set 61 HE (Colibri).
Cells were detected from phase-contrast images, and the median values of red (mCherry) and green
(GFP) fluorescence of each bacterium were measured using Cell Profiler software.
Fluorescence analysis of chromosomally encoded relE::gfp and yoeB::gfp fusions. Overnight
cultures grown in LB medium were diluted 100-fold in 10 ml of LB and incubated at 37°C with shaking
in a 100-ml flask. Bacteria were sampled immediately after inoculation and during growth and analyzed
using an LSR II (BD Biosystems) flow cytometer equipped with a 488-nm laser, a 530/30 nm filter, and a
575/26 nm filter. Microscopic images of bacteria were acquired using a confocal fluorescence microscope
(LSM710; Zeiss). A 488-nm laser and a 493-to-575-nm emission window were used for detection of green
fluorescence.
Single-cell analysis of yefM-yoeB::mSc expression. Overnight cultures grown in MOPS medium
containing glucose (0.4%) supplemented with Casamino Acids (0.2%) (Difco) (vitamin free) and sodium
bicarbonate (10 mM) were diluted to an optical density at 600 nm (OD
600
) of 0.02, grown3hto
exponential phase (OD
600
of ~0.3), and diluted in PBS before injection into an Attune NXT flow cytometer.
10
6
events per experiment were analyzed with a 561-nm laser and a 620/15 emission filter. The same
overnight cultures were diluted 50 times in PBS and loaded into a B04A CellASIC ONIX plate. Trapped
cells were perfused for3hinMOPS medium under 1 lb/in
2
, challenged with the same medium
supplemented with ampicillin for 5 h, and regrown with fresh medium for 16 h. Images were taken every
15 min using a Zeiss Axiobserver.Z1 microscope equipped with an ORCA-Flash 4.0 complementary metal
oxide semiconductor (CMOS) camera and filter set 00.
Analysis of fluorescence was performed using the MicrobeJ suite for ImageJ.
SUPPLEMENTAL MATERIAL
Supplemental material for this article may be found at https://doi.org/10.1128/mBio
.00640-18.
TEXT S1, PDF file, 0.3 MB.
FIG S1, PDF file, 0.4 MB.
FIG S2, PDF file, 0.4 MB.
FIG S3, PDF file, 0.3 MB.
FIG S4, PDF file, 0.4 MB.
TABLE S1, PDF file, 0.4 MB.
TABLE S2, PDF file, 0.5 MB.
Revisiting the Link between TA Systems and Persistence ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 11
TABLE S3, PDF file, 0.4 MB.
TABLE S4, PDF file, 0.2 MB.
MOVIE S1, AVI file, 18.9 MB.
ACKNOWLEDGMENTS
We are grateful to Natacha Mine, Mariliis Hinnu, Spyridon Gkotzis, and Bertrand
Delahaye for technical support and Philippe Goffin for providing plasmids. We thank
Etienne Maisonneuve and Kenn Gerdes for donating strains. We thank the reviewers for
their constructive comments.
This work is supported by the Fonds National de la Recherche Scientifique (FNRS)
(T.0147.15F PDR and J.0061.16F CDR to L.V.M.) and FRFS-WELBIO grant (CR-2017S-03,
T.0066.18 PDR and F.4505.16 MIS to A.G.-P.), the Fonds d’Encouragement à la Recherche
ULB (FER-ULB) (to A.G.-P. and L.V.M.), the Interuniversity Attraction Poles Program
initiated by the Belgian Science Policy Office (MICRODEV to L.V.M.), the Fonds Jean
Brachet and Fondation Van Buuren (to L.V.M. and A.G.-P.), the Estonian Research
Council (IUT2-22 to T.T., M.P., and N.K.), the European Regional Development Fund
through the Centre of Excellence in Molecular Cell Engineering (to V.H., M.P., N.K., and
T.T.), the Swedish Research Council (Vetenskapsrådet) (2013-4680 to V.H.) and the
Ragnar Söderberg Foundation (Ragnar Söderberg Fellow in Medicine 2014 to V.H.). N.F.
is a Ph.D. fellow at FNRS-FRS (Fonds de la Recherche Scientifique).
REFERENCES
1. Díaz-Orejas R, Espinosa M, Yeo CC. 2017. The importance of the
expendable: toxin-antitoxin genes in plasmids and chromosomes. Front
Microbiol 8:1479. https://doi.org/10.3389/fmicb.2017.01479.
2. Hall AM, Gollan B, Helaine S. 2017. Toxin-antitoxin systems: reversible
toxicity. Curr Opin Microbiol 36:102–110. https://doi.org/10.1016/j.mib
.2017.02.003.
3. Hayes F, Van Melderen L. 2011. Toxins-antitoxins: diversity, evolution
and function. Crit Rev Biochem Mol Biol 46:386 – 408. https://doi.org/10
.3109/10409238.2011.600437.
4. Hõrak R, Tamman H. 2017. Desperate times call for desperate measures:
benefits and costs of toxin-antitoxin systems. Curr Genet 63:69 –74.
https://doi.org/10.1007/s00294-016-0622-2.
5. Brzozowska I, Zielenkiewicz U. 2013. Regulation of toxin-antitoxin sys-
tems by proteolysis. Plasmid 70:33– 41. https://doi.org/10.1016/j.plasmid
.2013.01.007.
6. Loris R, Garcia-Pino A. 2014. Disorder- and dynamics-based regulatory
mechanisms in toxin-antitoxin modules. Chem Rev 114:6933– 6947.
https://doi.org/10.1021/cr400656f.
7. Christensen SK, Mikkelsen M, Pedersen K, Gerdes K. 2001. RelE, a global
inhibitor of translation, is activated during nutritional stress. Proc Natl
Acad SciUSA98:14328 –14333. https://doi.org/10.1073/pnas.2513
27898.
8. Castro-Roa D, Garcia-Pino A, De Gieter S, van Nuland NAJ, Loris R, Zenkin
N. 2013. The Fic protein Doc uses an inverted substrate to phosphorylate
and inactivate EF-Tu. Nat Chem Biol 9:811– 817. https://doi.org/10.1038/
nchembio.1364.
9. Jure˙ nas D, Chatterjee S, Konijnenberg A, Sobott F, Droogmans L, Garcia-
Pino A, Van Melderen L. 2017. AtaT blocks translation initiation by
N-acetylation of the initiator tRNA
fMet
. Nat Chem Biol 13:640 – 646.
https://doi.org/10.1038/nchembio.2346.
10. Zhang Y, Zhang J, Hoeflich KP, Ikura M, Qing G, Inouye M. 2003. MazF
cleaves cellular mRNAs specifically at ACA to block protein synthesis
in Escherichia coli. Mol Cell 12:913–923. https://doi.org/10.1016/S1097
-2765(03)00402-7.
11. Cheverton AM, Gollan B, Przydacz M, Wong CT, Mylona A, Hare SA,
Helaine S. 2016. A Salmonella toxin promotes persister formation
through acetylation of tRNA. Mol Cell 63:86 –96. https://doi.org/10.1016/
j.molcel.2016.05.002.
12. Tsuchimoto S, Nishimura Y, Ohtsubo E. 1992. The stable maintenance
system pem of plasmid R100: degradation of PemI protein may allow
PemK protein to inhibit cell growth. J Bacteriol 174:4205– 4211. https://
doi.org/10.1128/jb.174.13.4205-4211.1992.
13. Van Melderen L, Bernard P, Couturier M. 1994. Lon-dependent proteol-
ysis of CcdA is the key control for activation of CcdB in plasmid-free
segregant bacteria. Mol Microbiol 11:1151–1157. https://doi.org/10.1111/j
.1365-2958.1994.tb00391.x.
14. Lehnherr H, Yarmolinsky MB. 1995. Addiction protein Phd of plasmid
prophage P1 is a substrate of the ClpXP serine protease of Escherichia
coli. Proc Natl Acad SciUSA92:3274 –3277. https://doi.org/10.1073/
pnas.92.8.3274.
15. Afif H, Allali N, Couturier M, Van Melderen L. 2001. The ratio between
CcdA and CcdB modulates the transcriptional repression of the ccd
poison-antidote system. Mol Microbiol 41:73– 82. https://doi.org/10
.1046/j.1365-2958.2001.02492.x.
16. Tsuchimoto S, Ohtsubo E. 1993. Autoregulation by cooperative binding
of the PemI and PemK proteins to the promoter region of the pem
operon. Mol Gen Genet 237:81– 88.
17. Garcia-Pino A, De Gieter S, Talavera A, De Greve H, Efremov RG, Loris R.
2016. An intrinsically disordered entropic switch determines allostery in
Phd-Doc regulation. Nat Chem Biol 12:490 – 496. https://doi.org/10.1038/
nchembio.2078.
18. Pandey DP, Gerdes K. 2005. Toxin-antitoxin loci are highly abundant in
free-living but lost from host-associated prokaryotes. Nucleic Acids Res
33:966 –976. https://doi.org/10.1093/nar/gki201.
19. Leplae R, Geeraerts D, Hallez R, Guglielmini J, Drèze P, Van Melderen L.
2011. Diversity of bacterial type II toxin-antitoxin systems: a comprehen-
sive search and functional analysis of novel families. Nucleic Acids Res
39:5513–5525. https://doi.org/10.1093/nar/gkr131.
20. Ramisetty BC, Santhosh RS. 2016. Horizontal gene transfer of chromo-
somal type II toxin-antitoxin systems of Escherichia coli. FEMS Microbiol
Lett 363:fnv238. https://doi.org/10.1093/femsle/fnv238.
21. Sala A, Bordes P, Genevaux P. 2014. Multitasking SecB chaperones in
bacteria. Front Microbiol 5:666. https://doi.org/10.3389/fmicb.2014.00666.
22. Van Melderen L. 2010. Toxin-antitoxin systems: why so many, what
for? Curr Opin Microbiol 13:781–785. https://doi.org/10.1016/j.mib
.2010.10.006.
23. Van Melderen L, Saavedra De Bast M. 2009. Bacterial toxin-antitoxin
systems: more than selfish entities? PLoS Genet 5:e1000437. https://doi
.org/10.1371/journal.pgen.1000437.
24. Magnuson RD. 2007. Hypothetical functions of toxin-antitoxin systems. J
Bacteriol 189:6089 – 6092. https://doi.org/10.1128/JB.00958-07.
25. Yarmolinsky MB. 1995. Programmed cell death in bacterial populations.
Science 267:836 – 837. https://doi.org/10.1126/science.7846528.
26. Guglielmini J, Van Melderen L. 2011. Bacterial toxin-antitoxin systems:
translation inhibitors everywhere. Mob Genet Elements 1:283–290.
https://doi.org/10.4161/mge.18477.
27. Wozniak RA, Waldor MK. 2009. A toxin-antitoxin system promotes the
Goormaghtigh et al. ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 12
maintenance of an integrative conjugative element. PLoS Genet
5:e1000439. https://doi.org/10.1371/journal.pgen.1000439.
28. Huguet KT, Gonnet M, Doublet B, Cloeckaert A. 2016. A toxin antitoxin
system promotes the maintenance of the IncA/C-mobilizable Salmonella
Genomic Island 1. Sci Rep 6:32285. https://doi.org/10.1038/srep32285.
29. Szekeres S, Dauti M, Wilde C, Mazel D, Rowe-Magnus DA. 2007. Chro-
mosomal toxin-antitoxin loci can diminish large-scale genome reduc-
tions in the absence of selection. Mol Microbiol 63:1588 –1605. https://
doi.org/10.1111/j.1365-2958.2007.05613.x.
30. Koga M, Otsuka Y, Lemire S, Yonesaki T. 2011. Escherichia coli rnlA and
rnlB compose a novel toxin-antitoxin system. Genetics 187:123–130.
https://doi.org/10.1534/genetics.110.121798.
31. Saavedra De Bast M, Mine N, Van Melderen L. 2008. Chromosomal
toxin-antitoxin systems may act as antiaddiction modules. J Bacteriol
190:4603– 4609. https://doi.org/10.1128/JB.00357-08.
32. Tsilibaris V, Maenhaut-Michel G, Mine N, Van Melderen L. 2007. What is
the benefit to Escherichia coli of having multiple toxin-antitoxin systems
in its genome? J Bacteriol 189:6101– 6108. https://doi.org/10.1128/JB
.00527-07.
33. Maisonneuve E, Shakespeare LJ, Jørgensen MG, Gerdes K. 2011. Bacterial
persistence by RNA endonucleases. Proc Natl Acad Sci U S A 108:
13206 –13211. https://doi.org/10.1073/pnas.1100186108.
34. Maisonneuve E, Castro-Camargo M, Gerdes K. 2013. (p)ppGpp controls
bacterial persistence by stochastic induction of toxin-antitoxin activity.
Cell 154:1140 –1150. https://doi.org/10.1016/j.cell.2013.07.048.
35. Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S. 2004. Bacterial
persistence as a phenotypic switch. Science 305:1622–1625. https://doi
.org/10.1126/science.1099390.
36. Lewis K. 2010. Persister cells. Annu Rev Microbiol 64:357–372. https://
doi.org/10.1146/annurev.micro.112408.134306.
37. Balaban NQ. 2011. Persistence: mechanisms for triggering and enhanc-
ing phenotypic variability. Curr Opin Genet Dev 21:768 –775. https://doi
.org/10.1016/j.gde.2011.10.001.
38. Brauner A, Fridman O, Gefen O, Balaban NQ. 2016. Distinguishing be-
tween resistance, tolerance and persistence to antibiotic treatment. Nat
Rev Microbiol 14:320 –330. https://doi.org/10.1038/nrmicro.2016.34.
39. Kaldalu N, Hauryliuk V, Tenson T. 2016. Persisters —as elusive as ever. Appl
Microbiol Biotechnol 100:6545– 6553. https://doi.org/10.1007/s00253-016
-7648-8.
40. Ramisetty BC, Ghosh D, Roy Chowdhury M, Santhosh RS. 2016. What is
the link between stringent response, endoribonuclease encoding type II
toxin-antitoxin systems and persistence? Front Microbiol 7:1882. https://
doi.org/10.3389/fmicb.2016.01882.
41. Theodore A, Lewis K, Vulic M. 2013. Tolerance of Escherichia coli to
fluoroquinolone antibiotics depends on specific components of the
SOS response pathway. Genetics 195:1265–1276. https://doi.org/10.1534/
genetics.113.152306.
42. Shan Y, Brown Gandt A, Rowe SE, Deisinger JP, Conlon BP, Lewis K. 2017.
ATP-dependent persister formation in Escherichia coli. mBio 8:e02267
-16. https://doi.org/10.1128/mBio.02267-16.
43. Van Melderen L, Wood TK. 2017. Commentary: what is the link between
stringent response, endoribonuclease encoding type II toxin-antitoxin
systems and persistence? Front Microbiol 8:191. https://doi.org/10.3389/
fmicb.2017.00191.
44. Germain E, Roghanian M, Gerdes K, Maisonneuve E. 2015. Stochastic
induction of persister cells by HipA through (p)ppGpp-mediated activa-
tion of mRNA endonucleases. Proc Natl Acad SciUSA112:5171–5176.
https://doi.org/10.1073/pnas.1423536112.
45. Harms A, Fino C, Sørensen MA, Semsey S, Gerdes K. 2017. Prophages
and growth dynamics confound experimental results with antibiotic-
tolerant persister cells. mBio 8:e01964-17. https://doi.org/10.1128/mBio
.01964-17.
46. Maisonneuve E, Shakespeare LJ, Jorgensen MG, Gerdes K. 2018.
Retraction: bacterial persistence by RNA endonucleases. Proc Natl Acad
SciUSA115:E2901. https://doi.org/10.1073/pnas.1803278115.
47. Maisonneuve E, Castro-Camargo M, Gerdes K. 2018. Retraction:
(p)ppGpp controls bacterial persistence by stochastic induction of
toxin-antitoxin activity. Cell 172:1135. https://doi.org/10.1016/j.cell.2018
.02.023.
48. Goeders N, Drèze PL, Van Melderen L. 2013. Relaxed cleavage specificity
within the RelE toxin family. J Bacteriol 195:2541–2549. https://doi.org/
10.1128/JB.02266-12.
49. Datsenko KA, Wanner BL. 2000. One-step inactivation of chromosomal
genes in Escherichia coli K-12 using PCR products. Proc Natl Acad Sci
U S A 97:6640 – 6645. https://doi.org/10.1073/pnas.120163297.
50. Jiang Y, Pogliano J, Helinski DR, Konieczny I. 2002. ParE toxin encoded by
the broad-host-range plasmid RK2 is an inhibitor of Escherichia coli
gyrase. Mol Microbiol 44:971–979. https://doi.org/10.1046/j.1365-2958
.2002.02921.x.
51. Yuan J, Yamaichi Y, Waldor MK. 2011. The three Vibrio cholerae chromo-
some II-encoded ParE toxins degrade chromosome I following loss of
chromosome II. J Bacteriol 193:611– 619. https://doi.org/10.1128/JB.01185-10.
52. Goormaghtigh F, Van Melderen L. 2016. Optimized method for measur-
ing persistence in Escherichia coli with improved reproducibility. Meth-
ods Mol Biol 1333:43–52. https://doi.org/10.1007/978-1-4939-2854-5_4.
53. Kholodii GY, Mindlin SZ. 1985. Integration of bacteriophages lambda
and phi 80 in wild-type Escherichia coli at secondary attachment sites.
II. Genetic structure and mechanism of polylysogen formation for
lambda, phi 80 and the lambda att80 hybrid. Mol Gen Genet 198:
491– 496. https://doi.org/10.1007/BF00332945.
54. Gross M, Marianovsky I, Glaser G. 2006. MazG—a regulator of pro-
grammed cell death in Escherichia coli. Mol Microbiol 59:590 – 601.
https://doi.org/10.1111/j.1365-2958.2005.04956.x.
55. Battesti A, Majdalani N, Gottesman S. 2011. The RpoS-mediated general
stress response in Escherichia coli. Annu Rev Microbiol 65:189 –213.
https://doi.org/10.1146/annurev-micro-090110-102946.
56. Hirsch M, Elliott T. 2002. Role of ppGpp in rpoS stationary-phase regu-
lation in Escherichia coli. J Bacteriol 184:5077–5087. https://doi.org/10
.1128/JB.184.18.5077-5087.2002.
57. Roostalu J, Jõers A, Luidalepp H, Kaldalu N, Tenson T. 2008. Cell division
in Escherichia coli cultures monitored at single cell resolution. BMC
Microbiol 8:68. https://doi.org/10.1186/1471-2180-8-68.
58. Lindner AB, Madden R, Demarez A, Stewart EJ, Taddei F. 2008. Asym-
metric segregation of protein aggregates is associated with cellular
aging and rejuvenation. Proc Natl Acad SciUSA105:3076 –3081.
https://doi.org/10.1073/pnas.0708931105.
59. Landgraf D, Okumus B, Chien P, Baker TA, Paulsson J. 2012. Segregation
of molecules at cell division reveals native protein localization. Nat
Methods 9:480 – 482. https://doi.org/10.1038/nmeth.1955.
60. Choi H, Yang Z, Weisshaar JC. 2015. Single-cell, real-time detection of
oxidative stress induced in Escherichia coli by the antimicrobial peptide
CM15. Proc Natl Acad SciUSA112:E303–E310. https://doi.org/10.1073/
pnas.1417703112.
61. Bindels DS, Haarbosch L, van Weeren L, Postma M, Wiese KE, Mastop M,
Aumonier S, Gotthard G, Royant A, Hink MA, Gadella TW, Jr. 2017.
mScarlet: a bright monomeric red fluorescent protein for cellular imag-
ing. Nat Methods 14:53–56. https://doi.org/10.1038/nmeth.4074.
62. Kudrin P, Varik V, Oliveira SR, Beljantseva J, Del Peso Santos T, Dzhygyr
I, Rejman D, Cava F, Tenson T, Hauryliuk V. 2017. Subinhibitory concen-
trations of bacteriostatic antibiotics induce relA-dependent and relA-
independent tolerance to beta-lactams. Antimicrob Agents Chemother
61:e02173-16. https://doi.org/10.1128/AAC.02173-16.
63. Helaine S, Cheverton AM, Watson KG, Faure LM, Matthews SA, Holden
DW. 2014. Internalization of Salmonella by macrophages induces forma-
tion of nonreplicating persisters. Science 343:204 –208. https://doi.org/
10.1126/science.1244705.
64. Adams KN, Takaki K, Connolly LE, Wiedenhoft H, Winglee K, Humbert O,
Edelstein PH, Cosma CL, Ramakrishnan L. 2011. Drug tolerance in repli-
cating mycobacteria mediated by a macrophage-induced efflux mech-
anism. Cell 145:39 –53. https://doi.org/10.1016/j.cell.2011.02.022.
65. Dörr T, Vulic´ M, Lewis K. 2010. Ciprofloxacin causes persister formation
by inducing the TisB toxin in Escherichia coli. PLoS Biol 8:e1000317.
https://doi.org/10.1371/journal.pbio.1000317.
66. Gelens L, Hill L, Vandervelde A, Danckaert J, Loris R. 2013. A general
model for toxin-antitoxin module dynamics can explain persister cell
formation in E. coli. PLoS Comput Biol 9:e1003190. https://doi.org/10
.1371/journal.pcbi.1003190.
67. Cataudella I, Sneppen K, Gerdes K, Mitarai N. 2013. Conditional cooper-
ativity of toxin - antitoxin regulation can mediate bistability between
growth and dormancy. PLoS Comput Biol 9:e1003174. https://doi.org/
10.1371/journal.pcbi.1003174.
68. Tian C, Semsey S, Mitarai N. 2017. Synchronized switching of multiple
toxin-antitoxin modules by (p)ppGpp fluctuation. Nucleic Acids Res
45:8180 – 8189. https://doi.org/10.1093/nar/gkx552.
69. Neidhardt FC, Bloch PL, Smith DF. 1974. Culture medium for enterobac-
teria. J Bacteriol 119:736 –747.
70. Jackman SD, Vandervalk BP, Mohamadi H, Chu J, Yeo S, Hammond SA,
Revisiting the Link between TA Systems and Persistence ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 13
Jahesh G, Khan H, Coombe L, Warren RL, Birol I. 2017. ABySS 2.0:
resource-efficient assembly of large genomes using a Bloom filter. Ge-
nome Res 27:768 –777. https://doi.org/10.1101/gr.214346.116.
71. Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, Cuomo
CA, Zeng Q, Wortman J, Young SK, Earl AM. 2014. Pilon: an integrated
tool for comprehensive microbial variant detection and genome assem-
bly improvement. PLoS One 9:e112963. https://doi.org/10.1371/journal
.pone.0112963.
72. Li H, Durbin R. 2009. Fast and accurate short read alignment with
Burrows-Wheeler transform. Bioinformatics 25:1754 –1760. https://doi
.org/10.1093/bioinformatics/btp324.
73. Darling AE, Mau B, Perna NT. 2010. progressiveMauve: multiple genome
alignment with gene gain, loss and rearrangement. PLoS One 5:e11147.
https://doi.org/10.1371/journal.pone.0011147.
74. Herbig A, Jäger G, Battke F, Nieselt K. 2012. GenomeRing: alignment
visualization based on SuperGenome coordinates. Bioinformatics 28:
i7–i15. https://doi.org/10.1093/bioinformatics/bts217.
75. Marçais G, Delcher AL, Phillippy AM, Coston R, Salzberg SL, Zimin A.
2018. MUMmer4: a fast and versatile genome alignment system. PLoS
Comput Biol 14:e1005944. https://doi.org/10.1371/journal.pcbi.1005944.
76. Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, Wishart DS. 2016.
PHASTER: a better, faster version of the PHAST phage search tool.
Nucleic Acids Res 44:W16 –W21. https://doi.org/10.1093/nar/gkw387.
77. Köster J, Rahmann S. 2012. Snakemake—a scalable bioinformatics
workflow engine. Bioinformatics 28:2520 –2522. https://doi.org/10
.1093/bioinformatics/bts480.
78. Grüning B, Dale R, Sjödin A, Rowe J, Chapman BA, Tomkins-Tinch CH,
Valieris R, The Bioconda Team, Köster J. 2017. Bioconda: a sustainable
and comprehensive software distribution for the life sciences. bioRxiv
https://doi.org/10.1101/207092.
Goormaghtigh et al. ®
May/June 2018 Volume 9 Issue 3 e00640-18 mbio.asm.org 14
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