Antibiotic efficacy — context matters
The MIT Faculty has made this article openly available.
Please share
how this access benefits you. Your story matters.
Citation
Yang, Jason H et al. “Antibiotic Efficacy — Context Matters.” Current
Opinion in Microbiology 39 (October 2017): 73–80 © 2017 The
Authors
As Published
http://dx.doi.org/10.1016/J.MIB.2017.09.002
Publisher
Elsevier BV
Version
Final published version
Citable link
http://hdl.handle.net/1721.1/117594
Terms of Use
Creative Commons Attribution 4.0 International License
Antibiotic
efficacy
—
context
matters
Jason
H
Yang
1
,
2
,
4
,
Sarah
C
Bening
1
,
2
,
4
and
James
J
Collins
1
,
2
,
3
Antibioticlethalityisacomplexphysiologicalprocess,sensitivetoexternalcues.Recentadvancesusingsystemsapproaches haverevealedhoweventsdownstreamofprimarytarget inhibitionactivelyparticipateinantibioticdeathprocesses.In particular,alteredmetabolism,translationalstressandDNA damageeachcontributetoantibiotic-inducedcelldeath. Moreover,environmentalfactorssuchasoxygenavailability, extracellularmetabolites,populationheterogeneityand multidrugcontextsalterantibioticefficacybyimpacting bacterialmetabolismandstressresponses.Herewereview recentstudiesonantibioticefficacyandhighlightinsights gainedontheinvolvementofcellularrespiration,redoxstress andalteredmetabolisminantibioticlethality.Wediscussthe complexityfoundinnaturalenvironmentsandhighlight knowledgegapsinantibioticlethalitythatmaybeaddressed usingsystemsapproaches.
Addresses
1InstituteforMedicalEngineering&Science,DepartmentofBiological
Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
2
BroadInstituteofMITandHarvard,415MainSt,Cambridge,MA 02142,USA
3WyssInstituteforBiologicallyInspiredEngineeringatHarvard
University, 3 Blackfan Cir, Boston, MA 02115, USA Correspondingauthor:Collins,JamesJ([email protected])
4Co-firstauthor.
CurrentOpinioninMicrobiology2017,39:73–80
ThisreviewcomesfromathemedissueonBacterialsystemsbiology EditedbyChristophDehioandDirkBumann
ForacompleteoverviewseetheIssueandtheEditorial
Availableonline16thOctober2017
https://doi.org/10.1016/j.mib.2017.09.002
1369-5274/ã2017TheAuthors.PublishedbyElsevierLtd.Thisisan openaccessarticleundertheCCBYlicense(http://creativecommons. org/licenses/by/4.0/).
Introduction
The
discovery
of
antibiotics
early
in
the
20th
century
transformed
medical
practice
and
microbiological
inves-tigation,
driving
discovery
in
numerous
aspects
of
micro-bial
physiology
including
stress
responses,
mutagenesis
and
microbial
ecology.
The
primary
targets
and
mecha-nisms
of
action
for
most
bactericidal
antibiotics
have
been
identified
and
well-studied
[
1
];
however,
there
is
a
grow-ing
appreciation
that
antibiotic
lethality
is
a
complex
systems-level
process
that
is
sensitive
to
environmental
factors
[
2
].
Environmental
differences
elicit
diverse
anti-biotic
treatment
responses,
from
increased
susceptibility
to
phenotypic
tolerance.
In
light
of
the
diminishing
pipeline
for
antibiotic
discovery
[
3
],
there
is
an
urgent
need
to
better
understand
mechanisms
and
factors
that
influence
antibiotic
efficacy.
Here,
we
review
recent
studies
of
antibiotic
lethality
in
diverse
microbial
species.
We
summarize
intracellular
mechanisms
underlying
lethality
and
describe
environ-mental
factors
that
tune
efficacy.
We
discuss
how
anti-biotics
do
more
than
inhibit
their
primary
targets,
describe
how
downstream
processes
critically
participate
in
active
death
processes,
and
highlight
insights
gained
from
manipulating
extracellular
conditions.
We
also
con-sider
the
complexity
of
natural
environments
and
discuss
how
systems
approaches
and
emerging
technologies
may
provide
novel
insights
into
antibiotic
lethality.
Bactericidal
processes
Bactericidal
antibiotics
primarily
target
processes
essen-tial
to
cellular
replication,
but
studies
from
several
labo-ratories
implicate
events
downstream
of
target
inhibition
as
critical
components
of
lethality
(
Figure
1
).
This
is
supported
by
sequenced
strains
from
experimental
evo-lution
studies
[
4–7
]
and
clinical
isolates
[
8–10
]
demon-strating
that
mutations
unrelated
to
an
antibiotic
target
or
transport
can
significantly
inhibit
antibiotic
lethality.
In
2007,
Kohanski
et
al.
introduced
the
hypothesis
that
bactericidal
antibiotics
of
different
classes
commonly
induce
reactive
oxygen
species
(ROS)
as
part
of
their
lethality
[
11
].
Informed
by
microarray
experiments
with
Escherichia
coli
cells
treated
with
diverse
bactericidal
antibiotics,
the
authors
genetically
validated
ROS-medi-ated
contributions
to
lethality
by
tricarboxylic
acid
(TCA)
cycle
activity,
Fe–S
cluster
biosynthesis,
and
SOS-medi-ated
DNA
repair.
This
stimulated
further
studies
reveal-ing
the
involvement
of
two-component
signaling
[
12
],
iron
homeostasis
[
13–15
],
and
nucleotide
oxidation
[
16,17
]
in
ROS-mediated
antibiotic
lethality;
enhanced
lethality
in
cells
deficient
in
oxidative
defenses
[
18
];
protection
against
lethality
by
mechanisms
involving
antioxidant
defense
[
19–23
];
as
well
as
investigations
challenging
the
general
hypothesis
[
24–26
].
These
stud-ies
have
been
reviewed
and
addressed
elsewhere
[
2,27,28
].
These
investigations
and
several
others
demonstrate
that
antibiotics
perturb
diverse
aspects
of
bacterial
physiology.
Such
perturbations
actively
contribute
to
cell
death,
in
part,
by
directly
inducing
cellular
damage
while
also
driving
the
bacterial
cell
away
from
homeostasis
and
inhibiting
its
ability
to
cope
with
antibiotic
challenge.
Studies
on
these
perturbations
have
revealed
several
additional
important
insights
linking
aspects
of
cellular
physiology
to
antibiotic
lethality.
Altered
metabolism
and
reactive
oxygen
species
Several
recent
studies
have
further
explored
and
con-firmed
the
involvement
of
altered
metabolism
and
ROS
in
antibiotic
lethality
using
new
technologies.
Targeted
metabolomic
studies
have
revealed
significant
changes
in
intracellular
energy
metabolites
following
antibiotic
treat-ment
[
29–31
],
corroborated
by
live-cell
imaging
experi-ments
directly
measuring
transient
changes
in
ATP
[
32
].
Experiments
using
the
Seahorse
XF
Analyzer
have
captured
real-time
increases
to
cellular
respiration
by
bactericidal
antibiotics
[
33–35
],
complementing
real-time
measurements
of
overflow
ROS
production
using
elec-trochemical
[
36
]
or
genetically
encoded
biosensors
[
33
].
Interestingly,
integrated
analyses
of
transcriptomic
and
metabolomic
data
have
revealed
TCA
cycle
activity
to
be
critical
for
antibiotic
lethality,
independent
from
drug
uptake
[
37
].
Collectively,
these
studies,
and
several
others
[
8,38–45
],
demonstrate
that
changes
to
bacterial
metabolism
participate
in
lethality
for
many
bactericidal
antibiotics.
Bacteria
are
naturally
optimized
for
energy
efficiency
[
46,47
]
and
several
recent
studies
suggest
metabolic
deregulation
may
also
contribute
to
antibiotic
death
processes.
For
instance,
penicillin-binding
protein
disrup-tion
by
b-lactams
stimulates
a
futile
cycle
of
cell
wall
synthesis
and
degradation
that
depletes
cellular
resources
as
part
of
its
toxicity
[
48,49
].
In
addition,
ATP
synthase
inhibition
by
bedaquiline
stimulates
futile
cycling
of
protons
in
the
respiratory
chain
[
50
],
which
increases
oxygen
consumption
[
35
]
and
is
lethal
to
M.
tuberculosis
[
51
].
Moreover,
genetically
induced
futile
cycling
in
MazF-mediated
RNA
degradation
has
been
shown
to
confer
complete
protection
against
b-lactam
and
quino-lone
lethality,
but
potentiate
sensitivity
to
aminoglyco-sides
[
52
].
It
is
likely
that
metabolic
deregulation
by
futile
cycling
constitutes
a
common
mechanism
of
antibiotic
lethality.
This
is
supported
by
recent
evidence
that
genetically
induced
futile
cycling
shares
many
metabolic
features
with
treatment
by
bactericidal
antibiotics,
including
increased
oxygen
consumption,
increased
ROS
production
and
net
decreases
in
intracellular
ATP
[
53
].
Interestingly,
cells
subjected
to
genetically
induced
futile
cycling
also
exhibit
increased
sensitivity
to
oxidative
stress.
Metabolic
deregulation
may
therefore
amplify
antibiotic-induced
stress
and
enhance
lethality.
Translational
stress
Antibiotic
lethality
is
well
recognized
to
decrease
under
conditions
with
reduced
bacterial
growth,
and
several
recent
proteomic
studies
have
revealed
ribosomal
74 BacterialsystemsbiologyFigure1 Antibiotics Target Inhibition Disrupted Cellular Processes Bacterial Stress Responses Metabolic Demand Toxic Metabolic Byproducts Damaged Cellular Components Active Death Processes
Cell Death
Current Opinion in Microbiology
Antibioticsinduceactivedeathprocessesunderlyinglethality.Targetinhibitiondirectlytriggerslethalitybydisruptingessentialcellularprocesses (black). Stress responses induced by such disruptions indirectly trigger lethality by increasing metabolic demand and generating metabolic byproducts that damage cellular components (e.g., DNA, proteins, lipids) (blue). Environmental factors tune antibiotic lethality by acting on stress responsesand/oralteringbacterialmetabolism.
biosynthesis
and
activity
to
be
key
regulators
of
bacterial
growth
rate
and
metabolism
[
47,54,55
].
Macromolecular
processes
such
as
protein
translation
are
therefore
also
likely
to
be
involved
in
antibiotic
lethality.
In
support
of
this,
bacteriostatic
inhibitors
of
protein
translation
suppress
the
lethality
of
bactericidal
antibiotics
in
wild-type
E.
coli
[
34
],
but
induce
lethality
in
mutant
cells
depleted
for
ribosomal
assembly
operons
[
56
].
Addition-ally,
bactericidal
antibiotics
induce
several
heat
shock
genes
responsive
to
translational
stress
[
11
].
Because
growing
cells
devote
>50%
of
their
energy
to
support
the
demands
of
protein
translation
[
57
],
translational
stress
likely
amplifies
the
lethal
consequences
of
meta-bolic
stresses
induced
by
antibiotic
treatment.
Future
studies
are
needed
to
clarify
how
antibiotic
disruption
of
translation
and
other
macromolecular
processes
con-tributes
to
lethality.
DNA
damage
Several
DNA
repair
genes
are
enriched
in
chemogenomic
screens
[
58,59
]
and
promoter–reporter
experiments
[
60
],
suggesting
DNA
damage
to
also
be
critical
for
antibiotic
lethality.
These
findings
have
been
supported
geneti-cally,
as
deletion
of
SOS
response
genes
enhances
drug
susceptibility
[
11,16
],
while
over-expression
of
mismatch
repair
genes
confers
protection
[
16,33
].
Moreover,
bio-chemical
inhibition
of
RecA
with
polysulfonated
com-pounds
potentiates
the
lethality
of
multiple
antibiotics
against
both
Gram-negative
and
Gram-positive
bacteria
[
61,62
].
Oxidative
damage
to
the
nucleotide
pool
may,
in
part,
underlie
this
phenotype
[
29
],
as
incorporation
of
8-oxo-guanine
induces
mismatch
repair
defects
that
trig-ger
the
formation
of
double-stranded
DNA
breaks
[
16
].
Additionally,
Holliday
junction
resolvase
disruption
was
recently
shown
to
enhance
quinolone
lethality
in
myco-bacteria
[
63
].
This
could
be
rescued
by
treatment
with
bipyridyl,
an
iron
chelator,
and
thiourea,
a
hydroxyl
radi-cal
chelator,
supporting
prior
work
implicating
antibiotic-induced
oxidative
stress
in
damaging
DNA
[
11,16
].
Mechanistic
studies
are
needed
to
further
clarify
the
processes
bridging
antibiotic
target
inhibition
and
lethal
DNA
damage.
Primary
and
secondary
death
processes
Collectively,
these
studies
implicate
events
downstream
of
target
inhibition
as
active
participants
in
antibiotic
lethality.
It
is
our
view
that
much
of
the
misunderstanding
over
antibiotic
lethality
is
derived
from
the
difficulty
in
experimentally
separating
the
lethal
contributions
of
essential
gene
product
inhibition
from
those
of
down-stream
mechanisms.
It
may
be
possible
to
distinguish
between
these
biochemically,
as
the
lethality
driven
by
secondary
processes
can
be
interrupted
by
inhibiting
protein
translation
and
cell
respiration
[
34
]
or
by
meta-bolic
shunting
[
37
].
Cell
death
emerging
from
stress-induced
death
processes
was
recently
studied
in
an
anti-biotic-free
system
using
a
historically
significant
fusion
protein
[
64
].
The
authors
demonstrated
that
the
primary
consequence
of
jamming
the
SecY
protein
translocation
machinery
was
cell
stasis
and
that
cell
death
instead
emerged
from
downstream
events
shared
with
antibiotic
lethality,
including
ROS
accumulation
and
double-stranded
DNA
damage.
Importantly,
nucleotide
oxida-tion
occurred
hours
before
cell
death
in
this
system.
Taken
together,
these
studies
support
a
model
for
anti-biotic
lethality
where
target
inhibition
drives
active
death
processes
by
(1)
damaging
essential
cellular
processes
and
(2)
inducing
stress
responses
that
increase
metabolic
activity,
thereby
generating
toxic
metabolic
byproducts,
that
damage
cellular
components
(
Figure
1
).
Environmental
factors
Bacterial
stress
responses
evolved
to
help
bacteria
adapt
to
various
environments
[
65
]
and
may
confer
protection
against
antibiotic
lethality.
For
instance,
pre-treatment
with
hydrogen
peroxide
can
protect
against
multiple
bactericidal
antibiotics
by
priming
oxidative
stress
responses
[
33
],
and
nutrient
starvation
can
induce
phe-notypically
tolerant
‘persister’
cells
by
activating
ppGpp
and
the
stringent
response
[
66,67
].
Several
recent
studies
demonstrate
how
environmental
factors
may
alter
antibi-otic
efficacy,
providing
additional
insight
into
antibiotic
lethality
(
Figure
2
).
Oxygen
availability
Antibiotic
efficacy
decreases
in
the
absence
of
oxygen
[
24,25,33
]
and
hyperbaric
oxygen
can
potentiate
Figure 2
Extracellular
Metabolites
Altered
Metabolism
Stress
Responses
Oxygen
Availability
Antibiotics
Population
Heterogeneity
Multidrug
Contexts
Target
Inhibition
Cell
Death
Current Opinion in Microbiology
Environmental factors tune antibiotic lethality. Cues such as oxygen availabilityandextracellularmetabolitesimpactcelldeathbyactingon cellmetabolism.Populationheterogeneityandmultidrugcontexts protectagainstlethalitybyinducingstressresponsesanddefense mechanisms.
antibiotic
killing
of
bacterial
biofilms
[
68
].
Under
normoxia,
oxygen
participates
in
aerobic
respiration
as
the
terminal
electron
acceptor
for
ATP
synthesis
and
produces
reactive
species
as
metabolic
byproducts
[
35
].
In
such
conditions,
antibiotic
efficacy
is
linked
to
respi-ratory
activity;
for
example,
deletion
of
the
cytochrome
oxidases,
which
reduces
respiration,
inhibits
drug
lethal-ity
[
34
],
while
deletion
of
ATP
synthase
subunits,
which
increases
respiration,
enhances
lethality
[
34,69
].
Simi-larly,
respiratory
suppression
by
nitrite
also
inhibits
antibiotic
killing
[
70
].
Under
anoxia,
nitrate
frequently
participates
in
anaerobic
respiration
as
the
terminal
elec-tron
acceptor
and
can
potentiate
antibiotic
killing
[
33
];
it
is
not
yet
known
if
nitrate
and
other
terminal
electron
acceptors
also
generate
toxic
metabolic
byproducts
that
contribute
to
antibiotic
death
processes.
Cellular
respira-tion
likely
affects
antibiotic
efficacy
due
to
its
roles
in
metabolism
and
energy
production,
but
may
also
contrib-ute
to
lethality
by
facilitating
drug
import
[
37,71
]
or
stimulating
intracellular
alkalization
[
72
].
Collectively,
these
studies
implicate
respiratory
activity
as
an
important
mediator
of
the
lethal
processes
induced
by
antibiotics.
Extracellular
metabolites
Environmental
nutrients
affect
many
aspects
of
bacterial
physiology
that
alter
antibiotic
efficacy,
including
growth
kinetics
and
stress
response
activation
[
73,74
].
Recent
studies
using
flow
cytometry
and
high-throughput
assays
have
revealed
how
specific
carbon
metabolites
or
amino
acids
may
enhance
antibiotic
death
processes
by
fueling
TCA
cycle
activity,
increasing
cellular
respiration
and
inducing
a
proton
motive
force,
promoting
both
drug
uptake
and
subsequent
lethality
[
37,71,75,76
].
Interest-ingly,
flux
through
the
glyoxylate
shunt
was
shown
to
suppress
aminoglycoside
potentiation
by
metabolites
such
as
fumarate
despite
measureable
drug
uptake,
impli-cating
an
active
metabolic
component
to
antibiotic
lethal-ity
[
37
].
This
is
supported
by
recent
chemogenetic
screens
revealing
collateral
antibiotic
sensitivity
under
nutrient
limitation
[
77
].
Additionally,
cysteine
supple-mentation
was
shown
to
increase
cellular
respiration,
ROS,
and
antibiotic
lethality,
as
well
as
inhibit
persister
formation
in
Mycobacterium
[
78
]
through
thiol-mediated
redox
stress
[
79,80
].
To
date,
input–output
relationships
between
environmental
metabolites
and
antibiotic
death
phenotypes
have
not
been
systematically
mapped;
such
efforts
will
be
important
for
identifying
specific
metabolic
pathways
participating
in
antibiotic
lethality.
Population
heterogeneity
Antibiotic
treatment
and
environmental
stresses
can
give
rise
to
population
heterogeneity
and
confer
phenotypic
protection
against
lethality
[
81
];
simple
examples
include
variable
expression
of
multi-drug
efflux
pumps
[
82
]
and
stochastic
formation
of
persister
cells
by
toxin–antitoxin
systems
and
the
ppGpp-mediated
stringent
response
[
83,84
].
Recent
studies
have
revealed
several
additional
persister
mechanisms,
including
ATP
depletion
[
85,86
],
morphological
differentiation
[
87
],
inter-cell
signaling
[
88,89
],
and
toxin–antitoxin-mediated
inhibition
of
pro-ton-motive
force
[
90,91
]
or
protein
translation
[
92
].
Microbial
ecologists
have
largely
viewed
population
het-erogeneity
as
a
form
of
bet-hedging,
conserved
to
facili-tate
the
adaptation
to
new
environments
[
93
].
In
support
of
this,
several
studies
demonstrate
how
persister
mecha-nisms
can
help
pathogens
survive
inside
host
cells
[
94,95
]
and
provide
a
reservoir
for
evolving
antibiotic
resistance
[
96–98
].
Investigations
have
also
shown
how
social
beha-viors
such
as
quorum
sensing
[
99
],
signaling
[
100
]
and
cooperative
mutualism
[
101
]
can
confer
protection
against
antibiotic
lethality
in
mixed-species
environments.
Nascent
studies
on
population-level
responses
to
antibi-otic
treatment
have
revealed
several
intra-species
and
inter-species
mechanisms
for
collective
protection
[
102,103
],
but
such
experiments
are
challenging
due
to
scale.
Advances
in
synthetic
biology
and
microfluidic
technologies
are
now
poised
to
enable
significant
insight
into
ecological
mechanisms
participating
in
antibiotic
efficacy.
Multidrug
contexts
Antibiotic
treatment
outcomes
are
diverse
in
multidrug
environments,
and
previous
exposure
to
an
antibiotic
can
alter
the
efficacy
of
other
antibiotics
[
104
].
Studies
on
multidrug
contexts
highlight
the
roles
of
cellular
respira-tion
[
34
],
ATP
synthesis
and
polysaccharide
synthesis
[
69
]
in
determining
treatment
efficacy.
Pretreatment
with
sub-lethal
doses
of
single
antibiotics
induces
physi-ological
stress
responses
that
protect
against
antibiotic
lethality
[
60
]
and
other
environmental
stresses
[
105
].
Extended
antibiotic
exposure
leads
to
genetically
encoded
resistance
with
collateral
sensitivity
or
resistance
to
antibiotics
of
different
classes
[
106
],
which
may
be
exploited
to
potentiate
population-level
lethality
by
anti-biotic
cycling
[
107,108
].
Recent
efforts
integrating
meta-bolomic
profiling
with
experimental
evolution
have
revealed
how
bacterial
metabolism
may
constrain
the
acquisition
of
antibiotic
resistance
and
differential
cross-resistance
[
109
].
Mechanistic
details
explaining
antibiotic
synergy
and
antagonism
remain
nascent;
future
studies
on
multidrug
efficacy
and
resistance
would
bene-fit
from
integrated
exploration
of
environmental
and
physiological
factors.
Perspectives
Although
antibiotics
are
routinely
studied
under
well-controlled
lab
conditions,
the
natural
environments
in
which
antibiotics
are
typically
found
and
used
are
com-plex
in
nutrient
availability,
microbial
heterogeneity,
and
other
external
stressors.
Because
environmental
factors
can
elicit
diverse
effects
on
antibiotic
lethality,
careful
consideration
must
be
paid
to
experimental
conditions
to
minimize
confounding
effects
by
contextual
elements.
Several
open
questions
remain
with
respect
to
antibiotic
76 Bacterialsystemsbiologylethality
in
natural
contexts,
including
antibiotic-induced
changes
to
the
extracellular
environment
and
the
dynamic
interactions
between
different
species
sharing
a
common
ecological
niche.
Moreover,
understanding
the
differences
in
microbial
physiology
between
host
meta-bolic
environments
and
the
nutrient
rich
conditions
com-monly
used
to
study
bacterial
pathogens
will
be
important
for
translating
in
vitro
discoveries
into
clinical
care.
Further
understanding
on
how
external
effectors
act
on
bactericidal
processes
will
require
systems
approaches
to
meet
the
challenges
posed
by
such
complexity.
Antibiotic
lethality
is
increasingly
understood
to
be
driven
by
active
death
processes
resulting
from
both
target
inhibition
and
subsequent
induction
of
stress
responses.
Compensatory
changes
to
bacterial
physiology
likely
participate
bi-directionally,
furthering
downstream
cellular
damage
and
modulating
the
direct
upstream
consequences
of
target
inhibition.
For
instance,
altera-tions
in
cellular
metabolism
or
protein
translation
may
have
far-reaching
consequences
on
target
availability
and
the
biochemistry
of
drug–target
interactions.
Future
stud-ies
are
needed
to
clarify
the
relative
contributions
of
primary
and
secondary
death
processes.
Recent
advances
in
experimental
technologies
and
quan-titative
modeling
are
now
poised
to
enable
additional
systems-level
insights
into
antibiotic
lethality.
Mass
spec-trometry
allows
interrogation
into
spatial
determinants
of
antibiotic
efficacy
[
110,111
]
and
synthetic
biology
has
contributed
novel
tools
for
perturbing
essential
genes
[
112
].
Current
quantitative
models
are
providing
mecha-nistic
insights
into
several
nonlinear
components
of
anti-biotic
lethality
[
56
,113
]
and
are
useful
for
predicting
multidrug
treatment
outcomes
[
114,115
]
and
antibiotic
drug
synergies
[
116,117
].
Integration
of
these
tools
will
enable
identification
of
additional
mechanistic
details
between
primary
target
inhibition
and
subsequent
lethality.
Conclusion
Antibiotics
naturally
evolved
as
inhibitory
agents
for
microbial
warfare
and
have
provided
insights
into
mech-anisms
underlying
bacterial
cell
death.
Recent
studies
have
revealed
that
several
events
downstream
of
primary
target
inhibition
actively
contribute
to
antibiotic
lethality,
including
alterations
to
cellular
metabolism,
protein
translation,
and
DNA
damage.
These
processes
are
sen-sitive
to
environmental
cues
and
future
studies
will
be
needed
to
better
understand
antibiotic
efficacy
in
natural
settings.
Systems
approaches
have
the
potential
to
accel-erate
such
efforts
and
provide
additional
mechanistic
insight
into
antibiotic
lethality.
Conflicts
of
interest
JJC
is
scientific
co-founder
and
SAB
chair
of
EnBiotix,
an
antibiotics
startup
company.
Acknowledgements
This work was supported by the National Institutes of Health under award numbersK99GM118907andU19AI111276,theNationalScience FoundationGraduateResearchFellowshipunderawardnumber1122374, theDefenseThreatReductionAgencyunderawardnumber HDTRA1-15-1-0051,andagenerousgiftfromAnitaandJoshBekenstein.
References
and
recommended
reading
Papers of particular interest, published within the period of review, have been highlighted as:
of special interest of outstanding interest
1. KohanskiMA,DwyerDJ,CollinsJJ:Howantibioticskillbacteria: fromtargetstonetworks.NatRevMicrobiol2010,8:423-435.
2. DwyerDJ,CollinsJJ,WalkerGC:Unravelingthephysiological complexitiesofantibioticlethality.AnnuRevPharmacolToxicol 2015,55:313-332.
3. BrownED,WrightGD:Antibacterialdrugdiscoveryinthe resistanceera.Nature2016,529:336-343.
4. FridmanO,GoldbergA,RoninI,ShoreshN,BalabanNQ: Optimizationoflagtimeunderliesantibiotictolerancein evolvedbacterialpopulations.Nature2014,513:418-421.
5. VandenBerghB,MichielsJE,MichielsJ:Experimentalevolution ofEscherichiacolipersisterlevelsusingcyclicantibiotic treatments.MethodsMolBiol2016,1333:131-143.
6. GomezJE,Kaufmann-MalagaBB,WivaggCN,KimPB,SilvisMR, RenedoN,IoergerTR,AhmadR,LivnyJ,FishbeinSetal.: Ribosomalmutationspromotetheevolutionofantibiotic resistanceinamultidrugenvironment.Elife2017,6.
7. KohanskiMA,DePristoMA,CollinsJJ:Sublethalantibiotic treatmentleadstomultidrugresistanceviaradical-induced mutagenesis.MolCell2010,37:311-320.
8. NguyenD,Joshi-DatarA,LepineF,BauerleE,OlakanmiO,BeerK, McKayG,SiehnelR,SchafhauserJ,WangYetal.:Active starvationresponsesmediateantibiotictoleranceinbiofilms andnutrient-limitedbacteria.Science2011,334:982-986.
9. RuedaJ,RealpeT,MejiaGI,ZapataE,RozoJC,FerroBE, RobledoJ:Genotypicanalysisofgenesassociatedwith independentresistanceandcross-resistancetoisoniazidand ethionamideinMycobacteriumtuberculosisclinicalisolates. AntimicrobAgentsChemother2015,59:7805-7810.
10. JiaB,RaphenyaAR,AlcockB,WaglechnerN,GuoP,TsangKK, LagoBA,DaveBM,PereiraS,SharmaANetal.:CARD2017: expansionandmodel-centriccurationofthecomprehensive antibioticresistancedatabase.NucleicAcidsRes2017,45: D566-D573.
11. KohanskiMA,DwyerDJ,HayeteB,LawrenceCA,CollinsJJ:A commonmechanismofcellulardeathinducedbybactericidal antibiotics.Cell2007,130:797-810.
12. KohanskiMA,DwyerDJ,WierzbowskiJ,CottarelG,CollinsJJ: Mistranslationofmembraneproteinsandtwo-component systemactivationtriggerantibiotic-mediatedcelldeath.Cell 2008,135:679-690.
13. YeomJ,ImlayJA,ParkW:Ironhomeostasisaffects antibiotic-mediatedcelldeathinPseudomonasspecies.JBiolChem 2010,285:22689-22695.
14. FrawleyER,CrouchML,Bingham-RamosLK,RobbinsHF, WangW,WrightGD,FangFC:Ironandcitrateexportbyamajor facilitatorsuperfamilypumpregulatesmetabolismandstress resistanceinSalmonellaTyphimurium.ProcNatlAcadSciUSA 2013,110:12054-12059.
15. MehiO,BogosB,CsorgoB,PalF,NyergesA,PappB,PalC: Perturbationofironhomeostasispromotestheevolutionof antibioticresistance.MolBiolEvol2014,31:2793-2804.
16. FotiJJ,DevadossB,WinklerJA,CollinsJJ,WalkerGC:Oxidation oftheguaninenucleotidepoolunderliescelldeathby bactericidalantibiotics.Science2012,336:315-319.
17. KotturJ,NairDT:Reactiveoxygenspeciesplayanimportant roleinthebactericidalactivityofquinoloneantibiotics.Angew ChemIntEdEngl2016,55:2397-2400.
18. BizziniA,ZhaoC,AuffrayY,HartkeA:TheEnterococcusfaecalis superoxidedismutaseisessentialforitstoleranceto vancomycinandpenicillin.JAntimicrobChemother2009, 64:1196-1202.
19. GusarovI,ShatalinK,StarodubtsevaM,NudlerE:Endogenous nitricoxideprotectsbacteriaagainstawidespectrumof antibiotics.Science2009,325:1380-1384.
20. WangX,ZhaoX,MalikM,DrlicaK:Contributionofreactive oxygenspeciestopathwaysofquinolone-mediatedbacterial celldeath.JAntimicrobChemother2010,65:520-524.
21. ShatalinK,ShatalinaE,MironovA,NudlerE:H2S:auniversal defenseagainstantibioticsinbacteria.Science2011, 334:986-990.
22. LingJ,ChoC,GuoLT,AerniHR,RinehartJ,SollD:Protein aggregationcausedbyaminoglycosideactionispreventedby ahydrogenperoxidescavenger.MolCell2012,48:713-722.
23. MoselM,LiL,DrlicaK,ZhaoX:Superoxide-mediatedprotection ofEscherichiacolifromantimicrobials.AntimicrobAgents Chemother2013,57:5755-5759.
24. LiuY,ImlayJA:Celldeathfromantibioticswithoutthe involvementofreactiveoxygenspecies.Science2013, 339:1210-1213.
25. KerenI,WuY,InocencioJ,MulcahyLR,LewisK:Killingby bactericidalantibioticsdoesnotdependonreactiveoxygen species.Science2013,339:1213-1216.
26. EzratyB,VergnesA,BanzhafM,DuvergerY,HuguenotA, BrochadoAR,SuSY,EspinosaL,LoiseauL,PyBetal.:Fe–S clusterbiosynthesiscontrolsuptakeofaminoglycosidesina ROS-lessdeathpathway.Science2013,340:1583-1587.
27. VanAckerH,CoenyeT:Theroleofreactiveoxygenspeciesin antibiotic-mediatedkillingofbacteria.TrendsMicrobiol2017, 25:456-466.
28. ZhaoX,HongY,DrlicaK:Movingforwardwithreactiveoxygen speciesinvolvementinantimicrobiallethality.JAntimicrob Chemother2015,70:639-642.
29. BelenkyP,YeJD,PorterCB,CohenNR,LobritzMA,FerranteT, JainS,KorryBJ,SchwarzEG,WalkerGCetal.:Bactericidal antibioticsinducetoxicmetabolicperturbationsthatleadto cellulardamage.CellRep2015,13:968-980.
30. HoerrV,DugganGE,ZbytnuikL,PoonKK,GrosseC,
NeugebauerU,MethlingK,LofflerB,VogelHJ:Characterization andpredictionofthemechanismofactionofantibiotics throughNMRmetabolomics.BMCMicrobiol2016,16:82.
31. ZampieriM,ZimmermannM,ClaassenM,SauerU:Nontargeted metabolomicsrevealsthemultilevelresponsetoantibiotic perturbations.CellRep2017,19:1214-1228.
32.
Maglicaantibiotic-inducedZ,OzdemirE,changesMcKinneyinJD:mycobacterialSingle-celltrackingenergy reveals metabolism.MBio2015,6e02236-02214.
Thispaper demonstrates that antibiotic treatment significantly alters single-cell energy production using microfluidicsand an intracellular ATPbiosensor.
33. DwyerDJ,BelenkyPA,YangJH,MacDonaldIC,MartellJD, TakahashiN,ChanCT,LobritzMA,BraffD,SchwarzEGetal.: Antibioticsinduceredox-relatedphysiologicalalterationsas partoftheirlethality.ProcNatlAcadSciUSA2014, 111:E2100-E2109.
34. LobritzMA,BelenkyP,PorterCB,GutierrezA,YangJH, SchwarzEG,DwyerDJ,KhalilAS,CollinsJJ:Antibioticefficacy islinkedtobacterialcellularrespiration.ProcNatlAcadSciUS A2015,112:8173-8180.
35. LamprechtDA,FininPM,RahmanMA,CummingBM,RussellSL, JonnalaSR,AdamsonJH,SteynAJ:Turningtherespiratory flexibilityofMycobacteriumtuberculosisagainstitself.Nat Commun2016,7:12393.
36. LiuX,MarrakchiM,JahneM,RogersS,AndreescuS:Real-time investigationofantibiotics-inducedoxidativestressand superoxidereleaseinbacteriausinganelectrochemical biosensor.FreeRadicBiolMed2016,91:25-33.
37. MeylanS,PorterCB,YangJH,BelenkyP,GutierrezA,LobritzMA, ParkJ,KimSH,MoskowitzSM,CollinsJJ:Carbonsourcestune antibioticsusceptibilityinPseudomonasaeruginosavia tricarboxylicacidcyclecontrol.CellChemBiol2017,24: 195-206.
38. NandakumarM,NathanC,RheeKY:Isocitratelyasemediates broadantibiotictoleranceinMycobacteriumtuberculosis.Nat Commun2014,5:4306.
39. VilchezeC,HartmanT,WeinrickB,Jacobs WRJr:
Mycobacteriumtuberculosisisextraordinarilysensitiveto killingbyavitaminC-inducedFentonreaction.NatCommun 2013,4:1881.
40. ThomasVC,KinkeadLC,JanssenA,SchaefferCR,WoodsKM, LindgrenJK,PeasterJM,ChaudhariSS,SadykovM,JonesJetal.: Adysfunctionaltricarboxylicacidcycleenhancesfitnessof Staphylococcusepidermidisduringbeta-lactamstress.MBio 2013,4.
41. BrynildsenMP,WinklerJA,SpinaCS,MacDonaldIC,CollinsJJ: Potentiatingantibacterialactivitybypredictablyenhancing endogenousmicrobialROSproduction.NatBiotechnol2013, 31:160-165.
42. GrantSS,KaufmannBB,ChandNS,HaseleyN,HungDT: Eradicationofbacterialpersisterswithantibiotic-generated hydroxylradicals.ProcNatlAcadSciUSA2012,109: 12147-12152.
43. TkachenkoAG,AkhovaAV,ShumkovMS,NesterovaLY: PolyaminesreduceoxidativestressinEscherichiacolicells exposedtobactericidalantibiotics.ResMicrobiol2012,163: 83-91.
44. DuanX,HuangX,WangX,YanS,GuoS,AbdallaAE,HuangC, XieJ:L-Serinepotentiatesfluoroquinoloneactivityagainst Escherichiacolibyenhancingendogenousreactiveoxygen speciesproduction.JAntimicrobChemother2016,71: 2192-2199.
45. Goswami M,MangoliSH, JawaliN: Involvementof reactiveoxygen speciesintheactionofciprofloxacinagainstEscherichiacoli. AntimicrobAgentsChemother2006,50:949-954.
46. MaitraA,DillKA:Bacterialgrowthlawsreflecttheevolutionary importanceofenergyefficiency.ProcNatlAcadSciUSA2015, 112:406-411.
47. HuiS,SilvermanJM,ChenSS,EricksonDW,BasanM,WangJ, HwaT,WilliamsonJR:Quantitativeproteomicanalysisreveals asimplestrategyofglobalresourceallocationinbacteria.Mol SystBiol2015,11:784.
48. ChoH,UeharaT,BernhardtTG:Beta-lactamantibioticsinduce alethalmalfunctioningofthebacterialcellwallsynthesis machinery.Cell2014,159:1300-1311.
49.
ChoGarnerH,WivaggEC,BernhardtCN,KapoorTG:BacterialM,BarryZ,cellRohswallPD,biogenesisSuhH,MartoJA, ismediatedbySEDSandPBPpolymerasefamilies
functioningsemi-autonomously.NatMicrobiol2016:16172.
These papers [48,49] demonstrated futile cycling of peptidoglycan pre-cursors in response to different b-lactamantibiotics, carefully applying antibiotics to probe aspects of cell physiology.
50. HardsK,RobsonJR,BerneyM,ShawL,BaldD,KoulA,AndriesK, CookGM:Bactericidalmodeofactionofbedaquiline.J AntimicrobChemother2015,70:2028-2037.
51. RaoSP,AlonsoS,RandL,DickT,PetheK:The protonmotiveforceisrequiredformaintainingATP homeostasisandviabilityofhypoxic,nonreplicating Mycobacteriumtuberculosis.ProcNatlAcadSciUSA2008, 105:11945-11950.
52. MokWW,ParkJO,RabinowitzJD,BrynildsenMP:RNAfutile cyclinginmodelpersistersderivedfromMazFaccumulation. MBio2015,6e01588-01515.
53.
AdolfsentowardoxidativeKJ,BrynildsenstressMP:inEscherichiaFutilecyclingcoli.increasesMetabEngsensitivity2015, 29:26-35.
Thispaperdemonstratesthatmetabolicfutilecyclingincreasesoxygen consumptionandROSproductionanddecreasesintracellularATP.The netresultisanintracellularphenotypesimilartoantibiotic-treatedcells that exhibits increased sensitivity to oxidative stress.
54. ScottM,KlumppS,MateescuEM,HwaT:Emergenceofrobust growthlawsfromoptimalregulationofribosomesynthesis. MolSystBiol2014,10:747.
55. BasanM,HuiS,OkanoH,ZhangZ,ShenY,WilliamsonJR,HwaT: OverflowmetabolisminEscherichiacoliresultsfromefficient proteomeallocation.Nature2015,528:99-104.
56.
LevinnumbersBR,McCallgame:IC,ribosomePerrotV,densities,WeissH,Ovesepianbacterialgrowth,A,BaqueroandF:A antibiotic-mediatedstasisanddeath.MBio2017,8.
Thispaperdemonstratesthatantibioticlethalityisassociatedwiththe number of functionalribosomal units for antibiotics inhibiting protein translationandthatbacteriostaticantibioticscan belethalinbacteria depletedforribosomaloperons.
57. RussellJB,CookGM:Energeticsofbacterialgrowth:balance ofanabolicandcatabolicreactions.MicrobiolRev1995,59: 48-62.
58. LiuA,TranL,BecketE,LeeK,ChinnL,ParkE,TranK,MillerJH: AntibioticsensitivityprofilesdeterminedwithanEscherichia coligeneknockoutcollection:generatinganantibioticbar code.AntimicrobAgentsChemother2010,54:1393-1403.
59. NicholsRJ,SenS,ChooYJ,BeltraoP,ZietekM,ChabaR,LeeS, KazmierczakKM,LeeKJ,WongAetal.:Phenotypiclandscape ofabacterialcell.Cell2011,144:143-156.
60.
MathieuVizueteP,A,SongFleurierX,MaticS,FrenoyI:DiscoveryA,DairouandJ,BredechefunctionMF,ofaSanchez-general corehormeticstressresponseinE.coliinducedbysublethal concentrationsofantibiotics.CellRep2016,17:46-57.
This paper demonstrates that sublethal treatment with antibiotics of diverseclassesinduceacommon,generalstressresponsethatincreases metabolismandaltersmacromolecularphysiologicalprocessesusinga genepromoter–reportersystem.
61. AlamMK,AlhhazmiA,DeCoteauJF,LuoY,GeyerCR:RecA inhibitorspotentiateantibioticactivityandblockevolutionof antibioticresistance.CellChemBiol2016,23:381-391.
62. NautiyalA,PatilKN,MuniyappaK:Suraminisapotentand selectiveinhibitorofMycobacteriumtuberculosisRecA proteinandtheSOSresponse:RecAasapotentialtargetfor antibacterialdrugdiscovery.JAntimicrobChemother2014, 69:1834-1843.
63. LongQ,DuQ,FuT,DrlicaK,ZhaoX,XieJ:Involvementof Hollidayjunctionresolvaseinfluoroquinolone-mediated killingofMycobacteriumsmegmatis.AntimicrobAgents Chemother2015,59:1782-1785.
64. TakahashiN,GruberCC,YangJH,LiuX,BraffD,YashaswiniC, BhubhaniS,FurutaY,AndreescuS,CollinsJJetal.:Lethalityof MalE–LacZhybridproteinsharesmechanisticattributeswith oxidativecomponentofantibioticlethality.ProcNatlAcadSci USA2017,114:9164-9169http://dx.doi.org/10.1073/
pnas.1707466114Thispaperinvestigatesactivedeathprocesses followingstressresponseinductioninanantibiotic-freesystem usingahistoricallyimportantfusionproteinthatsharesseveral hallmarkswithantibioticlethality..
65. StorzG,HenggeR:AmericanSocietyforMicrobiology:Bacterial StressResponses.edn2.ASMPress;2011.
66. AmatoSM,BrynildsenMP:Persisterheterogeneityarisingfrom asinglemetabolicstress.CurrBiol2015,25:2090-2098.
67.
RadzikowskiHeinemannM:JL,BacterialVedelaarpersistenceS,SiegelD,isOrtegaanactiveAD,sigmaSSchmidtstressA, responsetometabolicfluxlimitation.MolSystBiol2016, 12:882.
Thispaperintegratedproteomicandmetabolomicapproachesto char-acterizepersisterphenotypesinducedbynutrienttransitions. 68. KolpenM,MousaviN,SamsT,BjarnsholtT,CiofuO,MoserC,
KuhlM,HoibyN,JensenPO:Reinforcementofthebactericidal
effectofciprofloxacinonPseudomonasaeruginosabiofilmby hyperbaricoxygentreatment.IntJAntimicrobAgents2016, 47:163-167.
69.
ChevereauinteractionG,mechanisms.BollenbachT:MolSystematicSystBiol2015,discovery11:807.ofdrug
Thispaper modeledtheexpectedgrowth rateofE. colisingle gene deletion mutants challenged with antibiotic combinations to identify genes and corresponding cellular functions involved in drug interactions. 70. ZemkeAC,KocakBR,BombergerJM:Sodiumnitriteinhibits
killingofPseudomonasaeruginosabiofilmsbyciprofloxacin. AntimicrobAgentsChemother2017,61.
71. AllisonKR,BrynildsenMP,CollinsJJ:Metabolite-enabled eradicationofbacterialpersistersbyaminoglycosides.Nature 2011,473:216-220.
72. BartekIL,ReichlenMJ,HonakerRW,LeistikowRL,ClambeyET, ScobeyMS,HindsAB,BornSE,CoveyCR,SchurrMJetal.: AntibioticbactericidalactivityiscounteredbymaintainingpH homeostasisinMycobacteriumsmegmatis.mSphere2016,1.
73. VarikV,OliveiraSR,HauryliukV,TensonT:Compositionofthe outgrowthmediummodulateswake-upkineticsand ampicillinsensitivityofstringentandrelaxedEscherichiacoli. SciRep2016,6:22308.
74. BaileyAM,WebberMA,PiddockLJ:Mediumplaysarolein determiningexpressionofacrB,marA,andsoxSin Escherichiacoli.AntimicrobAgentsChemother2006,50: 1071-1074.
75. PengB,SuYB,LiH,HanY,GuoC,TianYM,PengXX:Exogenous alanineand/orglucosepluskanamycinkills antibiotic-resistantbacteria.CellMetab2015,21:249-261.
76. ShanY,LazinskiD,RoweS,CamilliA,LewisK:Geneticbasisof persistertolerancetoaminoglycosidesinEscherichiacoli. MBio2015,6.
77.
CoteBrownJP,ED:FrenchThegenome-wideS,GehrkeSS,MacNairinteractionCR,MangatnetworkCS,ofBharatnutrientA, stressgenesinEscherichiacoli.MBio2016,7.
This paper demonstrates that nutrient stress can yield synthetic lethal interactions with several metabolic processes.
78.
VilchezeLeungLW,C,FreundlichHartmanT,JS,WeinrickJacobsB,WRJainJr:P,EnhancedWeisbrodrespirationTR, preventsdrugtoleranceanddrugresistancein
Mycobacteriumtuberculosis.ProcNatlAcadSciUSA2017, 114:4495-4500.
Thispaper demonstratesthat metabolicaminoacid supplementation maypreventtheemergenceofantibioticresistanceinM.tuberculosis. 79. ParkS,ImlayJA:Highlevelsofintracellularcysteinepromote
oxidativeDNAdamagebydrivingthefentonreaction.J Bacteriol2003,185:1942-1950.
80. HartmanT,WeinrickB,VilchezeC,BerneyM,TufarielloJ, CookGM,Jacobs WRJr:Succinatedehydrogenaseisthe regulatorofrespirationinMycobacteriumtuberculosis.PLoS Pathog2014,10:e1004510.
81. BraunerA,FridmanO,GefenO,BalabanNQ:Distinguishing betweenresistance,toleranceandpersistencetoantibiotic treatment.NatRevMicrobiol2016,14:320-330.
82. PuY,ZhaoZ,LiY,ZouJ,MaQ,ZhaoY,KeY,ZhuY,ChenH, BakerMAetal.:Enhancedeffluxactivityfacilitatesdrug toleranceindormantbacterialcells.MolCell2016,62:284-294.
83. HarmsA,MaisonneuveE,GerdesK:Mechanismsofbacterial persistenceduringstressandantibioticexposure.Science 2016,354Thisisagoodrecentreviewonmechanismsofpersister formationinvolvingtoxin–antitoxinsystemsandthestringent response..
84. CohenNR,LobritzMA,CollinsJJ:Microbialpersistenceandthe roadtodrugresistance.CellHostMicrobe2013,13:632-642.
85. ShanY,BrownGandtA,RoweSE,DeisingerJP,ConlonBP, LewisK:ATP-dependentpersisterformationinEscherichia coli.MBio2017,8SeeannotationtoRef.[86]..
86. ConlonBP,RoweSE,GandtAB,NuxollAS,DoneganNP,ZalisEA, ClairG,AdkinsJN,CheungAL,LewisK:Persisterformationin
StaphylococcusaureusisassociatedwithATPdepletion.Nat Microbiol2016,1:16051Thesetwopapers[85,86]demonstrate thattheformationofpersistercellscanbeindependentoftoxin– antitoxininductionanddependsonintracellularATPlevels..
87. WuML,GengenbacherM,DickT:Mildnutrientstarvation triggersthedevelopmentofasmall-cellsurvivalmorphotype inMycobacteria.FrontMicrobiol2016,7:947.
88. VegaNM,AllisonKR,KhalilAS,CollinsJJ:Signaling-mediated bacterialpersisterformation.NatChemBiol2012,8:431-433.
89. VegaNM,AllisonKR,SamuelsAN,KlempnerMS,CollinsJJ: SalmonellatyphimuriuminterceptsEscherichiacolisignaling toenhanceantibiotictolerance.ProcNatlAcadSciUSA2013, 110:14420-14425.
90. VerstraetenN,KnapenWJ,KintCI,LiebensV,VandenBerghB, DewachterL,MichielsJE,FuQ,DavidCC,FierroACetal.:Obg andmembranedepolarizationarepartofamicrobial bet-hedgingstrategythatleadstoantibiotictolerance.MolCell 2015,59:9-21.
91. DorrT,VulicM,LewisK:Ciprofloxacincausespersister formationbyinducingtheTisBtoxininEscherichiacoli.PLoS Biol2010,8:e1000317.
92. MaisonneuveE,Castro-CamargoM,GerdesK:(p)ppGpp controlsbacterialpersistencebystochasticinductionof toxin–antitoxinactivity.Cell2013,154:1140-1150.
93. GrimbergenAJ,SiebringJ,SolopovaA,KuipersOP:Microbial bet-hedging:thepowerofbeingdifferent.CurrOpinMicrobiol 2015,25:67-72.
94. HelaineS,ChevertonAM,WatsonKG,FaureLM,MatthewsSA, HoldenDW:InternalizationofSalmonellabymacrophages inducesformationofnonreplicatingpersisters.Science2014, 343:204-208.
95. ManinaG,DharN,McKinneyJD:Stressandhostimmunity amplifyMycobacteriumtuberculosisphenotypic
heterogeneityandinducenongrowingmetabolicallyactive forms.CellHostMicrobe2015,17:32-46.
96. Levin-ReismanI,RoninI,GefenO,BranissI,ShoreshN, BalabanNQ:Antibiotictolerancefacilitatestheevolutionof resistance.Science2017,355:826-830.
97. VandenBerghB,MichielsJE,WenseleersT,WindelsEM, BoerPV,KestemontD,DeMeesterL,VerstrepenKJ, VerstraetenN,FauvartMetal.:Frequencyofantibiotic applicationdrivesrapidevolutionaryadaptationof Escherichiacolipersistence.NatMicrobiol2016,1:16020.
98. ArtemovaT,GerardinY,DudleyC,VegaNM,GoreJ:Isolatedcell behaviordrivestheevolutionofantibioticresistance.MolSyst Biol2015,11:822.
99. ThompsonJA,OliveiraRA,DjukovicA,UbedaC,XavierKB: ManipulationofthequorumsensingsignalAI-2affectsthe antibiotic-treatedgutmicrobiota.CellRep2015,10:1861-1871.
100.LeeHH,MollaMN,CantorCR,CollinsJJ:Bacterialcharitywork leadstopopulation-wideresistance.Nature2010,467:82-85.
101.YurtsevEA,ConwillA,GoreJ:Oscillatorydynamicsina bacterialcross-protectionmutualism.ProcNatlAcadSciUSA 2016,113:6236-6241.
102.MeredithHR,SrimaniJK,LeeAJ,LopatkinAJ,YouL:Collective antibiotictolerance:mechanisms,dynamicsandintervention. NatChemBiol2015,11:182-188.
103.VegaNM,GoreJ:Collectiveantibioticresistance:mechanisms andimplications.CurrOpinMicrobiol2014,21:28-34.
104.BollenbachT:Antimicrobialinteractions:mechanismsand implicationsfordrugdiscoveryandresistanceevolution.Curr OpinMicrobiol2015,27:1-9.
105.
MitoschstresspredictsK,RieckhsubsequentG,Bollenbachsingle-cellT:Noisysurvivalresponseintoanantibioticacidic environment.CellSyst2017,4:393-403.e5.
This paper used a promoter–reporter screen to identify a temporal acid stress response to trimethoprim with implications for drug mechanism of action and antibiotic-induced cross-protection against other environ-mental stressors.
106.PalC,PappB,LazarV:Collateralsensitivityof antibiotic-resistantmicrobes.TrendsMicrobiol2015,23:401-407.
107.KimS,LiebermanTD,KishonyR:Alternatingantibiotic treatmentsconstrainevolutionarypathstomultidrug resistance.ProcNatlAcadSciUSA2014,111:14494-14499.
108.ImamovicL,SommerMO:Useofcollateralsensitivitynetworks todesigndrugcyclingprotocolsthatavoidresistance development.SciTranslMed2013,5:204ra132.
109.
ZampieriMetabolicM,constraintsEnkeT,ChubukovontheevolutionV,RicciV,ofPiddockantibioticL,SauerU: resistance.MolSystBiol2017,13:917.
Thispapercombinedexperimentalevolutionandmetabolomicsto char-acterizehowmetabolicfluxesshapebacterialadaptationtoantibiotic stress.
110.PrideauxB,ViaLE,ZimmermanMD,EumS,SarathyJ,O’BrienP, ChenC,KayaF,WeinerDM,ChenPYetal.:Theassociation betweensterilizingactivityanddrugdistributioninto tuberculosislesions.NatMed2015,21:1223-1227.
111.
FlynnPienaarJL,E,KirschnerCilfoneNA,DE,LinLindermanPL,DartoisJJ:V,AMattilacomputationalJT,ButlertoolJR, integratinghostimmunitywithantibioticdynamicstostudy tuberculosistreatment.JTheorBiol2015,367:166-179.
Thispaper integratesspatially resolvedmassspectrometrydatawith mathematicalmodels of antibioticsusceptibility to predict treatment outcomes on the complex in vivo environments of M. tuberculosis granulomas.
112.CameronDE,CollinsJJ:Tunableproteindegradationin bacteria.NatBiotechnol2014,32:1276-1281.
113.
AbelHinkleyZurT,WieschMagnusP,C,AbelWaldorS,GkotzisMK,UdekwuS,OcampoK,CohenP,EngelstadterT:ClassicJ, reactionkineticscanexplaincomplexpatternsofantibiotic action.SciTranslMed2015,7:287ra273.
Thispaperdemonstratesthatsimplemathematicalmodelscanexplain severalcomplexfeaturesofantibiotickillingdynamics.
114.ChandrasekaranS,Cokol-CakmakM,SahinN,YilanciogluK, KazanH,CollinsJJ,CokolM:Chemogenomicsand orthology-baseddesignofantibioticcombinationtherapies.MolSystBiol 2016,12:872Thispaperappliesamachinelearningalgorthimto chemogenomicscreeningdatatopredictoutcomesfrom treatmentswithantibioticcombinations..
115.WildenhainJ,SpitzerM,DolmaS,JarvikN,WhiteR,RoyM, GriffithsE,BellowsDS,WrightGD,TyersM:Predictionof synergismfromchemical–geneticinteractionsbymachine learning.CellSyst2015,1:383-395.
116.AzizRK,MonkJM,LewisRM,InLohS,MishraA,AbhayNagleA, SatyanarayanaC,DhakshinamoorthyS,LucheM,KitchenDB etal.:Systemsbiology-guidedidentificationofsyntheticlethal genepairsanditspotentialusetodiscoverantibiotic combinations.SciRep2015,5:16025.
117.KruegerAS,MunckC,DantasG,ChurchGM,GalaganJ,LeharJ, SommerMO:Simulatingserial-targetantibacterialdrug synergiesusingfluxbalanceanalysis.PLoSOne2016,11: e0147651.