---
_id: '8657'
abstract:
- lang: eng
text: "Synthesis of proteins – translation – is a fundamental process of life. Quantitative
studies anchor translation into the context of bacterial physiology and reveal
several mathematical relationships, called “growth laws,” which capture physiological
feedbacks between protein synthesis and cell growth. Growth laws describe the
dependency of the ribosome abundance as a function of growth rate, which can change
depending on the growth conditions. Perturbations of translation reveal that bacteria
employ a compensatory strategy in which the reduced translation capability results
in increased expression of the translation machinery.\r\nPerturbations of translation
are achieved in various ways; clinically interesting is the application of translation-targeting
antibiotics – translation inhibitors. The antibiotic effects on bacterial physiology
are often poorly understood. Bacterial responses to two or more simultaneously
applied antibiotics are even more puzzling. The combined antibiotic effect determines
the type of drug interaction, which ranges from synergy (the effect is stronger
than expected) to antagonism (the effect is weaker) and suppression (one of the
drugs loses its potency).\r\nIn the first part of this work, we systematically
measure the pairwise interaction network for translation inhibitors that interfere
with different steps in translation. We find that the interactions are surprisingly
diverse and tend to be more antagonistic. To explore the underlying mechanisms,
we begin with a minimal biophysical model of combined antibiotic action. We base
this model on the kinetics of antibiotic uptake and binding together with the
physiological response described by the growth laws. The biophysical model explains
some drug interactions, but not all; it specifically fails to predict suppression.\r\nIn
the second part of this work, we hypothesize that elusive suppressive drug interactions
result from the interplay between ribosomes halted in different stages of translation.
To elucidate this putative mechanism of drug interactions between translation
inhibitors, we generate translation bottlenecks genetically using in- ducible
control of translation factors that regulate well-defined translation cycle steps.
These perturbations accurately mimic antibiotic action and drug interactions,
supporting that the interplay of different translation bottlenecks partially causes
these interactions.\r\nWe extend this approach by varying two translation bottlenecks
simultaneously. This approach reveals the suppression of translocation inhibition
by inhibited translation. We rationalize this effect by modeling dense traffic
of ribosomes that move on transcripts in a translation factor-mediated manner.
This model predicts a dissolution of traffic jams caused by inhibited translocation
when the density of ribosome traffic is reduced by lowered initiation. We base
this model on the growth laws and quantitative relationships between different
translation and growth parameters.\r\nIn the final part of this work, we describe
a set of tools aimed at quantification of physiological and translation parameters.
We further develop a simple model that directly connects the abundance of a translation
factor with the growth rate, which allows us to extract physiological parameters
describing initiation. We demonstrate the development of tools for measuring translation
rate.\r\nThis thesis showcases how a combination of high-throughput growth rate
mea- surements, genetics, and modeling can reveal mechanisms of drug interactions.
Furthermore, by a gradual transition from combinations of antibiotics to precise
genetic interventions, we demonstrated the equivalency between genetic and chemi-
cal perturbations of translation. These findings tile the path for quantitative
studies of antibiotic combinations and illustrate future approaches towards the
quantitative description of translation."
acknowledged_ssus:
- _id: LifeSc
- _id: M-Shop
acknowledgement: I thank Life Science Facilities for their continuous support with
providing top-notch laboratory materials, keeping the devices humming, and coordinating
the repairs and building of custom-designed laboratory equipment with the MIBA Machine
shop.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
citation:
ama: 'Kavcic B. Perturbations of protein synthesis: from antibiotics to genetics
and physiology. 2020. doi:10.15479/AT:ISTA:8657'
apa: 'Kavcic, B. (2020). Perturbations of protein synthesis: from antibiotics
to genetics and physiology. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8657'
chicago: 'Kavcic, Bor. “Perturbations of Protein Synthesis: From Antibiotics to
Genetics and Physiology.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8657.'
ieee: 'B. Kavcic, “Perturbations of protein synthesis: from antibiotics to genetics
and physiology,” Institute of Science and Technology Austria, 2020.'
ista: 'Kavcic B. 2020. Perturbations of protein synthesis: from antibiotics to genetics
and physiology. Institute of Science and Technology Austria.'
mla: 'Kavcic, Bor. Perturbations of Protein Synthesis: From Antibiotics to Genetics
and Physiology. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8657.'
short: 'B. Kavcic, Perturbations of Protein Synthesis: From Antibiotics to Genetics
and Physiology, Institute of Science and Technology Austria, 2020.'
date_created: 2020-10-13T16:46:14Z
date_published: 2020-10-14T00:00:00Z
date_updated: 2023-09-07T13:20:48Z
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status: public
supervisor:
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full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
title: 'Perturbations of protein synthesis: from antibiotics to genetics and physiology'
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2020'
...
---
_id: '6392'
abstract:
- lang: eng
text: "The regulation of gene expression is one of the most fundamental processes
in living systems. In recent years, thanks to advances in sequencing technology
and automation, it has become possible to study gene expression quantitatively,
genome-wide and in high-throughput. This leads to the possibility of exploring
changes in gene expression in the context of many external perturbations and their
combinations, and thus of characterising the basic principles governing gene regulation.
In this thesis, I present quantitative experimental approaches to studying transcriptional
and protein level changes in response to combinatorial drug treatment, as well
as a theoretical data-driven approach to analysing thermodynamic principles guiding
transcription of protein coding genes. \r\nIn the first part of this work, I
present a novel methodological framework for quantifying gene expression changes
in drug combinations, termed isogrowth profiling. External perturbations through
small molecule drugs influence the growth rate of the cell, leading to wide-ranging
changes in cellular physiology and gene expression. This confounds the gene expression
changes specifically elicited by the particular drug. Combinatorial perturbations,
owing to the increased stress they exert, influence the growth rate even more
strongly and hence suffer the convolution problem to a greater extent when measuring
gene expression changes. Isogrowth profiling is a way to experimentally abstract
non-specific, growth rate related changes, by performing the measurement using
varying ratios of two drugs at such concentrations that the overall inhibition
rate is constant. Using a robotic setup for automated high-throughput re-dilution
culture of Saccharomyces cerevisiae, the budding yeast, I investigate all pairwise
interactions of four small molecule drugs through sequencing RNA along a growth
isobole. Through principal component analysis, I demonstrate here that isogrowth
profiling can uncover drug-specific as well as drug-interaction-specific gene
expression changes. I show that drug-interaction-specific gene expression changes
can be used for prediction of higher-order drug interactions. I propose a simplified
generalised framework of isogrowth profiling, with few measurements needed for
each drug pair, enabling the broad application of isogrowth profiling to high-throughput
screening of inhibitors of cellular growth and beyond. Such high-throughput screenings
of gene expression changes specific to pairwise drug interactions will be instrumental
for predicting the higher-order interactions of the drugs.\r\n\r\nIn the second
part of this work, I extend isogrowth profiling to single-cell measurements of
gene expression, characterising population heterogeneity in the budding yeast
in response to combinatorial drug perturbation while controlling for non-specific
growth rate effects. Through flow cytometry of strains with protein products fused
to green fluorescent protein, I discover multiple proteins with bi-modally distributed
expression levels in the population in response to drug treatment. I characterize
more closely the effect of an ionic stressor, lithium chloride, and find that
it inhibits the splicing of mRNA, most strongly affecting ribosomal protein transcripts
and leading to a bi-stable behaviour of a small ribosomal subunit protein Rps22B.
Time-lapse microscopy of a microfluidic culture system revealed that the induced
Rps22B heterogeneity leads to preferential survival of Rps22B-low cells after
long starvation, but to preferential proliferation of Rps22B-high cells after
short starvation. Overall, this suggests that yeast cells might use splicing of
ribosomal genes for bet-hedging in fluctuating environments. I give specific examples
of how further exploration of cellular heterogeneity in yeast in response to external
perturbation has the potential to reveal yet-undiscovered gene regulation circuitry.\r\n\r\nIn
the last part of this thesis, a re-analysis of a published sequencing dataset
of nascent elongating transcripts is used to characterise the thermodynamic constraints
for RNA polymerase II (RNAP) elongation. Population-level data on RNAP position
throughout the transcribed genome with single nucleotide resolution are used to
infer the sequence specific thermodynamic determinants of RNAP pausing and backtracking.
This analysis reveals that the basepairing strength of the eight nucleotide-long
RNA:DNA duplex relative to the basepairing strength of the same sequence when
in DNA:DNA duplex, and the change in this quantity during RNA polymerase movement,
is the key determinant of RNAP pausing. This is true for RNAP pausing while elongating,
but also of RNAP pausing while backtracking and of the backtracking length. The
quantitative dependence of RNAP pausing on basepairing energetics is used to infer
the increase in pausing due to transcriptional mismatches, leading to a hypothesis
that pervasive RNA polymerase II pausing is due to basepairing energetics, as
an evolutionary cost for increased RNA polymerase II fidelity.\r\n\r\nThis work
advances our understanding of the general principles governing gene expression,
with the goal of making computational predictions of single-cell gene expression
responses to combinatorial perturbations based on the individual perturbations
possible. This ability would substantially facilitate the design of drug combination
treatments and, in the long term, lead to our increased ability to more generally
design targeted manipulations to any biological system. "
acknowledged_ssus:
- _id: LifeSc
- _id: M-Shop
- _id: Bio
alternative_title:
- IST Austria Thesis
author:
- first_name: Martin
full_name: Lukacisin, Martin
id: 298FFE8C-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisin
orcid: 0000-0001-6549-4177
citation:
ama: Lukacisin M. Quantitative investigation of gene expression principles through
combinatorial drug perturbation and theory. 2019. doi:10.15479/AT:ISTA:6392
apa: Lukacisin, M. (2019). Quantitative investigation of gene expression principles
through combinatorial drug perturbation and theory. IST Austria. https://doi.org/10.15479/AT:ISTA:6392
chicago: Lukacisin, Martin. “Quantitative Investigation of Gene Expression Principles
through Combinatorial Drug Perturbation and Theory.” IST Austria, 2019. https://doi.org/10.15479/AT:ISTA:6392.
ieee: M. Lukacisin, “Quantitative investigation of gene expression principles through
combinatorial drug perturbation and theory,” IST Austria, 2019.
ista: Lukacisin M. 2019. Quantitative investigation of gene expression principles
through combinatorial drug perturbation and theory. IST Austria.
mla: Lukacisin, Martin. Quantitative Investigation of Gene Expression Principles
through Combinatorial Drug Perturbation and Theory. IST Austria, 2019, doi:10.15479/AT:ISTA:6392.
short: M. Lukacisin, Quantitative Investigation of Gene Expression Principles through
Combinatorial Drug Perturbation and Theory, IST Austria, 2019.
date_created: 2019-05-09T19:53:00Z
date_published: 2019-05-09T00:00:00Z
date_updated: 2023-09-22T09:19:41Z
day: '09'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.15479/AT:ISTA:6392
extern: '1'
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date_created: 2019-05-10T13:51:49Z
date_updated: 2020-07-14T12:47:29Z
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publication_identifier:
isbn:
- 978-3-99078-001-5
issn:
- 2663-337X
publication_status: published
publisher: IST Austria
related_material:
record:
- id: '1029'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
title: Quantitative investigation of gene expression principles through combinatorial
drug perturbation and theory
type: dissertation
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...
---
_id: '6263'
abstract:
- lang: eng
text: 'Antibiotic resistance can emerge spontaneously through genomic mutation and render
treatment ineffective. To counteract this process, in addition to the discovery and
description of resistance mechanisms,a deeper understanding of resistanceevolvabilityand
its determinantsis needed. To address this challenge, this thesisuncoversnew genetic
determinants of resistance evolvability using a customized robotic setup,
exploressystematic ways in which resistance evolution is perturbed due to
dose-responsecharacteristics of drugs and mutation rate differences,and mathematically investigates
the evolutionary fate of one specific type of evolvability modifier -a stress-induced
mutagenesis allele.We find severalgenes which strongly inhibit or potentiate resistance evolution. In order
to identify them, we first developedan automated high-throughput feedback-controlled
protocol whichkeeps the population size and selection pressure approximately constant
for hundreds of cultures by dynamically re-diluting the cultures and adjusting the antibiotic
concentration. We implementedthis protocol on a customized liquid handling robot and
propagated 100 different gene deletion strains of Escherichia coliin triplicate for over 100
generations in tetracycline and in chloramphenicol, and comparedtheir adaptation rates.We find a diminishing returns pattern, where initially sensitive strains adapted more
compared to less sensitive ones. Our data uncover that deletions of certain genes
which do not affect mutation rate,including efflux pump components, a chaperone and
severalstructural and regulatory genes can strongly and reproducibly alterresistance evolution.
Sequencing analysis of evolved populations indicates that epistasis with resistance
mutations is the most likelyexplanation. This work could inspire treatment strategies in
which targeted inhibitors of evolvability mechanisms will be given alongside antibiotics to
slow down resistance evolution and extend theefficacy of antibiotics.We implemented astochasticpopulation genetics model,
toverifyways in which general properties, namely, dose-response characteristics of drugs and mutation rates, influence
evolutionary dynamics. In particular, under the exposure to antibiotics with shallow dose-response curves,bacteria have narrower distributions of fitness effects of new mutations.
We show that in silicothis also leads to slower resistance evolution. We
see and confirm with experiments that increased mutation rates, apart from speeding
up evolution, also leadto high reproducibility of phenotypic adaptation in a context
of continually strong selection pressure.Knowledge of these patterns can aid in predicting the dynamics of antibiotic
resistance evolutionand adapting treatment schemes accordingly.Focusing on a previously described type of evolvability modifier
–a stress-induced mutagenesis allele –we find conditions under which it can persist in a population under
periodic selectionakin to clinical treatment. We set up a deterministic
infinite populationcontinuous time model tracking the frequencies of a mutator and resistance allele and
evaluate various treatment schemes in how well they maintain a stress-induced
mutator allele. In particular,a high diversity of stresses is crucial for the persistence
of the mutator allele. This leads to a general trade-off where exactly those
diversifying treatment schemes which are likely to decrease levels of resistance could lead to stronger selection of highly
evolvable genotypes.In the long run, this work will lead to a deeper understanding of the genetic and cellular
mechanisms involved in antibiotic resistance evolution and could inspire new strategies
for slowing down its rate. '
acknowledged_ssus:
- _id: M-Shop
- _id: LifeSc
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
citation:
ama: Lukacisinova M. Genetic determinants of antibiotic resistance evolution. 2018.
doi:10.15479/AT:ISTA:th1072
apa: Lukacisinova, M. (2018). Genetic determinants of antibiotic resistance evolution.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th1072
chicago: Lukacisinova, Marta. “Genetic Determinants of Antibiotic Resistance Evolution.”
Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:th1072.
ieee: M. Lukacisinova, “Genetic determinants of antibiotic resistance evolution,”
Institute of Science and Technology Austria, 2018.
ista: Lukacisinova M. 2018. Genetic determinants of antibiotic resistance evolution.
Institute of Science and Technology Austria.
mla: Lukacisinova, Marta. Genetic Determinants of Antibiotic Resistance Evolution.
Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th1072.
short: M. Lukacisinova, Genetic Determinants of Antibiotic Resistance Evolution,
Institute of Science and Technology Austria, 2018.
date_created: 2019-04-09T13:57:15Z
date_published: 2018-12-28T00:00:00Z
date_updated: 2023-09-22T09:20:37Z
day: '28'
ddc:
- '570'
- '576'
- '579'
degree_awarded: PhD
department:
- _id: ToBo
doi: 10.15479/AT:ISTA:th1072
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status: public
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relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
title: Genetic determinants of antibiotic resistance evolution
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '818'
abstract:
- lang: eng
text: 'Antibiotics have diverse effects on bacteria, including massive changes in
bacterial gene expression. Whereas the gene expression changes under many antibiotics
have been measured, the temporal organization of these responses and their dependence
on the bacterial growth rate are unclear. As described in Chapter 1, we quantified
the temporal gene expression changes in the bacterium Escherichia coli in response
to the sudden exposure to antibiotics using a fluorescent reporter library and
a robotic system. Our data show temporally structured gene expression responses,
with response times for individual genes ranging from tens of minutes to several
hours. We observed that many stress response genes were activated in response
to antibiotics. As certain stress responses cross-protect bacteria from other
stressors, we then asked whether cellular responses to antibiotics have a similar
protective role in Chapter 2. Indeed, we found that the trimethoprim-induced acid
stress response protects bacteria from subsequent acid stress. We combined microfluidics
with time-lapse imaging to monitor survival, intracellular pH, and acid stress
response in single cells. This approach revealed that the variable expression
of the acid resistance operon gadBC strongly correlates with single-cell survival
time. Cells with higher gadBC expression following trimethoprim maintain higher
intracellular pH and survive the acid stress longer. Overall, we provide a way
to identify single-cell cross-protection between antibiotics and environmental
stressors from temporal gene expression data, and show how antibiotics can increase
bacterial fitness in changing environments. While gene expression changes to antibiotics
show a clear temporal structure at the population-level, it is unclear whether
this clear temporal order is followed by every single cell. Using dual-reporter
strains described in Chapter 3, we measured gene expression dynamics of promoter
pairs in the same cells using microfluidics and microscopy. Chapter 4 shows that
the oxidative stress response and the DNA stress response showed little timing
variability and a clear temporal order under the antibiotic nitrofurantoin. In
contrast, the acid stress response under trimethoprim ran independently from all
other activated response programs including the DNA stress response, which showed
particularly high timing variability in this stress condition. In summary, this
approach provides insight into the temporal organization of gene expression programs
at the single-cell level and suggests dependencies between response programs and
the underlying variability-introducing mechanisms. Altogether, this work advances
our understanding of the diverse effects that antibiotics have on bacteria. These
results were obtained by taking into account gene expression dynamics, which allowed
us to identify general principles, molecular mechanisms, and dependencies between
genes. Our findings may have implications for infectious disease treatments, and
microbial communities in the human body and in nature. '
acknowledgement: 'First of all, I would like to express great gratitude to my PhD
supervisor Tobias Bollenbach. Through his open and trusting attitude I had the freedom
to explore different scientific directions during this project, and follow the research
lines of my interest. I am thankful for constructive and often extensive discussions
and his support and commitment during the different stages of my PhD. I want to
thank my committee members, Călin Guet, Terry Hwa and Nassos Typas for their interest
and their valuable input to this project. Special thanks to Nassos for career guidance,
and for accepting me in his lab. A big thank you goes to the past, present and affiliated
members of the Bollenbach group: Guillaume Chevereau, Marjon de Vos, Marta Lukačišinová,
Veronika Bierbaum, Qi Qin, Marcin Zagórski, Martin Lukačišin, Andreas Angermayr,
Bor Kavčič, Julia Tischler, Dilay Ayhan, Jaroslav Ferenc, and Georg Rieckh. I enjoyed
working and discussing with you very much and I will miss our lengthy group meetings,
our inspiring journal clubs, and our common lunches. Special thanks to Bor for great
mental and professional support during the hard months of thesis writing, and to
Marta for very creative times during the beginning of our PhDs. May the ‘Bacterial
Survival Guide’ decorate the walls of IST forever! A great thanks to my friend and
collaborator Georg Rieckh for his enthusiasm and for getting so involved in these
projects, for his endurance and for his company throughout the years. Thanks to
the FriSBi crowd at IST Austria for interesting meetings and discussions. In particular
I want to thank Magdalena Steinrück, and Anna Andersson for inspiring exchange,
and enjoyable time together. Thanks to everybody who contributed to the cover for
Cell Systems: The constructive input from Tobias Bollenbach, Bor Kavčič, Georg Rieckh,
Marta Lukačišinová, and Sebastian Nozzi, and the professional implementation by
the graphic designer Martina Markus from the University of Cologne. Thanks to all
my office mates in the first floor Bertalanffy building throughout the years: for
ensuring a pleasant working atmosphere, and for your company! In general, I want
to thank all the people that make IST such a great environment, with the many possibilities
to shape our own social and research environment. I want to thank my family for
all kind of practical support during the years, and my second family in Argentina
for their enthusiasm. Thanks to my brother Bernhard and my sister Martina for being
great siblings, and to Helena and Valentin for the joy you brought to my life. My
deep gratitude goes to Sebastian Nozzi, for constant support, patience, love and
for believing in me. '
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Karin
full_name: Mitosch, Karin
id: 39B66846-F248-11E8-B48F-1D18A9856A87
last_name: Mitosch
citation:
ama: Mitosch K. Timing, variability and cross-protection in bacteria – insights
from dynamic gene expression responses to antibiotics. 2017. doi:10.15479/AT:ISTA:th_862
apa: Mitosch, K. (2017). Timing, variability and cross-protection in bacteria
– insights from dynamic gene expression responses to antibiotics. Institute
of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_862
chicago: Mitosch, Karin. “Timing, Variability and Cross-Protection in Bacteria –
Insights from Dynamic Gene Expression Responses to Antibiotics.” Institute of
Science and Technology Austria, 2017. https://doi.org/10.15479/AT:ISTA:th_862.
ieee: K. Mitosch, “Timing, variability and cross-protection in bacteria – insights
from dynamic gene expression responses to antibiotics,” Institute of Science and
Technology Austria, 2017.
ista: Mitosch K. 2017. Timing, variability and cross-protection in bacteria – insights
from dynamic gene expression responses to antibiotics. Institute of Science and
Technology Austria.
mla: Mitosch, Karin. Timing, Variability and Cross-Protection in Bacteria – Insights
from Dynamic Gene Expression Responses to Antibiotics. Institute of Science
and Technology Austria, 2017, doi:10.15479/AT:ISTA:th_862.
short: K. Mitosch, Timing, Variability and Cross-Protection in Bacteria – Insights
from Dynamic Gene Expression Responses to Antibiotics, Institute of Science and
Technology Austria, 2017.
date_created: 2018-12-11T11:48:40Z
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title: Timing, variability and cross-protection in bacteria – insights from dynamic
gene expression responses to antibiotics
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...