---
_id: '11341'
abstract:
- lang: eng
text: Intragenic regions that are removed during maturation of the RNA transcript—introns—are
universally present in the nuclear genomes of eukaryotes1. The budding yeast,
an otherwise intron-poor species, preserves two sets of ribosomal protein genes
that differ primarily in their introns2,3. Although studies have shed light on
the role of ribosomal protein introns under stress and starvation4,5,6, understanding
the contribution of introns to ribosome regulation remains challenging. Here,
by combining isogrowth profiling7 with single-cell protein measurements8, we show
that introns can mediate inducible phenotypic heterogeneity that confers a clear
fitness advantage. Osmotic stress leads to bimodal expression of the small ribosomal
subunit protein Rps22B, which is mediated by an intron in the 5′ untranslated
region of its transcript. The two resulting yeast subpopulations differ in their
ability to cope with starvation. Low levels of Rps22B protein result in prolonged
survival under sustained starvation, whereas high levels of Rps22B enable cells
to grow faster after transient starvation. Furthermore, yeasts growing at high
concentrations of sugar, similar to those in ripe grapes, exhibit bimodal expression
of Rps22B when approaching the stationary phase. Differential intron-mediated
regulation of ribosomal protein genes thus provides a way to diversify the population
when starvation threatens in natural environments. Our findings reveal a role
for introns in inducing phenotypic heterogeneity in changing environments, and
suggest that duplicated ribosomal protein genes in yeast contribute to resolving
the evolutionary conflict between precise expression control and environmental
responsiveness9.
acknowledged_ssus:
- _id: LifeSc
- _id: M-Shop
- _id: Bio
acknowledgement: We thank the IST Austria Life Science Facility, the Miba Machine
Shop and M. Lukačišinová for support with the liquid handling robot; the Bioimaging
Facility at IST Austria, J. Power and B. Meier at the University of Cologne, and
C. Göttlinger at the FACS Analysis Facility at the Institute for Genetics, University
of Cologne, for support with flow cytometry experiments; L. Horst for the development
of the automated experimental methods in Cologne; J. Parenteau, S. Abou Elela, G.
Stormo, M. Springer and M. Schuldiner for providing us with yeast strains; B. Fernando,
T. Fink, G. Ansmann and G. Chevreau for technical support; H. Köver, G. Tkačik,
N. Barton, A. Angermayr and B. Kavčič for support during laboratory relocation;
D. Siekhaus, M. Springer and all the members of the Bollenbach group for support
and discussions; and K. Mitosch, M. Lukačišinová, G. Liti and A. de Luna for critical
reading of our manuscript. This work was supported in part by an Austrian Science
Fund (FWF) standalone grant P 27201-B22 (to T.B.), HFSP program Grant RGP0042/2013
(to T.B.), EU Marie Curie Career Integration Grant No. 303507, and German Research
Foundation (DFG) Collaborative Research Centre (SFB) 1310 (to T.B.). A.E.-C. was
supported by a Georg Forster fellowship from the Alexander von Humboldt Foundation.
article_processing_charge: No
article_type: original
author:
- first_name: Martin
full_name: Lukacisin, Martin
id: 298FFE8C-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisin
orcid: 0000-0001-6549-4177
- first_name: Adriana
full_name: Espinosa-Cantú, Adriana
last_name: Espinosa-Cantú
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Lukacisin M, Espinosa-Cantú A, Bollenbach MT. Intron-mediated induction of
phenotypic heterogeneity. Nature. 2022;605:113-118. doi:10.1038/s41586-022-04633-0
apa: Lukacisin, M., Espinosa-Cantú, A., & Bollenbach, M. T. (2022). Intron-mediated
induction of phenotypic heterogeneity. Nature. Springer Nature. https://doi.org/10.1038/s41586-022-04633-0
chicago: Lukacisin, Martin, Adriana Espinosa-Cantú, and Mark Tobias Bollenbach.
“Intron-Mediated Induction of Phenotypic Heterogeneity.” Nature. Springer
Nature, 2022. https://doi.org/10.1038/s41586-022-04633-0.
ieee: M. Lukacisin, A. Espinosa-Cantú, and M. T. Bollenbach, “Intron-mediated induction
of phenotypic heterogeneity,” Nature, vol. 605. Springer Nature, pp. 113–118,
2022.
ista: Lukacisin M, Espinosa-Cantú A, Bollenbach MT. 2022. Intron-mediated induction
of phenotypic heterogeneity. Nature. 605, 113–118.
mla: Lukacisin, Martin, et al. “Intron-Mediated Induction of Phenotypic Heterogeneity.”
Nature, vol. 605, Springer Nature, 2022, pp. 113–18, doi:10.1038/s41586-022-04633-0.
short: M. Lukacisin, A. Espinosa-Cantú, M.T. Bollenbach, Nature 605 (2022) 113–118.
date_created: 2022-05-01T22:01:42Z
date_published: 2022-05-05T00:00:00Z
date_updated: 2023-08-03T06:44:50Z
day: '05'
ddc:
- '570'
doi: 10.1038/s41586-022-04633-0
ec_funded: 1
external_id:
isi:
- '000784934100003'
pmid:
- '35444278'
file:
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checksum: d68cd1596bb9fd819b750fe47c8a138a
content_type: application/pdf
creator: dernst
date_created: 2022-08-05T06:08:24Z
date_updated: 2022-08-05T06:08:24Z
file_id: '11727'
file_name: 2022_Nature_Lukacisin.pdf
file_size: 25360311
relation: main_file
success: 1
file_date_updated: 2022-08-05T06:08:24Z
has_accepted_license: '1'
intvolume: ' 605'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '05'
oa: 1
oa_version: Published Version
page: 113-118
pmid: 1
project:
- _id: 25E83C2C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
publication: Nature
publication_identifier:
eissn:
- 1476-4687
issn:
- 0028-0836
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Intron-mediated induction of phenotypic heterogeneity
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 605
year: '2022'
...
---
_id: '8997'
abstract:
- lang: eng
text: Phenomenological relations such as Ohm’s or Fourier’s law have a venerable
history in physics but are still scarce in biology. This situation restrains predictive
theory. Here, we build on bacterial “growth laws,” which capture physiological
feedback between translation and cell growth, to construct a minimal biophysical
model for the combined action of ribosome-targeting antibiotics. Our model predicts
drug interactions like antagonism or synergy solely from responses to individual
drugs. We provide analytical results for limiting cases, which agree well with
numerical results. We systematically refine the model by including direct physical
interactions of different antibiotics on the ribosome. In a limiting case, our
model provides a mechanistic underpinning for recent predictions of higher-order
interactions that were derived using entropy maximization. We further refine the
model to include the effects of antibiotics that mimic starvation and the presence
of resistance genes. We describe the impact of a starvation-mimicking antibiotic
on drug interactions analytically and verify it experimentally. Our extended model
suggests a change in the type of drug interaction that depends on the strength
of resistance, which challenges established rescaling paradigms. We experimentally
show that the presence of unregulated resistance genes can lead to altered drug
interaction, which agrees with the prediction of the model. While minimal, the
model is readily adaptable and opens the door to predicting interactions of second
and higher-order in a broad range of biological systems.
acknowledgement: 'This work was supported in part by Tum stipend of Knafelj foundation
(to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and
P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation
(DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG)
Collaborative Research Centre (SFB) 1310 (to T.B.). '
article_number: e1008529
article_processing_charge: Yes
article_type: original
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic
action. PLOS Computational Biology. 2021;17. doi:10.1371/journal.pcbi.1008529
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2021). Minimal biophysical
model of combined antibiotic action. PLOS Computational Biology. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1008529
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical
Model of Combined Antibiotic Action.” PLOS Computational Biology. Public
Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008529.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of
combined antibiotic action,” PLOS Computational Biology, vol. 17. Public
Library of Science, 2021.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined
antibiotic action. PLOS Computational Biology. 17, e1008529.
mla: Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.”
PLOS Computational Biology, vol. 17, e1008529, Public Library of Science,
2021, doi:10.1371/journal.pcbi.1008529.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021).
date_created: 2021-01-08T07:16:18Z
date_published: 2021-01-07T00:00:00Z
date_updated: 2024-02-21T12:41:41Z
day: '07'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1008529
external_id:
isi:
- '000608045000010'
file:
- access_level: open_access
checksum: e29f2b42651bef8e034781de8781ffac
content_type: application/pdf
creator: dernst
date_created: 2021-02-04T12:30:48Z
date_updated: 2021-02-04T12:30:48Z
file_id: '9092'
file_name: 2021_PlosComBio_Kavcic.pdf
file_size: 3690053
relation: main_file
success: 1
file_date_updated: 2021-02-04T12:30:48Z
has_accepted_license: '1'
intvolume: ' 17'
isi: 1
keyword:
- Modelling and Simulation
- Genetics
- Molecular Biology
- Antibiotics
- Drug interactions
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: PLOS Computational Biology
publication_identifier:
issn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
record:
- id: '7673'
relation: earlier_version
status: public
- id: '8930'
relation: research_data
status: public
status: public
title: Minimal biophysical model of combined antibiotic action
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 17
year: '2021'
...
---
_id: '10271'
abstract:
- lang: eng
text: Understanding interactions between antibiotics used in combination is an important
theme in microbiology. Using the interactions between the antifolate drug trimethoprim
and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model,
we applied a transcriptomic approach for dissecting interactions between two antibiotics
with different modes of action. When trimethoprim and erythromycin were combined,
the transcriptional response of genes from the sulfate reduction pathway deviated
from the dominant effect of trimethoprim on the transcriptome. We successfully
altered the drug interaction from additivity to suppression by increasing the
sulfate level in the growth environment and identified sulfate reduction as an
important metabolic determinant that shapes the interaction between the two drugs.
Our work highlights the potential of using prioritization of gene expression patterns
as a tool for identifying key metabolic determinants that shape drug-drug interactions.
We further demonstrated that the sigma factor-binding protein gene crl shapes
the interactions between the two antibiotics, which provides a rare example of
how naturally occurring variations between strains of the same bacterial species
can sometimes generate very different drug interactions.
acknowledgement: High-throughput sequencing data were generated by the Vienna BioCenter
Core Facilities. The authors would like to thank Karin Mitosch, Bor Kavcic, and
Nadine Kraupner for their constructive feedback. The authors would also like to
thank Gertraud Stift, Julia Flor, Renate Srsek, Agnieszka Wiktor, and Booshini Fernando
for technical support.
article_number: '760017'
article_processing_charge: No
article_type: original
author:
- first_name: Qin
full_name: Qi, Qin
id: 3B22D412-F248-11E8-B48F-1D18A9856A87
last_name: Qi
orcid: 0000-0002-6148-2416
- first_name: S. Andreas
full_name: Angermayr, S. Andreas
last_name: Angermayr
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Qi Q, Angermayr SA, Bollenbach MT. Uncovering Key Metabolic Determinants of
the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli.
Frontiers in Microbiology. 2021;12. doi:10.3389/fmicb.2021.760017
apa: Qi, Q., Angermayr, S. A., & Bollenbach, M. T. (2021). Uncovering Key Metabolic
Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in
Escherichia coli. Frontiers in Microbiology. Frontiers. https://doi.org/10.3389/fmicb.2021.760017
chicago: Qi, Qin, S. Andreas Angermayr, and Mark Tobias Bollenbach. “Uncovering
Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin
in Escherichia Coli.” Frontiers in Microbiology. Frontiers, 2021. https://doi.org/10.3389/fmicb.2021.760017.
ieee: Q. Qi, S. A. Angermayr, and M. T. Bollenbach, “Uncovering Key Metabolic Determinants
of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia
coli,” Frontiers in Microbiology, vol. 12. Frontiers, 2021.
ista: Qi Q, Angermayr SA, Bollenbach MT. 2021. Uncovering Key Metabolic Determinants
of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia
coli. Frontiers in Microbiology. 12, 760017.
mla: Qi, Qin, et al. “Uncovering Key Metabolic Determinants of the Drug Interactions
Between Trimethoprim and Erythromycin in Escherichia Coli.” Frontiers in Microbiology,
vol. 12, 760017, Frontiers, 2021, doi:10.3389/fmicb.2021.760017.
short: Q. Qi, S.A. Angermayr, M.T. Bollenbach, Frontiers in Microbiology 12 (2021).
date_created: 2021-11-11T10:39:37Z
date_published: 2021-10-20T00:00:00Z
date_updated: 2023-08-14T11:43:23Z
day: '20'
ddc:
- '610'
doi: 10.3389/fmicb.2021.760017
ec_funded: 1
external_id:
isi:
- '000715997300001'
pmid:
- '34745067'
file:
- access_level: open_access
checksum: d41321748e9588dd3cf03e9a7222127f
content_type: application/pdf
creator: cchlebak
date_created: 2021-11-11T10:54:40Z
date_updated: 2021-11-11T10:54:40Z
file_id: '10272'
file_name: 2021_FrontiersMicrob_Qi.pdf
file_size: 2397203
relation: main_file
success: 1
file_date_updated: 2021-11-11T10:54:40Z
has_accepted_license: '1'
intvolume: ' 12'
isi: 1
keyword:
- microbiology
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25E83C2C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
publication: Frontiers in Microbiology
publication_identifier:
eissn:
- 1664-302X
publication_status: published
publisher: Frontiers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim
and Erythromycin in Escherichia coli
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 12
year: '2021'
...
---
_id: '8037'
abstract:
- lang: eng
text: 'Genetic perturbations that affect bacterial resistance to antibiotics have
been characterized genome-wide, but how do such perturbations interact with subsequent
evolutionary adaptation to the drug? Here, we show that strong epistasis between
resistance mutations and systematically identified genes can be exploited to control
spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12
mutant populations in parallel, using a robotic platform that tightly controls
population size and selection pressure. We find a global diminishing-returns epistasis
pattern: strains that are initially more sensitive generally undergo larger resistance
gains. However, some gene deletion strains deviate from this general trend and
curtail the evolvability of resistance, including deletions of genes for membrane
transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force
evolution on inferior mutational paths, not explored in the wild type, and some
of these essentially block resistance evolution. This effect is due to strong
negative epistasis with resistance mutations. The identified genes and cellular
functions provide potential targets for development of adjuvants that may block
spontaneous resistance evolution when combined with antibiotics.'
article_number: '3105'
article_processing_charge: No
article_type: original
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Booshini
full_name: Fernando, Booshini
last_name: Fernando
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Lukacisinova M, Fernando B, Bollenbach MT. Highly parallel lab evolution reveals
that epistasis can curb the evolution of antibiotic resistance. Nature Communications.
2020;11. doi:10.1038/s41467-020-16932-z
apa: Lukacisinova, M., Fernando, B., & Bollenbach, M. T. (2020). Highly parallel
lab evolution reveals that epistasis can curb the evolution of antibiotic resistance.
Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-16932-z
chicago: Lukacisinova, Marta, Booshini Fernando, and Mark Tobias Bollenbach. “Highly
Parallel Lab Evolution Reveals That Epistasis Can Curb the Evolution of Antibiotic
Resistance.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-16932-z.
ieee: M. Lukacisinova, B. Fernando, and M. T. Bollenbach, “Highly parallel lab evolution
reveals that epistasis can curb the evolution of antibiotic resistance,” Nature
Communications, vol. 11. Springer Nature, 2020.
ista: Lukacisinova M, Fernando B, Bollenbach MT. 2020. Highly parallel lab evolution
reveals that epistasis can curb the evolution of antibiotic resistance. Nature
Communications. 11, 3105.
mla: Lukacisinova, Marta, et al. “Highly Parallel Lab Evolution Reveals That Epistasis
Can Curb the Evolution of Antibiotic Resistance.” Nature Communications,
vol. 11, 3105, Springer Nature, 2020, doi:10.1038/s41467-020-16932-z.
short: M. Lukacisinova, B. Fernando, M.T. Bollenbach, Nature Communications 11 (2020).
date_created: 2020-06-29T07:59:35Z
date_published: 2020-06-19T00:00:00Z
date_updated: 2023-08-22T07:48:30Z
day: '19'
ddc:
- '570'
doi: 10.1038/s41467-020-16932-z
extern: '1'
external_id:
isi:
- '000545685100002'
pmid:
- '32561723'
file:
- access_level: open_access
checksum: 4f5f49d63add331d5eb8a2bae477b396
content_type: application/pdf
creator: cziletti
date_created: 2020-06-30T09:58:50Z
date_updated: 2020-07-14T12:48:08Z
file_id: '8071'
file_name: 2020_NatureComm_Lukacisinova.pdf
file_size: 1546491
relation: main_file
file_date_updated: 2020-07-14T12:48:08Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
publication: Nature Communications
publication_identifier:
eissn:
- '20411723'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Highly parallel lab evolution reveals that epistasis can curb the evolution
of antibiotic resistance
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 11
year: '2020'
...
---
_id: '8250'
abstract:
- lang: eng
text: 'Antibiotics that interfere with translation, when combined, interact in diverse
and difficult-to-predict ways. Here, we explain these interactions by “translation
bottlenecks”: points in the translation cycle where antibiotics block ribosomal
progression. To elucidate the underlying mechanisms of drug interactions between
translation inhibitors, we generate translation bottlenecks genetically using
inducible 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 causes these
interactions. We further show that growth laws, combined with drug uptake and
binding kinetics, enable the direct prediction of a large fraction of observed
interactions, yet fail to predict suppression. However, varying two translation
bottlenecks simultaneously supports that dense traffic of ribosomes and competition
for translation factors account for the previously unexplained suppression. These
results highlight the importance of “continuous epistasis” in bacterial physiology.'
acknowledgement: "We thank M. Hennessey-Wesen, I. Tomanek, K. Jain, A. Staron, K.
Tomasek, M. Scott,\r\nK.C. Huang, and Z. Gitai for reading the manuscript and constructive
comments. B.K. is\r\nindebted to C. Guet for additional guidance and generous support,
which rendered this\r\nwork possible. B.K. thanks all members of Guet group for
many helpful discussions and\r\nsharing of resources. B.K. additionally acknowledges
the tremendous support from A.\r\nAngermayr and K. Mitosch with experimental work.
We further thank E. Brown for\r\nhelpful comments regarding lamotrigine, and A.
Buskirk for valuable suggestions\r\nregarding the ribosome footprint size. This
work was supported in part by Austrian\r\nScience Fund (FWF) standalone grants P
27201-B22 (to T.B.) and P 28844 (to G.T.),\r\nHFSP program Grant RGP0042/2013 (to
T.B.), German Research Foundation (DFG)\r\nstandalone grant BO 3502/2-1 (to T.B.),
and German Research Foundation (DFG)\r\nCollaborative Research Centre (SFB) 1310
(to T.B.). Open access funding provided by\r\nProjekt DEAL."
article_number: '4013'
article_processing_charge: No
article_type: original
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Kavcic B, Tkačik G, Bollenbach MT. Mechanisms of drug interactions between
translation-inhibiting antibiotics. Nature Communications. 2020;11. doi:10.1038/s41467-020-17734-z
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2020). Mechanisms of drug
interactions between translation-inhibiting antibiotics. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-020-17734-z
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Mechanisms of
Drug Interactions between Translation-Inhibiting Antibiotics.” Nature Communications.
Springer Nature, 2020. https://doi.org/10.1038/s41467-020-17734-z.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Mechanisms of drug interactions
between translation-inhibiting antibiotics,” Nature Communications, vol.
11. Springer Nature, 2020.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2020. Mechanisms of drug interactions between
translation-inhibiting antibiotics. Nature Communications. 11, 4013.
mla: Kavcic, Bor, et al. “Mechanisms of Drug Interactions between Translation-Inhibiting
Antibiotics.” Nature Communications, vol. 11, 4013, Springer Nature, 2020,
doi:10.1038/s41467-020-17734-z.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, Nature Communications 11 (2020).
date_created: 2020-08-12T09:13:50Z
date_published: 2020-08-11T00:00:00Z
date_updated: 2024-03-25T23:30:05Z
day: '11'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1038/s41467-020-17734-z
external_id:
isi:
- '000562769300008'
file:
- access_level: open_access
checksum: 986bebb308850a55850028d3d2b5b664
content_type: application/pdf
creator: dernst
date_created: 2020-08-17T07:36:57Z
date_updated: 2020-08-17T07:36:57Z
file_id: '8275'
file_name: 2020_NatureComm_Kavcic.pdf
file_size: 1965672
relation: main_file
success: 1
file_date_updated: 2020-08-17T07:36:57Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: Nature Communications
publication_identifier:
issn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '8657'
relation: dissertation_contains
status: public
status: public
title: Mechanisms of drug interactions between translation-inhibiting antibiotics
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 11
year: '2020'
...
---
_id: '7673'
abstract:
- lang: eng
text: Combining drugs can improve the efficacy of treatments. However, predicting
the effect of drug combinations is still challenging. The combined potency of
drugs determines the drug interaction, which is classified as synergistic, additive,
antagonistic, or suppressive. While probabilistic, non-mechanistic models exist,
there is currently no biophysical model that can predict antibiotic interactions.
Here, we present a physiologically relevant model of the combined action of antibiotics
that inhibit protein synthesis by targeting the ribosome. This model captures
the kinetics of antibiotic binding and transport, and uses bacterial growth laws
to predict growth in the presence of antibiotic combinations. We find that this
biophysical model can produce all drug interaction types except suppression. We
show analytically that antibiotics which cannot bind to the ribosome simultaneously
generally act as substitutes for one another, leading to additive drug interactions.
Previously proposed null expectations for higher-order drug interactions follow
as a limiting case of our model. We further extend the model to include the effects
of direct physical or allosteric interactions between individual drugs on the
ribosome. Notably, such direct interactions profoundly change the combined drug
effect, depending on the kinetic parameters of the drugs used. The model makes
additional predictions for the effects of resistance genes on drug interactions
and for interactions between ribosome-targeting antibiotics and antibiotics with
other targets. These findings enhance our understanding of the interplay between
drug action and cell physiology and are a key step toward a general framework
for predicting drug interactions.
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
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Kavcic B, Tkačik G, Bollenbach MT. A minimal biophysical model of combined
antibiotic action. bioRxiv. 2020. doi:10.1101/2020.04.18.047886
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2020). A minimal biophysical
model of combined antibiotic action. bioRxiv. Cold Spring Harbor Laboratory.
https://doi.org/10.1101/2020.04.18.047886
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “A Minimal Biophysical
Model of Combined Antibiotic Action.” BioRxiv. Cold Spring Harbor Laboratory,
2020. https://doi.org/10.1101/2020.04.18.047886.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “A minimal biophysical model of
combined antibiotic action,” bioRxiv. Cold Spring Harbor Laboratory, 2020.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2020. A minimal biophysical model of combined
antibiotic action. bioRxiv, 10.1101/2020.04.18.047886.
mla: Kavcic, Bor, et al. “A Minimal Biophysical Model of Combined Antibiotic Action.”
BioRxiv, Cold Spring Harbor Laboratory, 2020, doi:10.1101/2020.04.18.047886.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, BioRxiv (2020).
date_created: 2020-04-22T08:27:56Z
date_published: 2020-04-18T00:00:00Z
date_updated: 2024-03-25T23:30:05Z
day: '18'
department:
- _id: GaTk
doi: 10.1101/2020.04.18.047886
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.1101/2020.04.18.047886 '
month: '04'
oa: 1
oa_version: Preprint
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: bioRxiv
publication_status: published
publisher: Cold Spring Harbor Laboratory
related_material:
record:
- id: '8997'
relation: later_version
status: public
- id: '8657'
relation: dissertation_contains
status: public
status: public
title: A minimal biophysical model of combined antibiotic action
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '7026'
abstract:
- lang: eng
text: Effective design of combination therapies requires understanding the changes
in cell physiology that result from drug interactions. Here, we show that the
genome-wide transcriptional response to combinations of two drugs, measured at
a rigorously controlled growth rate, can predict higher-order antagonism with
a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90%
of the variation in cellular response can be decomposed into three principal components
(PCs) that have clear biological interpretations. We demonstrate that the third
PC captures emergent transcriptional programs that are dependent on both drugs
and can predict antagonism with a third drug targeting the emergent pathway. We
further show that emergent gene expression patterns are most pronounced at a drug
ratio where the drug interaction is strongest, providing a guideline for future
measurements. Our results provide a readily applicable recipe for uncovering emergent
responses in other systems and for higher-order drug combinations. A record of
this paper’s transparent peer review process is included in the Supplemental Information.
acknowledged_ssus:
- _id: LifeSc
article_processing_charge: No
article_type: original
author:
- first_name: Martin
full_name: Lukacisin, Martin
id: 298FFE8C-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisin
orcid: 0000-0001-6549-4177
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Lukacisin M, Bollenbach MT. Emergent gene expression responses to drug combinations
predict higher-order drug interactions. Cell Systems. 2019;9(5):423-433.e1-e3.
doi:10.1016/j.cels.2019.10.004
apa: Lukacisin, M., & Bollenbach, M. T. (2019). Emergent gene expression responses
to drug combinations predict higher-order drug interactions. Cell Systems.
Cell Press. https://doi.org/10.1016/j.cels.2019.10.004
chicago: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression
Responses to Drug Combinations Predict Higher-Order Drug Interactions.” Cell
Systems. Cell Press, 2019. https://doi.org/10.1016/j.cels.2019.10.004.
ieee: M. Lukacisin and M. T. Bollenbach, “Emergent gene expression responses to
drug combinations predict higher-order drug interactions,” Cell Systems,
vol. 9, no. 5. Cell Press, pp. 423-433.e1-e3, 2019.
ista: Lukacisin M, Bollenbach MT. 2019. Emergent gene expression responses to drug
combinations predict higher-order drug interactions. Cell Systems. 9(5), 423-433.e1-e3.
mla: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression Responses
to Drug Combinations Predict Higher-Order Drug Interactions.” Cell Systems,
vol. 9, no. 5, Cell Press, 2019, pp. 423-433.e1-e3, doi:10.1016/j.cels.2019.10.004.
short: M. Lukacisin, M.T. Bollenbach, Cell Systems 9 (2019) 423-433.e1-e3.
date_created: 2019-11-15T10:51:42Z
date_published: 2019-11-27T00:00:00Z
date_updated: 2023-08-30T07:24:58Z
day: '27'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.1016/j.cels.2019.10.004
external_id:
isi:
- '000499495400003'
file:
- access_level: open_access
checksum: 7a11d6c2f9523d65b049512d61733178
content_type: application/pdf
creator: dernst
date_created: 2019-11-15T10:57:42Z
date_updated: 2020-07-14T12:47:48Z
file_id: '7027'
file_name: 2019_CellSystems_Lukacisin.pdf
file_size: 4238460
relation: main_file
file_date_updated: 2020-07-14T12:47:48Z
has_accepted_license: '1'
intvolume: ' 9'
isi: 1
issue: '5'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 423-433.e1-e3
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
publication: Cell Systems
publication_identifier:
issn:
- 2405-4712
publication_status: published
publisher: Cell Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Emergent gene expression responses to drug combinations predict higher-order
drug interactions
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 9
year: '2019'
...
---
_id: '6046'
abstract:
- lang: eng
text: Sudden stress often triggers diverse, temporally structured gene expression
responses in microbes, but it is largely unknown how variable in time such responses
are and if genes respond in the same temporal order in every single cell. Here,
we quantified timing variability of individual promoters responding to sublethal
antibiotic stress using fluorescent reporters, microfluidics, and time‐lapse microscopy.
We identified lower and upper bounds that put definite constraints on timing variability,
which varies strongly among promoters and conditions. Timing variability can be
interpreted using results from statistical kinetics, which enable us to estimate
the number of rate‐limiting molecular steps underlying different responses. We
found that just a few critical steps control some responses while others rely
on dozens of steps. To probe connections between different stress responses, we
then tracked the temporal order and response time correlations of promoter pairs
in individual cells. Our results support that, when bacteria are exposed to the
antibiotic nitrofurantoin, the ensuing oxidative stress and SOS responses are
part of the same causal chain of molecular events. In contrast, under trimethoprim,
the acid stress response and the SOS response are part of different chains of
events running in parallel. Our approach reveals fundamental constraints on gene
expression timing and provides new insights into the molecular events that underlie
the timing of stress responses.
acknowledged_ssus:
- _id: Bio
article_number: e8470
article_processing_charge: No
author:
- first_name: Karin
full_name: Mitosch, Karin
id: 39B66846-F248-11E8-B48F-1D18A9856A87
last_name: Mitosch
- first_name: Georg
full_name: Rieckh, Georg
id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87
last_name: Rieckh
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Mitosch K, Rieckh G, Bollenbach MT. Temporal order and precision of complex
stress responses in individual bacteria. Molecular systems biology. 2019;15(2).
doi:10.15252/msb.20188470
apa: Mitosch, K., Rieckh, G., & Bollenbach, M. T. (2019). Temporal order and
precision of complex stress responses in individual bacteria. Molecular Systems
Biology. Embo Press. https://doi.org/10.15252/msb.20188470
chicago: Mitosch, Karin, Georg Rieckh, and Mark Tobias Bollenbach. “Temporal Order
and Precision of Complex Stress Responses in Individual Bacteria.” Molecular
Systems Biology. Embo Press, 2019. https://doi.org/10.15252/msb.20188470.
ieee: K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Temporal order and precision
of complex stress responses in individual bacteria,” Molecular systems biology,
vol. 15, no. 2. Embo Press, 2019.
ista: Mitosch K, Rieckh G, Bollenbach MT. 2019. Temporal order and precision of
complex stress responses in individual bacteria. Molecular systems biology. 15(2),
e8470.
mla: Mitosch, Karin, et al. “Temporal Order and Precision of Complex Stress Responses
in Individual Bacteria.” Molecular Systems Biology, vol. 15, no. 2, e8470,
Embo Press, 2019, doi:10.15252/msb.20188470.
short: K. Mitosch, G. Rieckh, M.T. Bollenbach, Molecular Systems Biology 15 (2019).
date_created: 2019-02-24T22:59:18Z
date_published: 2019-02-14T00:00:00Z
date_updated: 2023-08-24T14:49:53Z
day: '14'
department:
- _id: GaTk
doi: 10.15252/msb.20188470
external_id:
isi:
- '000459628300003'
pmid:
- '30765425'
intvolume: ' 15'
isi: 1
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pubmed/30765425
month: '02'
oa: 1
oa_version: Submitted Version
pmid: 1
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
publication: Molecular systems biology
publication_status: published
publisher: Embo Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Temporal order and precision of complex stress responses in individual bacteria
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 15
year: '2019'
...
---
_id: '822'
abstract:
- lang: eng
text: 'Polymicrobial infections constitute small ecosystems that accommodate several
bacterial species. Commonly, these bacteria are investigated in isolation. However,
it is unknown to what extent the isolates interact and whether their interactions
alter bacterial growth and ecosystem resilience in the presence and absence of
antibiotics. We quantified the complete ecological interaction network for 72
bacterial isolates collected from 23 individuals diagnosed with polymicrobial
urinary tract infections and found that most interactions cluster based on evolutionary
relatedness. Statistical network analysis revealed that competitive and cooperative
reciprocal interactions are enriched in the global network, while cooperative
interactions are depleted in the individual host community networks. A population
dynamics model parameterized by our measurements suggests that interactions restrict
community stability, explaining the observed species diversity of these communities.
We further show that the clinical isolates frequently protect each other from
clinically relevant antibiotics. Together, these results highlight that ecological
interactions are crucial for the growth and survival of bacteria in polymicrobial
infection communities and affect their assembly and resilience. '
article_processing_charge: No
author:
- first_name: Marjon
full_name: De Vos, Marjon
id: 3111FFAC-F248-11E8-B48F-1D18A9856A87
last_name: De Vos
- first_name: Marcin P
full_name: Zagórski, Marcin P
id: 343DA0DC-F248-11E8-B48F-1D18A9856A87
last_name: Zagórski
orcid: 0000-0001-7896-7762
- first_name: Alan
full_name: Mcnally, Alan
last_name: Mcnally
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: de Vos M, Zagórski MP, Mcnally A, Bollenbach MT. Interaction networks, ecological
stability, and collective antibiotic tolerance in polymicrobial infections. PNAS.
2017;114(40):10666-10671. doi:10.1073/pnas.1713372114
apa: de Vos, M., Zagórski, M. P., Mcnally, A., & Bollenbach, M. T. (2017). Interaction
networks, ecological stability, and collective antibiotic tolerance in polymicrobial
infections. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1713372114
chicago: Vos, Marjon de, Marcin P Zagórski, Alan Mcnally, and Mark Tobias Bollenbach.
“Interaction Networks, Ecological Stability, and Collective Antibiotic Tolerance
in Polymicrobial Infections.” PNAS. National Academy of Sciences, 2017.
https://doi.org/10.1073/pnas.1713372114.
ieee: M. de Vos, M. P. Zagórski, A. Mcnally, and M. T. Bollenbach, “Interaction
networks, ecological stability, and collective antibiotic tolerance in polymicrobial
infections,” PNAS, vol. 114, no. 40. National Academy of Sciences, pp.
10666–10671, 2017.
ista: de Vos M, Zagórski MP, Mcnally A, Bollenbach MT. 2017. Interaction networks,
ecological stability, and collective antibiotic tolerance in polymicrobial infections.
PNAS. 114(40), 10666–10671.
mla: de Vos, Marjon, et al. “Interaction Networks, Ecological Stability, and Collective
Antibiotic Tolerance in Polymicrobial Infections.” PNAS, vol. 114, no.
40, National Academy of Sciences, 2017, pp. 10666–71, doi:10.1073/pnas.1713372114.
short: M. de Vos, M.P. Zagórski, A. Mcnally, M.T. Bollenbach, PNAS 114 (2017) 10666–10671.
date_created: 2018-12-11T11:48:41Z
date_published: 2017-10-03T00:00:00Z
date_updated: 2023-09-26T16:18:48Z
day: '03'
department:
- _id: ToBo
doi: 10.1073/pnas.1713372114
ec_funded: 1
external_id:
isi:
- '000412130500061'
pmid:
- '28923953'
intvolume: ' 114'
isi: 1
issue: '40'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635929/
month: '10'
oa: 1
oa_version: Submitted Version
page: 10666 - 10671
pmid: 1
project:
- _id: 25E83C2C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
publication: PNAS
publication_identifier:
issn:
- '00278424'
publication_status: published
publisher: National Academy of Sciences
publist_id: '6827'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Interaction networks, ecological stability, and collective antibiotic tolerance
in polymicrobial infections
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 114
year: '2017'
...
---
_id: '666'
abstract:
- lang: eng
text: Antibiotics elicit drastic changes in microbial gene expression, including
the induction of stress response genes. While certain stress responses are known
to “cross-protect” bacteria from other stressors, it is unclear whether cellular
responses to antibiotics have a similar protective role. By measuring the genome-wide
transcriptional response dynamics of Escherichia coli to four antibiotics, we
found that trimethoprim induces a rapid acid stress response that protects bacteria
from subsequent exposure to acid. Combining microfluidics with time-lapse imaging
to monitor survival and acid stress response in single cells revealed that the
noisy expression of the acid resistance operon gadBC correlates with single-cell
survival. Cells with higher gadBC expression following trimethoprim maintain higher
intracellular pH and survive the acid stress longer. The seemingly random single-cell
survival under acid stress can therefore be predicted from gadBC expression and
rationalized in terms of GadB/C molecular function. Overall, we provide a roadmap
for identifying the molecular mechanisms of single-cell cross-protection between
antibiotics and other stressors.
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Karin
full_name: Mitosch, Karin
id: 39B66846-F248-11E8-B48F-1D18A9856A87
last_name: Mitosch
- first_name: Georg
full_name: Rieckh, Georg
id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87
last_name: Rieckh
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Mitosch K, Rieckh G, Bollenbach MT. Noisy response to antibiotic stress predicts
subsequent single cell survival in an acidic environment. Cell Systems.
2017;4(4):393-403. doi:10.1016/j.cels.2017.03.001
apa: Mitosch, K., Rieckh, G., & Bollenbach, M. T. (2017). Noisy response to
antibiotic stress predicts subsequent single cell survival in an acidic environment.
Cell Systems. Cell Press. https://doi.org/10.1016/j.cels.2017.03.001
chicago: Mitosch, Karin, Georg Rieckh, and Mark Tobias Bollenbach. “Noisy Response
to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.”
Cell Systems. Cell Press, 2017. https://doi.org/10.1016/j.cels.2017.03.001.
ieee: K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Noisy response to antibiotic
stress predicts subsequent single cell survival in an acidic environment,” Cell
Systems, vol. 4, no. 4. Cell Press, pp. 393–403, 2017.
ista: Mitosch K, Rieckh G, Bollenbach MT. 2017. Noisy response to antibiotic stress
predicts subsequent single cell survival in an acidic environment. Cell Systems.
4(4), 393–403.
mla: Mitosch, Karin, et al. “Noisy Response to Antibiotic Stress Predicts Subsequent
Single Cell Survival in an Acidic Environment.” Cell Systems, vol. 4, no.
4, Cell Press, 2017, pp. 393–403, doi:10.1016/j.cels.2017.03.001.
short: K. Mitosch, G. Rieckh, M.T. Bollenbach, Cell Systems 4 (2017) 393–403.
date_created: 2018-12-11T11:47:48Z
date_published: 2017-04-26T00:00:00Z
date_updated: 2023-09-07T12:00:25Z
day: '26'
ddc:
- '576'
- '610'
department:
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- _id: GaTk
doi: 10.1016/j.cels.2017.03.001
ec_funded: 1
file:
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content_type: application/pdf
creator: system
date_created: 2018-12-12T10:13:54Z
date_updated: 2020-07-14T12:47:35Z
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month: '04'
oa: 1
oa_version: Published Version
page: 393 - 403
project:
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call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
publication: Cell Systems
publication_identifier:
issn:
- '24054712'
publication_status: published
publisher: Cell Press
publist_id: '7061'
pubrep_id: '901'
quality_controlled: '1'
related_material:
record:
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relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Noisy response to antibiotic stress predicts subsequent single cell survival
in an acidic environment
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2017'
...
---
_id: '679'
abstract:
- lang: eng
text: Protective responses against pathogens require a rapid mobilization of resting
neutrophils and the timely removal of activated ones. Neutrophils are exceptionally
short-lived leukocytes, yet it remains unclear whether the lifespan of pathogen-engaged
neutrophils is regulated differently from that in the circulating steady-state
pool. Here, we have found that under homeostatic conditions, the mRNA-destabilizing
protein tristetraprolin (TTP) regulates apoptosis and the numbers of activated
infiltrating murine neutrophils but not neutrophil cellularity. Activated TTP-deficient
neutrophils exhibited decreased apoptosis and enhanced accumulation at the infection
site. In the context of myeloid-specific deletion of Ttp, the potentiation of
neutrophil deployment protected mice against lethal soft tissue infection with
Streptococcus pyogenes and prevented bacterial dissemination. Neutrophil transcriptome
analysis revealed that decreased apoptosis of TTP-deficient neutrophils was specifically
associated with elevated expression of myeloid cell leukemia 1 (Mcl1) but not
other antiapoptotic B cell leukemia/ lymphoma 2 (Bcl2) family members. Higher
Mcl1 expression resulted from stabilization of Mcl1 mRNA in the absence of TTP.
The low apoptosis rate of infiltrating TTP-deficient neutrophils was comparable
to that of transgenic Mcl1-overexpressing neutrophils. Our study demonstrates
that posttranscriptional gene regulation by TTP schedules the termination of the
antimicrobial engagement of neutrophils. The balancing role of TTP comes at the
cost of an increased risk of bacterial infections.
acknowledgement: This work was supported by grants from the Austrian Science Fund
(FWF) (P27538-B21, I1621-B22, and SFB 43, to PK); by funding from the European Union
Seventh Framework Programme Marie Curie Initial Training Networks (FP7-PEOPLE-2012-ITN)
for the project INBIONET (INfection BIOlogy Training NETwork under grant agreement
PITN-GA-2012-316682; and by a joint research cluster initiative of the University
of Vienna and the Medical University of Vienna.
author:
- first_name: Florian
full_name: Ebner, Florian
last_name: Ebner
- first_name: Vitaly
full_name: Sedlyarov, Vitaly
last_name: Sedlyarov
- first_name: Saren
full_name: Tasciyan, Saren
id: 4323B49C-F248-11E8-B48F-1D18A9856A87
last_name: Tasciyan
orcid: 0000-0003-1671-393X
- first_name: Masa
full_name: Ivin, Masa
last_name: Ivin
- first_name: Franz
full_name: Kratochvill, Franz
last_name: Kratochvill
- first_name: Nina
full_name: Gratz, Nina
last_name: Gratz
- first_name: Lukas
full_name: Kenner, Lukas
last_name: Kenner
- first_name: Andreas
full_name: Villunger, Andreas
last_name: Villunger
- first_name: Michael K
full_name: Sixt, Michael K
id: 41E9FBEA-F248-11E8-B48F-1D18A9856A87
last_name: Sixt
orcid: 0000-0002-6620-9179
- first_name: Pavel
full_name: Kovarik, Pavel
last_name: Kovarik
citation:
ama: Ebner F, Sedlyarov V, Tasciyan S, et al. The RNA-binding protein tristetraprolin
schedules apoptosis of pathogen-engaged neutrophils during bacterial infection.
The Journal of Clinical Investigation. 2017;127(6):2051-2065. doi:10.1172/JCI80631
apa: Ebner, F., Sedlyarov, V., Tasciyan, S., Ivin, M., Kratochvill, F., Gratz, N.,
… Kovarik, P. (2017). The RNA-binding protein tristetraprolin schedules apoptosis
of pathogen-engaged neutrophils during bacterial infection. The Journal of
Clinical Investigation. American Society for Clinical Investigation. https://doi.org/10.1172/JCI80631
chicago: Ebner, Florian, Vitaly Sedlyarov, Saren Tasciyan, Masa Ivin, Franz Kratochvill,
Nina Gratz, Lukas Kenner, Andreas Villunger, Michael K Sixt, and Pavel Kovarik.
“The RNA-Binding Protein Tristetraprolin Schedules Apoptosis of Pathogen-Engaged
Neutrophils during Bacterial Infection.” The Journal of Clinical Investigation.
American Society for Clinical Investigation, 2017. https://doi.org/10.1172/JCI80631.
ieee: F. Ebner et al., “The RNA-binding protein tristetraprolin schedules
apoptosis of pathogen-engaged neutrophils during bacterial infection,” The
Journal of Clinical Investigation, vol. 127, no. 6. American Society for Clinical
Investigation, pp. 2051–2065, 2017.
ista: Ebner F, Sedlyarov V, Tasciyan S, Ivin M, Kratochvill F, Gratz N, Kenner L,
Villunger A, Sixt MK, Kovarik P. 2017. The RNA-binding protein tristetraprolin
schedules apoptosis of pathogen-engaged neutrophils during bacterial infection.
The Journal of Clinical Investigation. 127(6), 2051–2065.
mla: Ebner, Florian, et al. “The RNA-Binding Protein Tristetraprolin Schedules Apoptosis
of Pathogen-Engaged Neutrophils during Bacterial Infection.” The Journal of
Clinical Investigation, vol. 127, no. 6, American Society for Clinical Investigation,
2017, pp. 2051–65, doi:10.1172/JCI80631.
short: F. Ebner, V. Sedlyarov, S. Tasciyan, M. Ivin, F. Kratochvill, N. Gratz, L.
Kenner, A. Villunger, M.K. Sixt, P. Kovarik, The Journal of Clinical Investigation
127 (2017) 2051–2065.
date_created: 2018-12-11T11:47:53Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2024-03-25T23:30:12Z
day: '01'
department:
- _id: MiSi
doi: 10.1172/JCI80631
external_id:
pmid:
- '28504646'
intvolume: ' 127'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451238/
month: '06'
oa: 1
oa_version: Submitted Version
page: 2051 - 2065
pmid: 1
project:
- _id: 25985A36-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: T00817-B21
name: The biochemical basis of PAR polarization
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
publication: The Journal of Clinical Investigation
publication_identifier:
issn:
- '00219738'
publication_status: published
publisher: American Society for Clinical Investigation
publist_id: '7038'
quality_controlled: '1'
related_material:
record:
- id: '12401'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: The RNA-binding protein tristetraprolin schedules apoptosis of pathogen-engaged
neutrophils during bacterial infection
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 127
year: '2017'
...
---
_id: '713'
abstract:
- lang: eng
text: To determine the dynamics of allelic-specific expression during mouse development,
we analyzed RNA-seq data from 23 F1 tissues from different developmental stages,
including 19 female tissues allowing X chromosome inactivation (XCI) escapers
to also be detected. We demonstrate that allelic expression arising from genetic
or epigenetic differences is highly tissue-specific. We find that tissue-specific
strain-biased gene expression may be regulated by tissue-specific enhancers or
by post-transcriptional differences in stability between the alleles. We also
find that escape from X-inactivation is tissue-specific, with leg muscle showing
an unexpectedly high rate of XCI escapers. By surveying a range of tissues during
development, and performing extensive validation, we are able to provide a high
confidence list of mouse imprinted genes including 18 novel genes. This shows
that cluster size varies dynamically during development and can be substantially
larger than previously thought, with the Igf2r cluster extending over 10 Mb in
placenta.
article_number: e25125
author:
- first_name: Daniel
full_name: Andergassen, Daniel
last_name: Andergassen
- first_name: Christoph
full_name: Dotter, Christoph
id: 4C66542E-F248-11E8-B48F-1D18A9856A87
last_name: Dotter
- first_name: Dyniel
full_name: Wenzel, Dyniel
last_name: Wenzel
- first_name: Verena
full_name: Sigl, Verena
last_name: Sigl
- first_name: Philipp
full_name: Bammer, Philipp
last_name: Bammer
- first_name: Markus
full_name: Muckenhuber, Markus
last_name: Muckenhuber
- first_name: Daniela
full_name: Mayer, Daniela
last_name: Mayer
- first_name: Tomasz
full_name: Kulinski, Tomasz
last_name: Kulinski
- first_name: Hans
full_name: Theussl, Hans
last_name: Theussl
- first_name: Josef
full_name: Penninger, Josef
last_name: Penninger
- first_name: Christoph
full_name: Bock, Christoph
last_name: Bock
- first_name: Denise
full_name: Barlow, Denise
last_name: Barlow
- first_name: Florian
full_name: Pauler, Florian
id: 48EA0138-F248-11E8-B48F-1D18A9856A87
last_name: Pauler
- first_name: Quanah
full_name: Hudson, Quanah
last_name: Hudson
citation:
ama: Andergassen D, Dotter C, Wenzel D, et al. Mapping the mouse Allelome reveals
tissue specific regulation of allelic expression. eLife. 2017;6. doi:10.7554/eLife.25125
apa: Andergassen, D., Dotter, C., Wenzel, D., Sigl, V., Bammer, P., Muckenhuber,
M., … Hudson, Q. (2017). Mapping the mouse Allelome reveals tissue specific regulation
of allelic expression. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.25125
chicago: Andergassen, Daniel, Christoph Dotter, Dyniel Wenzel, Verena Sigl, Philipp
Bammer, Markus Muckenhuber, Daniela Mayer, et al. “Mapping the Mouse Allelome
Reveals Tissue Specific Regulation of Allelic Expression.” ELife. eLife
Sciences Publications, 2017. https://doi.org/10.7554/eLife.25125.
ieee: D. Andergassen et al., “Mapping the mouse Allelome reveals tissue specific
regulation of allelic expression,” eLife, vol. 6. eLife Sciences Publications,
2017.
ista: Andergassen D, Dotter C, Wenzel D, Sigl V, Bammer P, Muckenhuber M, Mayer
D, Kulinski T, Theussl H, Penninger J, Bock C, Barlow D, Pauler F, Hudson Q. 2017.
Mapping the mouse Allelome reveals tissue specific regulation of allelic expression.
eLife. 6, e25125.
mla: Andergassen, Daniel, et al. “Mapping the Mouse Allelome Reveals Tissue Specific
Regulation of Allelic Expression.” ELife, vol. 6, e25125, eLife Sciences
Publications, 2017, doi:10.7554/eLife.25125.
short: D. Andergassen, C. Dotter, D. Wenzel, V. Sigl, P. Bammer, M. Muckenhuber,
D. Mayer, T. Kulinski, H. Theussl, J. Penninger, C. Bock, D. Barlow, F. Pauler,
Q. Hudson, ELife 6 (2017).
date_created: 2018-12-11T11:48:05Z
date_published: 2017-08-14T00:00:00Z
date_updated: 2021-01-12T08:11:57Z
day: '14'
ddc:
- '576'
department:
- _id: GaNo
- _id: SiHi
doi: 10.7554/eLife.25125
file:
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checksum: 1ace3462e64a971b9ead896091829549
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:13:36Z
date_updated: 2020-07-14T12:47:50Z
file_id: '5020'
file_name: IST-2017-885-v1+1_elife-25125-figures-v2.pdf
file_size: 6399510
relation: main_file
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checksum: 6241dc31eeb87b03facadec3a53a6827
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:13:36Z
date_updated: 2020-07-14T12:47:50Z
file_id: '5021'
file_name: IST-2017-885-v1+2_elife-25125-v2.pdf
file_size: 4264398
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file_date_updated: 2020-07-14T12:47:50Z
has_accepted_license: '1'
intvolume: ' 6'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
publication: eLife
publication_identifier:
issn:
- 2050084X
publication_status: published
publisher: eLife Sciences Publications
publist_id: '6971'
pubrep_id: '885'
quality_controlled: '1'
scopus_import: 1
status: public
title: Mapping the mouse Allelome reveals tissue specific regulation of allelic expression
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2017'
...
---
_id: '1027'
abstract:
- lang: eng
text: The rising prevalence of antibiotic resistant bacteria is an increasingly
serious public health challenge. To address this problem, recent work ranging
from clinical studies to theoretical modeling has provided valuable insights into
the mechanisms of resistance, its emergence and spread, and ways to counteract
it. A deeper understanding of the underlying dynamics of resistance evolution
will require a combination of experimental and theoretical expertise from different
disciplines and new technology for studying evolution in the laboratory. Here,
we review recent advances in the quantitative understanding of the mechanisms
and evolution of antibiotic resistance. We focus on key theoretical concepts and
new technology that enables well-controlled experiments. We further highlight
key challenges that can be met in the near future to ultimately develop effective
strategies for combating resistance.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Lukacisinova M, Bollenbach MT. Toward a quantitative understanding of antibiotic
resistance evolution. Current Opinion in Biotechnology. 2017;46:90-97.
doi:10.1016/j.copbio.2017.02.013
apa: Lukacisinova, M., & Bollenbach, M. T. (2017). Toward a quantitative understanding
of antibiotic resistance evolution. Current Opinion in Biotechnology. Elsevier.
https://doi.org/10.1016/j.copbio.2017.02.013
chicago: Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative
Understanding of Antibiotic Resistance Evolution.” Current Opinion in Biotechnology.
Elsevier, 2017. https://doi.org/10.1016/j.copbio.2017.02.013.
ieee: M. Lukacisinova and M. T. Bollenbach, “Toward a quantitative understanding
of antibiotic resistance evolution,” Current Opinion in Biotechnology,
vol. 46. Elsevier, pp. 90–97, 2017.
ista: Lukacisinova M, Bollenbach MT. 2017. Toward a quantitative understanding of
antibiotic resistance evolution. Current Opinion in Biotechnology. 46, 90–97.
mla: Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding
of Antibiotic Resistance Evolution.” Current Opinion in Biotechnology,
vol. 46, Elsevier, 2017, pp. 90–97, doi:10.1016/j.copbio.2017.02.013.
short: M. Lukacisinova, M.T. Bollenbach, Current Opinion in Biotechnology 46 (2017)
90–97.
date_created: 2018-12-11T11:49:45Z
date_published: 2017-08-01T00:00:00Z
date_updated: 2024-03-25T23:30:15Z
day: '01'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.1016/j.copbio.2017.02.013
ec_funded: 1
external_id:
isi:
- '000408077400015'
file:
- access_level: open_access
content_type: application/pdf
creator: dernst
date_created: 2019-01-18T09:57:57Z
date_updated: 2019-01-18T09:57:57Z
file_id: '5846'
file_name: 2017_CurrentOpinion_Lukaciinova.pdf
file_size: 858338
relation: main_file
success: 1
file_date_updated: 2019-01-18T09:57:57Z
has_accepted_license: '1'
intvolume: ' 46'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 90 - 97
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25E83C2C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
publication: Current Opinion in Biotechnology
publication_status: published
publisher: Elsevier
publist_id: '6364'
pubrep_id: '801'
quality_controlled: '1'
related_material:
record:
- id: '6263'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Toward a quantitative understanding of antibiotic resistance evolution
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 46
year: '2017'
...
---
_id: '1619'
abstract:
- lang: eng
text: The emergence of drug resistant pathogens is a serious public health problem.
It is a long-standing goal to predict rates of resistance evolution and design
optimal treatment strategies accordingly. To this end, it is crucial to reveal
the underlying causes of drug-specific differences in the evolutionary dynamics
leading to resistance. However, it remains largely unknown why the rates of resistance
evolution via spontaneous mutations and the diversity of mutational paths vary
substantially between drugs. Here we comprehensively quantify the distribution
of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics,
in the presence of eight antibiotics representing the main modes of action. Using
precise high-throughput fitness measurements for genome-wide Escherichia coli
gene deletion strains, we find that the width of the DFE varies dramatically between
antibiotics and, contrary to conventional wisdom, for some drugs the DFE width
is lower than in the absence of stress. We show that this previously underappreciated
divergence in DFE width among antibiotics is largely caused by their distinct
drug-specific dose-response characteristics. Unlike the DFE, the magnitude of
the changes in tolerated drug concentration resulting from genome-wide mutations
is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin,
i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin
than for other drugs. A population genetics model predicts that resistance evolution
for drugs with this property is severely limited and confined to reproducible
mutational paths. We tested this prediction in laboratory evolution experiments
using the “morbidostat”, a device for evolving bacteria in well-controlled drug
environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible
mutations—an almost paradoxical behavior since this drug causes DNA damage and
increases the mutation rate. Overall, we identified novel quantitative characteristics
of the evolutionary landscape that provide the conceptual foundation for predicting
the dynamics of drug resistance evolution.
article_number: e1002299
author:
- first_name: Guillaume
full_name: Chevereau, Guillaume
id: 424D78A0-F248-11E8-B48F-1D18A9856A87
last_name: Chevereau
- first_name: Marta
full_name: Dravecka, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Dravecka
orcid: 0000-0002-2519-8004
- first_name: Tugce
full_name: Batur, Tugce
last_name: Batur
- first_name: Aysegul
full_name: Guvenek, Aysegul
last_name: Guvenek
- first_name: Dilay
full_name: Ayhan, Dilay
last_name: Ayhan
- first_name: Erdal
full_name: Toprak, Erdal
last_name: Toprak
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Chevereau G, Lukacisinova M, Batur T, et al. Quantifying the determinants of
evolutionary dynamics leading to drug resistance. PLoS Biology. 2015;13(11).
doi:10.1371/journal.pbio.1002299
apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D., Toprak,
E., & Bollenbach, M. T. (2015). Quantifying the determinants of evolutionary
dynamics leading to drug resistance. PLoS Biology. Public Library of Science.
https://doi.org/10.1371/journal.pbio.1002299
chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek,
Dilay Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Quantifying the Determinants
of Evolutionary Dynamics Leading to Drug Resistance.” PLoS Biology. Public
Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.
ieee: G. Chevereau et al., “Quantifying the determinants of evolutionary
dynamics leading to drug resistance,” PLoS Biology, vol. 13, no. 11. Public
Library of Science, 2015.
ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan D, Toprak E, Bollenbach
MT. 2015. Quantifying the determinants of evolutionary dynamics leading to drug
resistance. PLoS Biology. 13(11), e1002299.
mla: Chevereau, Guillaume, et al. “Quantifying the Determinants of Evolutionary
Dynamics Leading to Drug Resistance.” PLoS Biology, vol. 13, no. 11, e1002299,
Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.
short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak,
M.T. Bollenbach, PLoS Biology 13 (2015).
date_created: 2018-12-11T11:53:04Z
date_published: 2015-11-18T00:00:00Z
date_updated: 2024-03-25T23:30:14Z
day: '18'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.1371/journal.pbio.1002299
ec_funded: 1
file:
- access_level: open_access
checksum: 0e82e3279f50b15c6c170c042627802b
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:09:00Z
date_updated: 2020-07-14T12:45:07Z
file_id: '4723'
file_name: IST-2016-468-v1+1_journal.pbio.1002299.pdf
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intvolume: ' 13'
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
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grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
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name: Revealing the mechanisms underlying drug interactions
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name: Optimality principles in responses to antibiotics
publication: PLoS Biology
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publisher: Public Library of Science
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status: public
- id: '9765'
relation: research_data
status: public
- id: '6263'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Quantifying the determinants of evolutionary dynamics leading to drug resistance
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2015'
...
---
_id: '1810'
abstract:
- lang: eng
text: Combining antibiotics is a promising strategy for increasing treatment efficacy
and for controlling resistance evolution. When drugs are combined, their effects
on cells may be amplified or weakened, that is the drugs may show synergistic
or antagonistic interactions. Recent work revealed the underlying mechanisms of
such drug interactions by elucidating the drugs'; joint effects on cell physiology.
Moreover, new treatment strategies that use drug combinations to exploit evolutionary
tradeoffs were shown to affect the rate of resistance evolution in predictable
ways. High throughput studies have further identified drug candidates based on
their interactions with established antibiotics and general principles that enable
the prediction of drug interactions were suggested. Overall, the conceptual and
technical foundation for the rational design of potent drug combinations is rapidly
developing.
author:
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: 'Bollenbach MT. Antimicrobial interactions: Mechanisms and implications for
drug discovery and resistance evolution. Current Opinion in Microbiology.
2015;27:1-9. doi:10.1016/j.mib.2015.05.008'
apa: 'Bollenbach, M. T. (2015). Antimicrobial interactions: Mechanisms and implications
for drug discovery and resistance evolution. Current Opinion in Microbiology.
Elsevier. https://doi.org/10.1016/j.mib.2015.05.008'
chicago: 'Bollenbach, Mark Tobias. “Antimicrobial Interactions: Mechanisms and Implications
for Drug Discovery and Resistance Evolution.” Current Opinion in Microbiology.
Elsevier, 2015. https://doi.org/10.1016/j.mib.2015.05.008.'
ieee: 'M. T. Bollenbach, “Antimicrobial interactions: Mechanisms and implications
for drug discovery and resistance evolution,” Current Opinion in Microbiology,
vol. 27. Elsevier, pp. 1–9, 2015.'
ista: 'Bollenbach MT. 2015. Antimicrobial interactions: Mechanisms and implications
for drug discovery and resistance evolution. Current Opinion in Microbiology.
27, 1–9.'
mla: 'Bollenbach, Mark Tobias. “Antimicrobial Interactions: Mechanisms and Implications
for Drug Discovery and Resistance Evolution.” Current Opinion in Microbiology,
vol. 27, Elsevier, 2015, pp. 1–9, doi:10.1016/j.mib.2015.05.008.'
short: M.T. Bollenbach, Current Opinion in Microbiology 27 (2015) 1–9.
date_created: 2018-12-11T11:54:08Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2021-01-12T06:53:21Z
day: '01'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.1016/j.mib.2015.05.008
ec_funded: 1
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creator: system
date_created: 2018-12-12T10:17:23Z
date_updated: 2020-07-14T12:45:17Z
file_id: '5277'
file_name: IST-2016-493-v1+1_1-s2.0-S1369527415000594-main.pdf
file_size: 1047255
relation: main_file
file_date_updated: 2020-07-14T12:45:17Z
has_accepted_license: '1'
intvolume: ' 27'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 1 - 9
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25E83C2C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
publication: Current Opinion in Microbiology
publication_status: published
publisher: Elsevier
publist_id: '5298'
pubrep_id: '493'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Antimicrobial interactions: Mechanisms and implications for drug discovery
and resistance evolution'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 27
year: '2015'
...
---
_id: '1823'
abstract:
- lang: eng
text: Abstract Drug combinations are increasingly important in disease treatments,
for combating drug resistance, and for elucidating fundamental relationships in
cell physiology. When drugs are combined, their individual effects on cells may
be amplified or weakened. Such drug interactions are crucial for treatment efficacy,
but their underlying mechanisms remain largely unknown. To uncover the causes
of drug interactions, we developed a systematic approach based on precise quantification
of the individual and joint effects of antibiotics on growth of genome-wide Escherichia
coli gene deletion strains. We found that drug interactions between antibiotics
representing the main modes of action are highly robust to genetic perturbation.
This robustness is encapsulated in a general principle of bacterial growth, which
enables the quantitative prediction of mutant growth rates under drug combinations.
Rare violations of this principle exposed recurring cellular functions controlling
drug interactions. In particular, we found that polysaccharide and ATP synthesis
control multiple drug interactions with previously unexplained mechanisms, and
small molecule adjuvants targeting these functions synthetically reshape drug
interactions in predictable ways. These results provide a new conceptual framework
for the design of multidrug combinations and suggest that there are universal
mechanisms at the heart of most drug interactions. Synopsis A general principle
of bacterial growth enables the prediction of mutant growth rates under drug combinations.
Rare violations of this principle expose cellular functions that control drug
interactions and can be targeted by small molecules to alter drug interactions
in predictable ways. Drug interactions between antibiotics are highly robust to
genetic perturbations. A general principle of bacterial growth enables the prediction
of mutant growth rates under drug combinations. Rare violations of this principle
expose cellular functions that control drug interactions. Diverse drug interactions
are controlled by recurring cellular functions, including LPS synthesis and ATP
synthesis. A general principle of bacterial growth enables the prediction of mutant
growth rates under drug combinations. Rare violations of this principle expose
cellular functions that control drug interactions and can be targeted by small
molecules to alter drug interactions in predictable ways.
article_number: '807'
author:
- first_name: Guillaume
full_name: Chevereau, Guillaume
id: 424D78A0-F248-11E8-B48F-1D18A9856A87
last_name: Chevereau
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Chevereau G, Bollenbach MT. Systematic discovery of drug interaction mechanisms.
Molecular Systems Biology. 2015;11(4). doi:10.15252/msb.20156098
apa: Chevereau, G., & Bollenbach, M. T. (2015). Systematic discovery of drug
interaction mechanisms. Molecular Systems Biology. Nature Publishing Group.
https://doi.org/10.15252/msb.20156098
chicago: Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery
of Drug Interaction Mechanisms.” Molecular Systems Biology. Nature Publishing
Group, 2015. https://doi.org/10.15252/msb.20156098.
ieee: G. Chevereau and M. T. Bollenbach, “Systematic discovery of drug interaction
mechanisms,” Molecular Systems Biology, vol. 11, no. 4. Nature Publishing
Group, 2015.
ista: Chevereau G, Bollenbach MT. 2015. Systematic discovery of drug interaction
mechanisms. Molecular Systems Biology. 11(4), 807.
mla: Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of
Drug Interaction Mechanisms.” Molecular Systems Biology, vol. 11, no. 4,
807, Nature Publishing Group, 2015, doi:10.15252/msb.20156098.
short: G. Chevereau, M.T. Bollenbach, Molecular Systems Biology 11 (2015).
date_created: 2018-12-11T11:54:12Z
date_published: 2015-04-01T00:00:00Z
date_updated: 2021-01-12T06:53:26Z
day: '01'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.15252/msb.20156098
ec_funded: 1
file:
- access_level: open_access
checksum: 4289b518fbe2166682fb1a1ef9b405f3
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:34Z
date_updated: 2020-07-14T12:45:17Z
file_id: '5087'
file_name: IST-2015-395-v1+1_807.full.pdf
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file_date_updated: 2020-07-14T12:45:17Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
- _id: 25E83C2C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '303507'
name: Optimality principles in responses to antibiotics
publication: Molecular Systems Biology
publication_status: published
publisher: Nature Publishing Group
publist_id: '5283'
pubrep_id: '395'
quality_controlled: '1'
scopus_import: 1
status: public
title: Systematic discovery of drug interaction mechanisms
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2015'
...