---
_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:
- access_level: open_access
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: '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: '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:
- _id: ToBo
- _id: GaTk
doi: 10.1016/j.cels.2017.03.001
ec_funded: 1
file:
- access_level: open_access
checksum: 04ff20011c3d9a601c514aa999a5fe1a
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:13:54Z
date_updated: 2020-07-14T12:47:35Z
file_id: '5041'
file_name: IST-2017-901-v1+1_1-s2.0-S2405471217300868-main.pdf
file_size: 2438660
relation: main_file
file_date_updated: 2020-07-14T12:47:35Z
has_accepted_license: '1'
intvolume: ' 4'
issue: '4'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '04'
oa: 1
oa_version: Published Version
page: 393 - 403
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
- _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:
- id: '818'
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: '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
file_size: 1387760
relation: main_file
file_date_updated: 2020-07-14T12:45:07Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 25EB3A80-B435-11E9-9278-68D0E5697425
grant_number: RGP0042/2013
name: Revealing the fundamental limits of cell growth
- _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: PLoS Biology
publication_status: published
publisher: Public Library of Science
publist_id: '5547'
pubrep_id: '468'
quality_controlled: '1'
related_material:
record:
- id: '9711'
relation: research_data
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
file:
- access_level: open_access
checksum: 1683bb0f42ef892a5b3b71a050d65d25
content_type: application/pdf
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
file_size: 1273573
relation: main_file
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'
...
---
_id: '2001'
abstract:
- lang: eng
text: Antibiotics affect bacterial cell physiology at many levels. Rather than just
compensating for the direct cellular defects caused by the drug, bacteria respond
to antibiotics by changing their morphology, macromolecular composition, metabolism,
gene expression and possibly even their mutation rate. Inevitably, these processes
affect each other, resulting in a complex response with changes in the expression
of numerous genes. Genome‐wide approaches can thus help in gaining a comprehensive
understanding of bacterial responses to antibiotics. In addition, a combination
of experimental and theoretical approaches is needed for identifying general principles
that underlie these responses. Here, we review recent progress in our understanding
of bacterial responses to antibiotics and their combinations, focusing on effects
at the levels of growth rate and gene expression. We concentrate on studies performed
in controlled laboratory conditions, which combine promising experimental techniques
with quantitative data analysis and mathematical modeling. While these basic research
approaches are not immediately applicable in the clinic, uncovering the principles
and mechanisms underlying bacterial responses to antibiotics may, in the long
term, contribute to the development of new treatment strategies to cope with and
prevent the rise of resistant pathogenic bacteria.
author:
- first_name: Karin
full_name: Mitosch, Karin
id: 39B66846-F248-11E8-B48F-1D18A9856A87
last_name: Mitosch
- 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, Bollenbach MT. Bacterial responses to antibiotics and their combinations.
Environmental Microbiology Reports. 2014;6(6):545-557. doi:10.1111/1758-2229.12190
apa: Mitosch, K., & Bollenbach, M. T. (2014). Bacterial responses to antibiotics
and their combinations. Environmental Microbiology Reports. Wiley. https://doi.org/10.1111/1758-2229.12190
chicago: Mitosch, Karin, and Mark Tobias Bollenbach. “Bacterial Responses to Antibiotics
and Their Combinations.” Environmental Microbiology Reports. Wiley, 2014.
https://doi.org/10.1111/1758-2229.12190.
ieee: K. Mitosch and M. T. Bollenbach, “Bacterial responses to antibiotics and their
combinations,” Environmental Microbiology Reports, vol. 6, no. 6. Wiley,
pp. 545–557, 2014.
ista: Mitosch K, Bollenbach MT. 2014. Bacterial responses to antibiotics and their
combinations. Environmental Microbiology Reports. 6(6), 545–557.
mla: Mitosch, Karin, and Mark Tobias Bollenbach. “Bacterial Responses to Antibiotics
and Their Combinations.” Environmental Microbiology Reports, vol. 6, no.
6, Wiley, 2014, pp. 545–57, doi:10.1111/1758-2229.12190.
short: K. Mitosch, M.T. Bollenbach, Environmental Microbiology Reports 6 (2014)
545–557.
date_created: 2018-12-11T11:55:08Z
date_published: 2014-06-22T00:00:00Z
date_updated: 2023-09-07T12:00:25Z
day: '22'
department:
- _id: ToBo
doi: 10.1111/1758-2229.12190
ec_funded: 1
intvolume: ' 6'
issue: '6'
language:
- iso: eng
month: '06'
oa_version: None
page: 545 - 557
project:
- _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: Environmental Microbiology Reports
publication_status: published
publisher: Wiley
publist_id: '5076'
quality_controlled: '1'
related_material:
record:
- id: '818'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Bacterial responses to antibiotics and their combinations
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2014'
...