---
_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
license: https://creativecommons.org/licenses/by/4.0/
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: '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: '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'
...