---
_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
license: https://creativecommons.org/licenses/by/4.0/
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: '9711'
article_processing_charge: No
author:
- first_name: Guillaume
full_name: Chevereau, Guillaume
id: 424D78A0-F248-11E8-B48F-1D18A9856A87
last_name: Chevereau
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
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 Hazal
full_name: Ayhan, Dilay Hazal
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. Excel file containing the raw
data for all figures. 2015. doi:10.1371/journal.pbio.1002299.s001
apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak,
E., & Bollenbach, M. T. (2015). Excel file containing the raw data for all
figures. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299.s001
chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek,
Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Excel File Containing
the Raw Data for All Figures.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s001.
ieee: G. Chevereau et al., “Excel file containing the raw data for all figures.”
Public Library of Science, 2015.
ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach
MT. 2015. Excel file containing the raw data for all figures, Public Library of
Science, 10.1371/journal.pbio.1002299.s001.
mla: Chevereau, Guillaume, et al. Excel File Containing the Raw Data for All
Figures. Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.s001.
short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak,
M.T. Bollenbach, (2015).
date_created: 2021-07-23T11:53:50Z
date_published: 2015-11-18T00:00:00Z
date_updated: 2023-02-23T10:07:02Z
day: '18'
department:
- _id: ToBo
doi: 10.1371/journal.pbio.1002299.s001
month: '11'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1619'
relation: used_in_publication
status: public
status: public
title: Excel file containing the raw data for all figures
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2015'
...
---
_id: '9765'
article_processing_charge: No
author:
- first_name: Guillaume
full_name: Chevereau, Guillaume
id: 424D78A0-F248-11E8-B48F-1D18A9856A87
last_name: Chevereau
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
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 Hazal
full_name: Ayhan, Dilay Hazal
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. Gene ontology enrichment analysis
for the most sensitive gene deletion strains for all drugs. 2015. doi:10.1371/journal.pbio.1002299.s008
apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak,
E., & Bollenbach, M. T. (2015). Gene ontology enrichment analysis for the
most sensitive gene deletion strains for all drugs. Public Library of Science.
https://doi.org/10.1371/journal.pbio.1002299.s008
chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek,
Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Gene Ontology Enrichment
Analysis for the Most Sensitive Gene Deletion Strains for All Drugs.” Public Library
of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s008.
ieee: G. Chevereau et al., “Gene ontology enrichment analysis for the most
sensitive gene deletion strains for all drugs.” Public Library of Science, 2015.
ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach
MT. 2015. Gene ontology enrichment analysis for the most sensitive gene deletion
strains for all drugs, Public Library of Science, 10.1371/journal.pbio.1002299.s008.
mla: Chevereau, Guillaume, et al. Gene Ontology Enrichment Analysis for the Most
Sensitive Gene Deletion Strains for All Drugs. Public Library of Science,
2015, doi:10.1371/journal.pbio.1002299.s008.
short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak,
M.T. Bollenbach, (2015).
date_created: 2021-08-03T07:05:16Z
date_published: 2015-11-18T00:00:00Z
date_updated: 2023-02-23T10:07:02Z
day: '18'
department:
- _id: ToBo
doi: 10.1371/journal.pbio.1002299.s008
month: '11'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1619'
relation: used_in_publication
status: public
status: public
title: Gene ontology enrichment analysis for the most sensitive gene deletion strains
for all drugs
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2015'
...
---
_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: 2024-03-18T23:30:29Z
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'
...