---
_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
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: 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. <i>PLoS Biology</i>. 2015;13(11).
    doi:<a href="https://doi.org/10.1371/journal.pbio.1002299">10.1371/journal.pbio.1002299</a>
  apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D., Toprak,
    E., &#38; Bollenbach, M. T. (2015). Quantifying the determinants of evolutionary
    dynamics leading to drug resistance. <i>PLoS Biology</i>. Public Library of Science.
    <a href="https://doi.org/10.1371/journal.pbio.1002299">https://doi.org/10.1371/journal.pbio.1002299</a>
  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.” <i>PLoS Biology</i>. Public
    Library of Science, 2015. <a href="https://doi.org/10.1371/journal.pbio.1002299">https://doi.org/10.1371/journal.pbio.1002299</a>.
  ieee: G. Chevereau <i>et al.</i>, “Quantifying the determinants of evolutionary
    dynamics leading to drug resistance,” <i>PLoS Biology</i>, 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.” <i>PLoS Biology</i>, vol. 13, no. 11, e1002299,
    Public Library of Science, 2015, doi:<a href="https://doi.org/10.1371/journal.pbio.1002299">10.1371/journal.pbio.1002299</a>.
  short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak,
    M.T. Bollenbach, PLoS Biology 13 (2015).
corr_author: '1'
date_created: 2018-12-11T11:53:04Z
date_published: 2015-11-18T00:00:00Z
date_updated: 2026-03-11T23:30:51Z
day: '18'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.1371/journal.pbio.1002299
ec_funded: 1
external_id:
  isi:
  - '000365898900011'
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'
isi: 1
issue: '11'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
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:
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    status: public
  - id: '9765'
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  - 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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 13
year: '2015'
...