---
_id: '570'
abstract:
- lang: eng
  text: 'Most phenotypes are determined by molecular systems composed of specifically
    interacting molecules. However, unlike for individual components, little is known
    about the distributions of mutational effects of molecular systems as a whole.
    We ask how the distribution of mutational effects of a transcriptional regulatory
    system differs from the distributions of its components, by first independently,
    and then simultaneously, mutating a transcription factor and the associated promoter
    it represses. We find that the system distribution exhibits increased phenotypic
    variation compared to individual component distributions - an effect arising from
    intermolecular epistasis between the transcription factor and its DNA-binding
    site. In large part, this epistasis can be qualitatively attributed to the structure
    of the transcriptional regulatory system and could therefore be a common feature
    in prokaryotes. Counter-intuitively, intermolecular epistasis can alleviate the
    constraints of individual components, thereby increasing phenotypic variation
    that selection could act on and facilitating adaptive evolution. '
article_number: e28921
author:
- first_name: Mato
  full_name: Lagator, Mato
  id: 345D25EC-F248-11E8-B48F-1D18A9856A87
  last_name: Lagator
- first_name: Srdjan
  full_name: Sarikas, Srdjan
  id: 35F0286E-F248-11E8-B48F-1D18A9856A87
  last_name: Sarikas
- first_name: Hande
  full_name: Acar, Hande
  id: 2DDF136A-F248-11E8-B48F-1D18A9856A87
  last_name: Acar
  orcid: 0000-0003-1986-9753
- first_name: Jonathan P
  full_name: Bollback, Jonathan P
  id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87
  last_name: Bollback
  orcid: 0000-0002-4624-4612
- first_name: Calin C
  full_name: Guet, Calin C
  id: 47F8433E-F248-11E8-B48F-1D18A9856A87
  last_name: Guet
  orcid: 0000-0001-6220-2052
citation:
  ama: Lagator M, Sarikas S, Acar H, Bollback JP, Guet CC. Regulatory network structure
    determines patterns of intermolecular epistasis. <i>eLife</i>. 2017;6. doi:<a
    href="https://doi.org/10.7554/eLife.28921">10.7554/eLife.28921</a>
  apa: Lagator, M., Sarikas, S., Acar, H., Bollback, J. P., &#38; Guet, C. C. (2017).
    Regulatory network structure determines patterns of intermolecular epistasis.
    <i>ELife</i>. eLife Sciences Publications. <a href="https://doi.org/10.7554/eLife.28921">https://doi.org/10.7554/eLife.28921</a>
  chicago: Lagator, Mato, Srdjan Sarikas, Hande Acar, Jonathan P Bollback, and Calin
    C Guet. “Regulatory Network Structure Determines Patterns of Intermolecular Epistasis.”
    <i>ELife</i>. eLife Sciences Publications, 2017. <a href="https://doi.org/10.7554/eLife.28921">https://doi.org/10.7554/eLife.28921</a>.
  ieee: M. Lagator, S. Sarikas, H. Acar, J. P. Bollback, and C. C. Guet, “Regulatory
    network structure determines patterns of intermolecular epistasis,” <i>eLife</i>,
    vol. 6. eLife Sciences Publications, 2017.
  ista: Lagator M, Sarikas S, Acar H, Bollback JP, Guet CC. 2017. Regulatory network
    structure determines patterns of intermolecular epistasis. eLife. 6, e28921.
  mla: Lagator, Mato, et al. “Regulatory Network Structure Determines Patterns of
    Intermolecular Epistasis.” <i>ELife</i>, vol. 6, e28921, eLife Sciences Publications,
    2017, doi:<a href="https://doi.org/10.7554/eLife.28921">10.7554/eLife.28921</a>.
  short: M. Lagator, S. Sarikas, H. Acar, J.P. Bollback, C.C. Guet, ELife 6 (2017).
date_created: 2018-12-11T11:47:14Z
date_published: 2017-11-13T00:00:00Z
date_updated: 2021-01-12T08:03:15Z
day: '13'
ddc:
- '576'
department:
- _id: CaGu
- _id: JoBo
- _id: NiBa
doi: 10.7554/eLife.28921
ec_funded: 1
file:
- access_level: open_access
  checksum: 273ab17f33305e4eaafd911ff88e7c5b
  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T10:14:42Z
  date_updated: 2020-07-14T12:47:10Z
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  content_type: application/pdf
  creator: system
  date_created: 2018-12-12T10:14:43Z
  date_updated: 2020-07-14T12:47:10Z
  file_id: '5097'
  file_name: IST-2017-918-v1+2_elife-28921-v3.pdf
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  relation: main_file
file_date_updated: 2020-07-14T12:47:10Z
has_accepted_license: '1'
intvolume: '         6'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '291734'
  name: International IST Postdoc Fellowship Programme
- _id: 2578D616-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '648440'
  name: Selective Barriers to Horizontal Gene Transfer
publication: eLife
publication_identifier:
  issn:
  - 2050084X
publication_status: published
publisher: eLife Sciences Publications
publist_id: '7244'
pubrep_id: '918'
quality_controlled: '1'
scopus_import: 1
status: public
title: Regulatory network structure determines patterns of intermolecular epistasis
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2017'
...
---
_id: '1121'
abstract:
- lang: eng
  text: "Horizontal gene transfer (HGT), the lateral acquisition of genes across existing
    species\r\nboundaries, is a major evolutionary force shaping microbial genomes
    that facilitates\r\nadaptation to new environments as well as resistance to antimicrobial
    drugs. As such,\r\nunderstanding the mechanisms and constraints that determine
    the outcomes of HGT\r\nevents is crucial to understand the dynamics of HGT and
    to design better strategies to\r\novercome the challenges that originate from
    it.\r\nFollowing the insertion and expression of a newly transferred gene, the
    success of an\r\nHGT event will depend on the fitness effect it has on the recipient
    (host) cell. Therefore,\r\npredicting the impact of HGT on the genetic composition
    of a population critically\r\ndepends on the distribution of fitness effects (DFE)
    of horizontally transferred genes.\r\nHowever, to date, we have little knowledge
    of the DFE of newly transferred genes, and\r\nhence little is known about the
    shape and scale of this distribution.\r\nIt is particularly important to better
    understand the selective barriers that determine\r\nthe fitness effects of newly
    transferred genes. In spite of substantial bioinformatics\r\nefforts to identify
    horizontally transferred genes and selective barriers, a systematic\r\nexperimental
    approach to elucidate the roles of different selective barriers in defining\r\nthe
    fate of a transfer event has largely been absent. Similarly, although the fact
    that\r\nenvironment might alter the fitness effect of a horizontally transferred
    gene may seem\r\nobvious, little attention has been given to it in a systematic
    experimental manner.\r\nIn this study, we developed a systematic experimental
    approach that consists of\r\ntransferring 44 arbitrarily selected Salmonella typhimurium
    orthologous genes into an\r\nEscherichia coli host, and estimating the fitness
    effects of these transferred genes at a\r\nconstant expression level by performing
    competition assays against the wild type.\r\nIn chapter 2, we performed one-to-one
    competition assays between a mutant strain\r\ncarrying a transferred gene and
    the wild type strain. By using flow cytometry we\r\nestimated selection coefficients
    for the transferred genes with a precision level of 10-3,and obtained the DFE
    of horizontally transferred genes. We then investigated if these\r\nfitness effects
    could be predicted by any of the intrinsic properties of the genes, namely,\r\nfunctional
    category, degree of complexity (protein-protein interactions), GC content,\r\ncodon
    usage and length. Our analyses revealed that the functional category and length\r\nof
    the genes act as potential selective barriers. Finally, using the same procedure
    with\r\nthe endogenous E. coli orthologs of these 44 genes, we demonstrated that
    gene dosage is\r\nthe most prominent selective barrier to HGT.\r\nIn chapter 3,
    using the same set of genes we investigated the role of environment on the\r\nsuccess
    of HGT events. Under six different environments with different levels of stress\r\nwe
    performed more complex competition assays, where we mixed all 44 mutant strains\r\ncarrying
    transferred genes with the wild type strain. To estimate the fitness effects of\r\ngenes
    relative to wild type we used next generation sequencing. We found that the DFEs\r\nof
    horizontally transferred genes are highly dependent on the environment, with\r\nabundant
    gene–by-environment interactions. Furthermore, we demonstrated a\r\nrelationship
    between average fitness effect of a gene across all environments and its\r\nenvironmental
    variance, and thus its predictability. Finally, in spite of the fitness effects\r\nof
    genes being highly environment-dependent, we still observed a common shape of\r\nDFEs
    across all tested environments."
acknowledgement: "This study was supported by European Research Council ERC CoG 2014
  – EVOLHGT,\r\nunder the grant number 648440.\r\n\r\nIt is a pleasure to thank the
  many people who made this thesis possible.\r\nI would like to first thank my advisor,
  Jonathan Paul Bollback for providing guidance in\r\nall aspects of my life, encouragement,
  sound advice, and good teaching over the last six\r\nyears.\r\nI would also like
  to thank the members of my dissertation committee – Călin C. Guet\r\nand John F.
  Baines – not only for their time and guidance, but for their intellectual\r\ncontributions
  to my development as a scientist.\r\nI would like to thank Flavia Gama and Rodrigo
  Redondo who have taught me all the\r\nskills in the laboratory with their graciousness
  and friendship. Also special thanks to\r\nBollback group for their support and for
  providing a stimulating and fun environment:\r\nIsabella Tomanek, Fabienne Jesse,
  Claudia Igler, and Pavel Payne.\r\nJerneja Beslagic is not only an amazing assistant,
  she also has a smile brighter and\r\nwarmer than the sunshine, bringing happiness
  to every moment. Always keep your light\r\nNeja, I will miss our invaluable chatters
  a lot."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Hande
  full_name: Acar, Hande
  id: 2DDF136A-F248-11E8-B48F-1D18A9856A87
  last_name: Acar
  orcid: 0000-0003-1986-9753
citation:
  ama: Acar H. Selective barriers to horizontal gene transfer. 2016.
  apa: Acar, H. (2016). <i>Selective barriers to horizontal gene transfer</i>. Institute
    of Science and Technology Austria.
  chicago: Acar, Hande. “Selective Barriers to Horizontal Gene Transfer.” Institute
    of Science and Technology Austria, 2016.
  ieee: H. Acar, “Selective barriers to horizontal gene transfer,” Institute of Science
    and Technology Austria, 2016.
  ista: Acar H. 2016. Selective barriers to horizontal gene transfer. Institute of
    Science and Technology Austria.
  mla: Acar, Hande. <i>Selective Barriers to Horizontal Gene Transfer</i>. Institute
    of Science and Technology Austria, 2016.
  short: H. Acar, Selective Barriers to Horizontal Gene Transfer, Institute of Science
    and Technology Austria, 2016.
date_created: 2018-12-11T11:50:16Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2023-09-07T11:42:26Z
day: '01'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: JoBo
ec_funded: 1
file:
- access_level: closed
  checksum: 94bbbc754c36115bf37f8fc11fad43c4
  content_type: application/pdf
  creator: dernst
  date_created: 2019-08-13T11:17:50Z
  date_updated: 2019-08-13T11:17:50Z
  file_id: '6814'
  file_name: PhDThesis_HandeAcar_1230.pdf
  file_size: 3682711
  relation: main_file
- access_level: open_access
  checksum: 94bbbc754c36115bf37f8fc11fad43c4
  content_type: application/pdf
  creator: dernst
  date_created: 2021-02-22T11:51:13Z
  date_updated: 2021-02-22T11:51:13Z
  file_id: '9184'
  file_name: 2016_Thesis_HandeAcar.pdf
  file_size: 3682711
  relation: main_file
  success: 1
file_date_updated: 2021-02-22T11:51:13Z
has_accepted_license: '1'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: '75'
project:
- _id: 2578D616-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '648440'
  name: Selective Barriers to Horizontal Gene Transfer
publication_identifier:
  issn:
  - 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '6239'
status: public
supervisor:
- first_name: Jonathan P
  full_name: Bollback, Jonathan P
  id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87
  last_name: Bollback
  orcid: 0000-0002-4624-4612
title: Selective barriers to horizontal gene transfer
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2016'
...
---
_id: '1902'
abstract:
- lang: eng
  text: In the 1960s-1980s, determination of bacterial growth rates was an important
    tool in microbial genetics, biochemistry, molecular biology, and microbial physiology.
    The exciting technical developments of the 1990s and the 2000s eclipsed that tool;
    as a result, many investigators today lack experience with growth rate measurements.
    Recently, investigators in a number of areas have started to use measurements
    of bacterial growth rates for a variety of purposes. Those measurements have been
    greatly facilitated by the availability of microwell plate readers that permit
    the simultaneous measurements on up to 384 different cultures. Only the exponential
    (logarithmic) portions of the resulting growth curves are useful for determining
    growth rates, and manual determination of that portion and calculation of growth
    rates can be tedious for high-throughput purposes. Here, we introduce the program
    GrowthRates that uses plate reader output files to automatically determine the
    exponential portion of the curve and to automatically calculate the growth rate,
    the maximum culture density, and the duration of the growth lag phase. GrowthRates
    is freely available for Macintosh, Windows, and Linux.We discuss the effects of
    culture volume, the classical bacterial growth curve, and the differences between
    determinations in rich media and minimal (mineral salts) media. This protocol
    covers calibration of the plate reader, growth of culture inocula for both rich
    and minimal media, and experimental setup. As a guide to reliability, we report
    typical day-to-day variation in growth rates and variation within experiments
    with respect to position of wells within the plates.
article_processing_charge: No
article_type: original
author:
- first_name: Barry
  full_name: Hall, Barry
  last_name: Hall
- first_name: Hande
  full_name: Acar, Hande
  id: 2DDF136A-F248-11E8-B48F-1D18A9856A87
  last_name: Acar
  orcid: 0000-0003-1986-9753
- first_name: Anna
  full_name: Nandipati, Anna
  last_name: Nandipati
- first_name: Miriam
  full_name: Barlow, Miriam
  last_name: Barlow
citation:
  ama: Hall B, Acar H, Nandipati A, Barlow M. Growth rates made easy. <i>Molecular
    Biology and Evolution</i>. 2014;31(1):232-238. doi:<a href="https://doi.org/10.1093/molbev/mst187">10.1093/molbev/mst187</a>
  apa: Hall, B., Acar, H., Nandipati, A., &#38; Barlow, M. (2014). Growth rates made
    easy. <i>Molecular Biology and Evolution</i>. Oxford University Press. <a href="https://doi.org/10.1093/molbev/mst187">https://doi.org/10.1093/molbev/mst187</a>
  chicago: Hall, Barry, Hande Acar, Anna Nandipati, and Miriam Barlow. “Growth Rates
    Made Easy.” <i>Molecular Biology and Evolution</i>. Oxford University Press, 2014.
    <a href="https://doi.org/10.1093/molbev/mst187">https://doi.org/10.1093/molbev/mst187</a>.
  ieee: B. Hall, H. Acar, A. Nandipati, and M. Barlow, “Growth rates made easy,” <i>Molecular
    Biology and Evolution</i>, vol. 31, no. 1. Oxford University Press, pp. 232–238,
    2014.
  ista: Hall B, Acar H, Nandipati A, Barlow M. 2014. Growth rates made easy. Molecular
    Biology and Evolution. 31(1), 232–238.
  mla: Hall, Barry, et al. “Growth Rates Made Easy.” <i>Molecular Biology and Evolution</i>,
    vol. 31, no. 1, Oxford University Press, 2014, pp. 232–38, doi:<a href="https://doi.org/10.1093/molbev/mst187">10.1093/molbev/mst187</a>.
  short: B. Hall, H. Acar, A. Nandipati, M. Barlow, Molecular Biology and Evolution
    31 (2014) 232–238.
date_created: 2018-12-11T11:54:37Z
date_published: 2014-01-01T00:00:00Z
date_updated: 2022-06-07T11:08:13Z
day: '01'
department:
- _id: JoBo
doi: 10.1093/molbev/mst187
external_id:
  pmid:
  - '24170494'
intvolume: '        31'
issue: '1'
language:
- iso: eng
month: '01'
oa_version: None
page: 232 - 238
pmid: 1
publication: Molecular Biology and Evolution
publication_identifier:
  eissn:
  - 1537-1719
  issn:
  - 0737-4038
publication_status: published
publisher: Oxford University Press
publist_id: '5193'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Growth rates made easy
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 31
year: '2014'
...