Literature DB >> 33068537

Extreme Antagonism Arising from Gene-Environment Interactions.

Thomas P Wytock1, Manjing Zhang2, Adrian Jinich3, Aretha Fiebig4, Sean Crosson4, Adilson E Motter5.   

Abstract

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.
Copyright © 2020 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2020        PMID: 33068537      PMCID: PMC7732749          DOI: 10.1016/j.bpj.2020.09.038

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  95 in total

1.  RNA polymerase mutants found through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media.

Authors:  Tom M Conrad; Michael Frazier; Andrew R Joyce; Byung-Kwan Cho; Eric M Knight; Nathan E Lewis; Robert Landick; Bernhard Ø Palsson
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-05       Impact factor: 11.205

2.  Lethality and synthetic lethality in the genome-wide metabolic network of Escherichia coli.

Authors:  Cheol-Min Ghim; Kwang-Il Goh; Byungnam Kahng
Journal:  J Theor Biol       Date:  2005-06-21       Impact factor: 2.691

Review 3.  Perspective: Sign epistasis and genetic constraint on evolutionary trajectories.

Authors:  Daniel M Weinreich; Richard A Watson; Lin Chao
Journal:  Evolution       Date:  2005-06       Impact factor: 3.694

Review 4.  The regulatory roles and mechanism of transcriptional pausing.

Authors:  R Landick
Journal:  Biochem Soc Trans       Date:  2006-12       Impact factor: 5.407

5.  Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq.

Authors:  Daniel E Deatherage; Jeffrey E Barrick
Journal:  Methods Mol Biol       Date:  2014

6.  Properties of selected mutations and genotypic landscapes under Fisher's geometric model.

Authors:  François Blanquart; Guillaume Achaz; Thomas Bataillon; Olivier Tenaillon
Journal:  Evolution       Date:  2014-11-17       Impact factor: 3.694

7.  Epistatic interaction maps relative to multiple metabolic phenotypes.

Authors:  Evan S Snitkin; Daniel Segrè
Journal:  PLoS Genet       Date:  2011-02-10       Impact factor: 5.917

8.  Adaptive evolution of the lactose utilization network in experimentally evolved populations of Escherichia coli.

Authors:  Selwyn Quan; J Christian J Ray; Zakari Kwota; Trang Duong; Gábor Balázsi; Tim F Cooper; Russell D Monds
Journal:  PLoS Genet       Date:  2012-01-12       Impact factor: 5.917

9.  Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics.

Authors:  Jeff Maltas; Brian Krasnick; Kevin B Wood
Journal:  Mol Biol Evol       Date:  2020-05-01       Impact factor: 16.240

10.  When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition.

Authors:  Rafael Pena-Miller; David Laehnemann; Gunther Jansen; Ayari Fuentes-Hernandez; Philip Rosenstiel; Hinrich Schulenburg; Robert Beardmore
Journal:  PLoS Biol       Date:  2013-04-23       Impact factor: 8.029

View more
  3 in total

1.  Cross-species metabolomic analysis of tau- and DDT-related toxicity.

Authors:  Vrinda Kalia; Megan M Niedzwiecki; Joshua M Bradner; Fion K Lau; Faith L Anderson; Meghan L Bucher; Katherine E Manz; Alexa Puri Schlotter; Zoe Coates Fuentes; Kurt D Pennell; Martin Picard; Douglas I Walker; William T Hu; Dean P Jones; Gary W Miller
Journal:  PNAS Nexus       Date:  2022-05-03

2.  Enhancement and mapping of tolerance to salt stress and 5-fluorocytosine in synthetic yeast strains via SCRaMbLE.

Authors:  Jianping Kang; Jieyi Li; Zhou Guo; Sijie Zhou; Shuxin Su; Wenhai Xiao; Yi Wu; Yingjin Yuan
Journal:  Synth Syst Biotechnol       Date:  2022-04-16

3.  Dissecting the Fitness Costs of Complex Mutations.

Authors:  Pablo Yubero; Juan F Poyatos
Journal:  Mol Biol Evol       Date:  2021-09-27       Impact factor: 16.240

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.