Literature DB >> 31734160

Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.

Martin Lukačišin1, Tobias Bollenbach2.   

Abstract

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.
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  dimensionality reduction; drug combinations; drug interactions; emergent response; gene expression; general principles of living systems; isogrowth profiling; microbial growth; mitochondrial translation; myriocin

Year:  2019        PMID: 31734160     DOI: 10.1016/j.cels.2019.10.004

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  9 in total

1.  Extreme Antagonism Arising from Gene-Environment Interactions.

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Review 2.  Expanding the search for small-molecule antibacterials by multidimensional profiling.

Authors:  Karin Ortmayr; Roberto de la Cruz Moreno; Mattia Zampieri
Journal:  Nat Chem Biol       Date:  2022-05-23       Impact factor: 16.174

3.  Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance.

Authors:  Erida Gjini; Kevin B Wood
Journal:  Elife       Date:  2021-07-22       Impact factor: 8.140

4.  Hidden suppressive interactions are common in higher-order drug combinations.

Authors:  Natalie Ann Lozano-Huntelman; April Zhou; Elif Tekin; Mauricio Cruz-Loya; Bjørn Østman; Sada Boyd; Van M Savage; Pamela Yeh
Journal:  iScience       Date:  2021-03-26

5.  Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast.

Authors:  Rosemary Yu; Egor Vorontsov; Carina Sihlbom; Jens Nielsen
Journal:  Elife       Date:  2021-03-15       Impact factor: 8.140

6.  Intron-mediated induction of phenotypic heterogeneity.

Authors:  Martin Lukačišin; Adriana Espinosa-Cantú; Tobias Bollenbach
Journal:  Nature       Date:  2022-04-20       Impact factor: 69.504

7.  Single-cell isogrowth profiling: Uniform inhibition uncovers non-uniform drug responses.

Authors:  Martin Lukačišin; Adriana Espinosa-Cantú; Tobias Bollenbach
Journal:  Clin Transl Med       Date:  2022-08

8.  Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design.

Authors:  Jennifer Brennan; Lalit Jain; Sofia Garman; Ann E Donnelly; Erik Scott Wright; Kevin Jamieson
Journal:  PLoS Comput Biol       Date:  2022-07-18       Impact factor: 4.779

9.  SynergyFinder 2.0: visual analytics of multi-drug combination synergies.

Authors:  Aleksandr Ianevski; Anil K Giri; Tero Aittokallio
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

  9 in total

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