Evidence map›Paper›PMID 26324468›Full record

ArticleMolecular systems biology2015

Stress-response balance drives the evolution of a network module and its host genome.

Caleb González, Joe Christian J Ray, Michael Manhart, Rhys M Adams, Dmitry Nevozhay, Alexandre V Morozov, Gábor Balázsi

Open access · goldAbstract read
In one paragraph

Article in Molecular systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed
6.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

56 citing papers in PubMed, 96 citations in OpenAlex.

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  7. Adaptive DNA amplification of synthetic gene circuit opens a way to overcome cancer chemoresistance.Proceedings of the National Academy of Sciences of the United States of America · 2023
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  15. Using single-cell models to predict the functionality of synthetic circuits at the population scale.Proceedings of the National Academy of Sciences of the United States of America · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Caleb GonzálezDepartment of Systems Biology - Unit 950, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Joe Christian J RayDepartment of Systems Biology - Unit 950, The University of Texas MD Anderson Cancer Center, Houston, TX, USA Center for Computational Biology & Department of Molecular Biosciences, University of Kansas, Lawrence, KS, USA.
Michael ManhartDepartment of Physics & Astronomy, Rutgers University, Piscataway, NJ, USA Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
Rhys M AdamsDepartment of Systems Biology - Unit 950, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Dmitry NevozhayDepartment of Systems Biology - Unit 950, The University of Texas MD Anderson Cancer Center, Houston, TX, USA School of Biomedicine, Far Eastern Federal University, Vladivostok, Russia.
Alexandre V MorozovDepartment of Physics & Astronomy, Rutgers University, Piscataway, NJ, USA BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA.
Gábor BalázsiDepartment of Systems Biology - Unit 950, The University of Texas MD Anderson Cancer Center, Houston, TX, USA Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, NY, USA Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA gabor.balazsi@stonybrook.edu.
The University of Texas MD Anderson Cancer Center · USRutgers, The State University of New Jersey · USStony Brook University · USUniversity of Kansas · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Connecting the selection of noisy gene expression deviants to genetic evolutionDP2OD006481 · OD · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BALAZSI, GABOR · 2009 to 2009
$2.3M
NCI NIH HHS CA16672NCI NIH HHS P30 CA016672NIH HHS 1DP2 OD006481-01NIH HHS DP2 OD006481
6 · The paper itself

Abstract

Stress response genes and their regulators form networks that underlie drug resistance. These networks often have an inherent tradeoff: their expression is costly in the absence of stress, but beneficial in stress. They can quickly emerge in the genomes of infectious microbes and cancer cells, protecting them from treatment. Yet, the evolution of stress resistance networks is not well understood. Here, we use a two-component synthetic gene circuit integrated into the budding yeast genome to model experimentally the adaptation of a stress response module and its host genome in three different scenarios. In agreement with computational predictions, we find that: (i) intra-module mutations target and eliminate the module if it confers only cost without any benefit to the cell; (ii) intra- and extra-module mutations jointly activate the module if it is potentially beneficial and confers no cost; and (iii) a few specific mutations repeatedly fine-tune the module's noisy response if it has excessive costs and/or insufficient benefits. Overall, these findings reveal how the timing and mechanisms of stress response network evolution depend on the environment.

Indexed as

Anti-Bacterial AgentsAntibiotics, AntineoplasticBiological EvolutionBleomycinComputational BiologyDoxycyclineDrug ResistanceEvolution, MolecularGene Regulatory NetworksMicrobial Sensitivity TestsModels, GeneticSaccharomyces cerevisiaeStress, PhysiologicalAnti-Bacterial AgentsAntibiotics, AntineoplasticBleomycinDoxycyclineZeocindrug resistanceexperimental evolutionpositive feedbacksynthetic gene circuittradeoff

Identifiers

PMID26324468
PMCPMC4562500
OpenAlexW2155706782

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.