Stress-response balance drives the evolution of a network module and its host genome.
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.
Department of Systems Biology - Unit 950, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Caleb
C
González
Department 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.
Joe Christian J
JC
Ray
Department of Physics & Astronomy, Rutgers University, Piscataway, NJ, USA Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
Michael
M
Manhart
Department of Systems Biology - Unit 950, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Rhys M
RM
Adams
Department 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.
Dmitry
D
Nevozhay
Department of Physics & Astronomy, Rutgers University, Piscataway, NJ, USA BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA.
Alexandre V
AV
Morozov
Department 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.
Gábor
G
Balázsi
D000900:Anti-Bacterial Agents / Q000494:pharmacology; D000903:Antibiotics, Antineoplastic / Q000494:pharmacology; D005075:Biological Evolution; D001761:Bleomycin / Q000494:pharmacology; D019295:Computational Biology; D004318:Doxycycline / Q000494:pharmacology; D004351:Drug Resistance / Q000235:genetics*; D019143:Evolution, Molecular; D053263:Gene Regulatory Networks / Q000235:genetics*; D008826:Microbial Sensitivity Tests; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D013312:Stress, Physiological / Q000235:genetics* / Q000502:physiology
D000900:Anti-Bacterial Agents; D000903:Antibiotics, Antineoplastic; D001761:Bleomycin; C105427:Zeocin; D004318:Doxycycline
drug resistance; experimental evolution; positive feedback; synthetic gene circuit; tradeoff