Evolutionary regain of lost gene circuit function

Gouda, Kheir; Manhart, Michael; Balazsi,

doi:10.1101/804187

Cited by 6 publications

(13 citation statements)

References 61 publications

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“…This resource loading leads to a drop in the expression level of other genes, resulting in coupling between independently expressed genes and more generally to context-dependent gene expression. Moreover, squelching can be toxic to cells 34 , 36 – 38 and places a selective pressure against engineered circuits and the host cell, thus affecting both on evolutionary timelines 39 , 40 . As many established synthetic eukaryotic gene-regulation systems utilize TAs 14 , 17 , 41 – 44 , squelching is potentially a pervasive problem in eukaryotic synthetic biology.…”

Section: Introductionmentioning

confidence: 99%

An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells

et al. 2020

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Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a device’s output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is loading of transcriptional and translational resources, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource loading in engineered mammalian genetic systems and develop an endoribonuclease-based feedforward controller that can adapt the expression level of a gene of interest to significant resource loading in mammalian cells. Near-perfect adaptation to resource loads is facilitated by high production and catalytic rates of the endoribonuclease. Our design is portable across cell lines and enables predictable tuning of controller function. Ultimately, our controller is a general-purpose device for predictable, robust, and context-independent control of gene expression.

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Section: Introductionmentioning

confidence: 99%

An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells

et al. 2020

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“…Synthetic gene networks have been engineered to regulate drug resistance and have been shown to serve as well-characterized models of natural stress response modules in evolution experiments ( González et al, 2015 ; Bódi et al, 2017 ; Farquhar et al, 2019 ; Gouda et al, 2019 ).…”

Section: Synthetic Drug Resistance Gene Network and Antimicrobial Rementioning

confidence: 99%

“…The authors found an optimum on the fitness landscape that balances the costs and benefits of expressing a drug resistance gene in various experimental antibiotic inducer and drug conditions. In a subsequent microbial evolution study using the same positive feedback yeast strain, it was found that the synthetic gene network was fine-tuned by evolution to modulate the network’s noisy response and optimize fitness via specific “intra-circuit” and “extra-circuit” DNA mutations ( González et al, 2015 ), which can lead to loss of gene circuit function that can be regained in certain conditions under drug selection ( Gouda et al, 2019 ). The study by Gouda et al (2019) also suggests that slow growth due to antibiotics may allow cells to access hidden drug-resistant states prior to genetic changes.…”

Section: Synthetic Drug Resistance Gene Network and Antimicrobial Rementioning

confidence: 99%

“…In a subsequent microbial evolution study using the same positive feedback yeast strain, it was found that the synthetic gene network was fine-tuned by evolution to modulate the network’s noisy response and optimize fitness via specific “intra-circuit” and “extra-circuit” DNA mutations ( González et al, 2015 ), which can lead to loss of gene circuit function that can be regained in certain conditions under drug selection ( Gouda et al, 2019 ). The study by Gouda et al (2019) also suggests that slow growth due to antibiotics may allow cells to access hidden drug-resistant states prior to genetic changes. Computational models based on fitness and gene expression properties have been developed to predict specific aspects of evolutionary dynamics (including the speed at which the ancestral genome disappears from the population and the types and number of mutant alleles that establish in each experimental condition) in different inducer and drug conditions ( González et al, 2015 ).…”

Section: Synthetic Drug Resistance Gene Network and Antimicrobial Rementioning

confidence: 99%

“…Computational models based on fitness and gene expression properties have been developed to predict specific aspects of evolutionary dynamics (including the speed at which the ancestral genome disappears from the population and the types and number of mutant alleles that establish in each experimental condition) in different inducer and drug conditions ( González et al, 2015 ). These computational models were validated in the same studies by microbial evolution experiments on the genetically engineered positive feedback yeast strain ( Nevozhay et al, 2012 ; González et al, 2015 ; Gouda et al, 2019 ).…”

Section: Synthetic Drug Resistance Gene Network and Antimicrobial Rementioning

confidence: 99%

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Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology

Farquhar

Flohr

Charlebois

2020

Front. Bioeng. Biotechnol.

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Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of nongenetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.

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Extreme Antagonism Arising from Gene-Environment Interactions

Wytock

Zhang

Jinich

et al. 2020

Biophysical Journal

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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 due to 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, while non-antagonistic 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. Statement of Significance Mutations that are deleterious in the absence of an antibiotic can become beneficial in its presence, which is an example of an extreme antagonistic gene-environment interaction. Such antagonism is of biophysical significance because it reflects non-local interactions mediated by intracellular networks rather than direct physical or chemical interactions. We develop and apply a forwardevolution experimental approach to systematically identify these interactions using DNA and RNA sequencing. These analyses reveal differences in the expression of the translational machinery between antagonistic and non-antagonistic mutations. Our findings demonstrate how distinct single-base pair mutations within the same gene can have divergent phenotypic consequences, which give rise to antagonistic interactions that can be explored in addressing antibiotic resistance.

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Evolutionary regain of lost gene circuit function

Cited by 6 publications

References 61 publications

An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells

An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells

Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology

Extreme Antagonism Arising from Gene-Environment Interactions

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