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Self-adjusting synthetic gene circuit for correcting insulin resistance.

ABSTRACT: By using tools from synthetic biology, sophisticated genetic devices can be assembled to reprogram mammalian cell activities. Here, we demonstrate that a self-adjusting synthetic gene circuit can be designed to sense and reverse the insulin-resistance syndrome in different mouse models. By functionally rewiring the mitogen-activated protein kinase (MAPK) signalling pathway to produce MAPK-mediated activation of the hybrid transcription factor TetR-ELK1, we assembled a synthetic insulin-sensitive transcription-control device that self-sufficiently distinguished between physiological and increased blood insulin levels and correspondingly fine-tuned the reversible expression of therapeutic transgenes from synthetic TetR-ELK1-specific promoters. In acute experimental hyperinsulinemia, the synthetic insulin-sensing designer circuit reversed the insulin-resistance syndrome by coordinating expression of the insulin-sensitizing compound adiponectin. Engineering synthetic gene circuits to sense pathologic markers and coordinate the expression of therapeutic transgenes may provide opportunities for future gene- and cell-based treatments of multifactorial metabolic disorders.

SUBMITTER: Ye H

PROVIDER: S-EPMC5412959 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Self-adjusting synthetic gene circuit for correcting insulin resistance.

Ye Haifeng H Xie Mingqi M Xue Shuai S Charpin-El Hamri Ghislaine G Yin Jianli J Zulewski Henryk H Fussenegger Martin M

Nature biomedical engineering 20161219 1

By using tools from synthetic biology, sophisticated genetic devices can be assembled to reprogram mammalian cell activities. Here, we demonstrate that a self-adjusting synthetic gene circuit can be designed to sense and reverse the insulin-resistance syndrome in different mouse models. By functionally rewiring the mitogen-activated protein kinase (MAPK) signalling pathway to produce MAPK-mediated activation of the hybrid transcription factor TetR-ELK1, we assembled a synthetic insulin-sensitive ...[more]

PMID: 28480128

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Project description:Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a "sweet spot" of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings.

Dataset Information

Self-adjusting synthetic gene circuit for correcting insulin resistance.

Publications

Self-adjusting synthetic gene circuit for correcting insulin resistance.

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