bioRxiv ScienceSearch

bioRxiv · 10.1101/150375

Hands-free control of heterologous gene expression in batch cultures

Abstract

BackgroundAutonomous cell-based control of heterologous gene expression can simplify batch-culture bioprocessing by eliminating external monitoring and extrinsic control of culture conditions. Existing approaches use auto-induction media, synthetic cell-cell communication systems, or application-specific biosensors. A simpler, resource-efficient, and general-purpose expression control system responsive to common changes during batch culture would be useful.\n\nResultsWe used native E.coli promoters and recombinase-based switches to repurpose endogenous transcription signals for control of heterologous gene expression. Specifically, natural changes in transcription from endogenous promoters result in recombinase expression at different phases of batch culture. So-expressed recombinases invert a constitutive promoter regulating expression of arbitrary heterologous genes. We realized reversible and single-use switching, reduced static and dynamic cell-to-cell variation, and overall expression amplification. We used \"off-the-shelf\" genetic parts and abstraction-based composition frameworks to realize reliable forward engineering of our synthetic genetic systems.\n\nConclusionWe engineered autonomous control systems for regulating heterologous gene expression. Our system uses generic endogenous promoters to sense and control heterologous expression during growth-phase transitions. Our system does not require specialized auto-induction media, production or activation of quorum sensing, or the development of application-specific biosensors. Cells programmed to control themselves could simplify existing bioprocess operations and enable the development of more powerful synthetic genetic systems.

Explore related subjects

Keep this discovery

BibTeXRIS

Borkowski, O., Endy, D., Subsoontorn, P.. 2017-06-16. Hands-free control of heterologous gene expression in batch cultures. https://doi.org/10.1101/150375

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Living electronic transistors with tunable conductivity

Electroactive bacteria, like Shewanella oneidensis, can couple the oxidation of organic electron donors to the reduction of external conductive surfaces, such as minerals and electrodes, by utilizing multiheme cytochromes to carry charge from within the cell to external surfaces. Additionally, multiheme cytochromes facilitate gateable, long-distance (micrometer-scale) redox conduction along the outer membrane and across multiple cells bridging electrodes. While electroactive microbes are being used to develop bioelectrochemical devices, there have been limited efforts to use synthetic biology to exert additional control over microbes serving as device components. Thus, this work implements an optogenetic biofilm patterning gene circuit and a small molecule sensor in S. oneidensis to simultaneously control cell deposition and cytochrome expression. This allows for photolithographic patterning of biofilms possessing tunable electrical properties controlled with small molecules. This system demonstrates tunable electrochemical activity, redox conduction, intrinsic biofilm conductivity, and negative differential transconductance as a function of cytochrome expression. Additionally, temperature-dependent measurements of this tunable biofilm conduction reveal changes in activation energy as a function of cytochrome expression. Through this combination of synthetic biology and electrochemistry, simultaneous control over biofilm geometry and conductivity sheds light on fundamental microbial electron transport processes, and it enables the construction of living electronic devices.

synthetic biology

Evolutionary stabilisation of stressful metabolism via integrated biocomputing and essential-gene metabolic locking circuits

Synthetic genetic circuits enable microbial differentiation from growth to production, yet metabolic burden, imbalance and toxicity frequently drive strain degeneration. Yeast strains engineered to produce different terpene products exhibited divergent genetic responses to metabolic stresses, but commonly underwent progressive loss of induction of synthetic GAL regulatory circuits, either across the entire population or within subpopulations. Using di- and tri-input biocomputing circuits, the essential glutamine synthetase gene GLN1 was coupled to GAL induction, thereby enabling stabilisation and evolutionary adaptation of the synthetic genetic circuits and stressful heterologous terpene synthetic pathways. The integrated biocomputing and metabolic coupling circuit systems not only prevent strain degeneration but also enable interrogation of non-degenerative evolutionary shifts, providing a platform for metabolic engineering optimisation.

synthetic biology

Unbiased and scalable reduction of diverse bacterial genomes

The genome is a complex, integrated system where the functions and regulatory interactions of its many components remain poorly understood. Genome minimization aims to reduce genomic complexity by removing non-essential elements to reveal the fundamental building blocks of cellular life. However, current minimization strategies are often slow and species-specific due to a reliance on prior information, and limited to producing single, isolated strains, which obscures the diverse ways a genome can adapt to large-scale DNA removal. Here we show the development and application of Stochastic Lineage-based Iterative Minimization (SLIM) a modular, high-throughput platform for unbiased genome reduction across phylogenetically diverse bacteria. We apply SLIM to generate a library of genome-reduced Escherichia coli lineages. We then interrogate the lineages, identifying both universal and lineage-specific transcriptional and translational reprogramming in response to deletions. We demonstrate that these expression dynamics drive environment-dependent fitness, allowing us to pinpoint a single gene deletion in one genome-reduced lineage as the driver of a measurable environmental growth defect. Beyond E. coli, we successfully deploy SLIM in phylogenetically distinct bacterial taxa to rapidly reduce the genomes of Shigella flexneri and Pseudomonas putida, distinct genus and order respectively from E. coli, without species-specific optimization. Our results establish a scalable, generalizable framework for navigating the vast landscape of minimized genomes, providing a powerful new tool for functional discovery and the rational design of synthetic genomic chassis.

synthetic biology