Synthetic BiologySynthetic BiologyPeer-reviewed journal article
In vitro transcription-based biosensing of glycolate for prototyping of a complex enzyme cascade
Sebastian BartheliD, Luca BrenkeriD, Christoph DiehliD, Nitin BohraiD, Simone GiaveriiD, Nicole PacziaiD, Tobias J. ErbiD
Abstract
In vitro metabolic systems allow the reconstitution of natural and new-to-nature pathways outside of their cellular context and are of increasing interest in bottom-up synthetic biology, cell-free manufacturing and metabolic engineering. Yet, the prototyping of such in vitro networks is very often restricted by time- and cost-intensive analytical methods. To overcome these limitations, we sought to develop an in vitro transcription (IVT)-based biosensing workflow that offers fast results at low-cost, minimal volumes and high-throughput. As a proof-of-concept, we present an IVT biosensor for the so-called CETCH cycle, a complex in vitro metabolic system that converts CO2 into glycolate. To quantify glycolate production, we constructed a sensor module that is based on the glycolate repressor GlcR from Paracoccus denitrificans, and established an IVT biosensing off-line workflow that allows to measure glycolate from CETCH samples from the {micro}M to mM range. We characterized the influence of different cofactors on IVT output and further optimized our IVT biosensor against varying sample conditions. We show that availability of free Mg2+ is a critical factor in IVT biosensing and that IVT output is heavily influenced by ATP, NADPH and other phosphorylated metabolites frequently used in in vitro systems. Our final biosensor is highly robust and shows an excellent correlation between IVT output and classical LC-MS quantification, but notably at [~]10-fold lowered cost and [~]10 times faster turnover time. Our results demonstrate the potential of IVT-based biosensor systems to break current limitations in biological design-build-test cycles for the prototyping of individual enzymes, complex reaction cascades and in vitro metabolic networks.
- Citations
- 6
- FWCI
- 0.82
- Field percentile
- top 32%
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