PeroxiHUB: A Modular Cell-Free Biosensing Platform Using H2O2 as Signal Integrator
Cell-free systems have great potential for delivering robust, inexpensive, and field-deployable biosensors. Many cell-free biosensors rely on transcription factors responding to small molecules, but their discovery and implementation still remain challenging. Here we report the engineering of Peroxi...
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Veröffentlicht in: | ACS synthetic biology 2022-08, Vol.11 (8), p.2578-2588 |
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Sprache: | eng |
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Zusammenfassung: | Cell-free systems have great potential for delivering robust, inexpensive, and field-deployable biosensors. Many cell-free biosensors rely on transcription factors responding to small molecules, but their discovery and implementation still remain challenging. Here we report the engineering of PeroxiHUB, an optimized H2O2-centered sensing platform supporting cell-free detection of different metabolites. H2O2 is a central metabolite and a byproduct of numerous enzymatic reactions. PeroxiHUB uses enzymatic transducers to convert metabolites of interest into H2O2, enabling rapid reprogramming of sensor specificity using alternative transducers. We first screen several transcription factors and optimize OxyR for the transcriptional response to H2O2 in a cell-free system, highlighting the need for preincubation steps to obtain suitable signal-to-noise ratios. We then demonstrate modular detection of metabolites of clinical interestlactate, sarcosine, and cholineusing different transducers mined via a custom retrosynthesis workflow publicly available on the SynBioCAD Galaxy portal. We find that expressing the transducer during the preincubation step is crucial for optimal sensor operation. We then show that different reporters can be connected to PeroxiHUB, providing high adaptability for various applications. Finally, we demonstrate that a peroxiHUB lactate biosensor can detect endogenous levels of this metabolite in clinical samples. Given the wide range of enzymatic reactions producing H2O2, the PeroxiHUB platform will support cell-free detection of a large number of metabolites in a modular and scalable fashion. |
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ISSN: | 2161-5063 2161-5063 |
DOI: | 10.1021/acssynbio.2c00138 |