Design and optimization of genetically encoded biosensors for high-throughput screening of chemicals

Lim, Hyun Gyu; Jang, Se Bok; Jang, Sungyeon; Seo, Sang Woo; Jung, Gyoo Yeol

doi:10.1016/j.copbio.2018.01.011

Cited by 73 publications

(67 citation statements)

References 49 publications

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“…Before tuning the dynamic range, we assessed the operational range of the toehold-switch-based riboswitch circuit. The operational range of an intracellular biosensor is critical for its successful application in metabolic engineering, such as in high-throughput screening of metabolite producers [5,28]. For synthetic riboswitch circuits, the operational range and half-maximal effective concentration (EC 50 ) are typically determined by the binding affinities between the genetic circuit components that are not easy to be adjusted [29].…”

Section: Construction Of Toehold Switch-based Genetic Modulatormentioning

confidence: 99%

“…One important synthetic biology research direction is to embed synthetic biological circuits in microbial cells to control their responses to environmental inputs, mainly by designing novel genetic circuits [2]. Simple genetic parts are assembled to construct complex genetic circuits with useful functions, and numerous applications utilizing genetic circuits have been reported such as monitoring of small molecules, control of metabolic pathways, directed evolution of enzymes, and logic computation [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Tight and strong gene expression regulation is highly desirable to maximize the regulatory outcome while minimizing leaky expression that can contribute to the gene expression noise and unnecessary consumption of cellular resources. Further, the operational range must encompass the expected ligand concentration range, which is specific to each application [3,5]. In particular, to improve metabolite-producing microbial strains, biosensors that operate at high concentrations of ligands need to be engineered because the operational range of biosensors is often significantly lower than the chemical productivity of the optimized strains [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Toehold Switch-Based Genetic Modulator for Tuning of Riboswitch Sensor

Hwang¹,

Kim²,

Jang³

et al. 2020

Preprint

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BackgroundSynthetic biological circuits are widely utilized to control microbial cell functions. Natural and synthetic riboswitches are attractive classes of sensor modules for use in synthetic biological applications. However, tuning the dose-response parameters of riboswitch circuits is challenging because considerable understanding of riboswitch mechanism and screening of mutant libraries are generally required. Therefore, novel molecular parts and strategies for controlling the dose-response parameters of riboswitch circuits are needed.ResultsHere, we developed a toehold switch-based genetic modulator that combines a previously reported hybrid input construct, which consists of riboswitch and transcriptional repressor, and de-novo-designed riboregulators named as toehold switches. First, the introduction of a pair of toehold switch and trigger as a downstream signal-processing module resulted in a functional riboswitch circuit. Next, several optimization strategies that focused on the stoichiometric ratio of RNA components greatly improved the fold-change. Finally, further characterizations confirmed low leakiness and high orthogonality for multiple toehold switches, indicating its applicability in riboswitch circuits in a seamless manner.ConclusionsThe toehold switch-based genetic modulator improved the dynamic range and dramatically shifted the operational range compared to the previous sensors only with hybrid input construct. The programmable RNA-RNA interactions amenable to in silico design and optimization can facilitate further development of RNA-based genetic modulators for flexible tuning of riboswitch circuitry and synthetic biosensors.

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Section: Construction Of Toehold Switch-based Genetic Modulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Toehold Switch-Based Genetic Modulator for Tuning of Riboswitch Sensor

Hwang¹,

Kim²,

Jang³

et al. 2020

Preprint

Self Cite

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“…One recently described approach, Capture-SELEX 20,21 , is unique in that it does not require chemical modification of the ligand; however, the method depends upon specific binding-mediated conformational changes to separate binders, a requirement separate from any conformational changes needed to ensure in vivo functionality of a biosensor. Furthermore, all of the methods which select for an isolated aptamer do not directly form the basis of an in vivo biosensor without further development and optimization 22 . In the second category are methods which jointly select for aptamers in the context of a biosensor platform, all of which are based on integration of the aptamer with a ribozyme where binding modulates cleavage 23,24 .…”

Section: Introductionmentioning

confidence: 99%

A multiplexed, automated evolution pipeline enables scalable discovery and characterization of biosensors

Townshend

Xiang

Manzanarez

et al. 2020

Preprint

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Biosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Using aptamer-coupled ribozyme libraries and a novel ribozyme regeneration method, we developed de novo rapid in vitro evolution of RNA biosensors (DRIVER) that enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identified and validated biosensors against six small molecules, including five for which no aptamers were previously found. DRIVERevolved biosensors were applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors were also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

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“…There is therefore an interest in implementing directed evolution approaches to create variants of the ADO enzyme with improved properties such that improved alkane titers can be achieved. In order to identify appropriate variants among a large library of enzymes, an alkane-responsive biosensor is necessary 16 .…”

Section: Introductionmentioning

confidence: 99%

Does co-expression of Yarrowia lipolytica genes encoding Yas1p, Yas2p and Yas3p make a potential alkane-responsive biosensor in Saccharomyces cerevisiae?

Dabirian

Skrekas

David

et al. 2020

Preprint

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Alkane-based biofuels are desirable to produce at a commercial scale as these have properties similar to our current petroleum-derived transportation fuels. Rationally engineering microorganisms to produce a desirable compound, such as alkanes, is, however, challenging. Metabolic engineers are therefore increasingly implementing evolutionary engineering approaches combined with high-throughput screening tools, including metabolite biosensors, to identify productive targets. Engineering Saccharomyces cerevisiae to produce alkanes can be facilitated by using an alkane-responsive biosensor, which can potentially be developed from the native alkane-sensing system in Yarrowia lipolytica, a well-known alkane-assimilating yeast. This putative alkane-sensing system is, at least, based on three different transcription factors (TFs) named Yas1p, Yas2p and Yas3p. Although this system is not fully elucidated in Y. lipolytica, we were interested in evaluating the possibility of translating this system into an alkane-responsive biosensor in S. cerevisiae. We evaluated the alkane-sensing system in S. cerevisiae by developing one sensor based on the native Y. lipolytica ALK1 promoter and one sensor based on the native S. cerevisiae CYC1 promoter. In both systems, we found that the TFs Yas1p, Yas2p and Yas3p do not seem to act in the same way as these have been reported to do in their native host. Additional analysis of the TFs suggests that more knowledge regarding their mechanism is needed before a potential alkane-responsive sensor based on the Y. lipolytica system can be established in S. cerevisiae.

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Design and optimization of genetically encoded biosensors for high-throughput screening of chemicals

Cited by 73 publications

References 49 publications

Toehold Switch-Based Genetic Modulator for Tuning of Riboswitch Sensor

Toehold Switch-Based Genetic Modulator for Tuning of Riboswitch Sensor

A multiplexed, automated evolution pipeline enables scalable discovery and characterization of biosensors

Does co-expression of Yarrowia lipolytica genes encoding Yas1p, Yas2p and Yas3p make a potential alkane-responsive biosensor in Saccharomyces cerevisiae?

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