A Genetically Encoded Biosensor for Monitoring Isoprene Production in Engineered <i>Escherichia coli</i>

Kim, Seong Keun; Kim, Seo Hyun; Subhadra, Bindu; Woo, Seung‐Gyun; Rha, Eugene; Kim, Seon-Won; Kim, Haseong; Lee, Dae-Hee

doi:10.1021/acssynbio.8b00164

Cited by 54 publications

(39 citation statements)

References 59 publications

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“…MvaK1 is responsible for the first step of the lower MVA pathway by converting MVA to mevalonate phosphate (MVA 5-P in Figure 1) [10] and is important for the regulation of the entire MVA pathway because it is inhibited by known feedback inhibitors: C5 (IPP and DMAPP), C15 (geranyl pyrophosphate (GPP) and FPP), and longer chain terpenoids [13,14]. FPP is a feedback inhibitor of the widely used Staphylococcus aureus MvaK1 (SaMvaK1) for creating a heterologous MVA pathway [15]. Previously, we have also used SaMvaK1 to produce (−)-α-bisabolol in engineered E. coli [16].…”

Section: Feedback-resistant Mvak1mentioning

confidence: 99%

Enhanced (−)-α-Bisabolol Productivity by Efficient Conversion of Mevalonate in Escherichia coli

Kim

Seong

et al. 2019

Catalysts

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(−)-α-Bisabolol, a naturally occurring sesquiterpene alcohol, has been used in pharmaceuticals and cosmetics owing to its beneficial effects on inflammation and skin healing. Previously, we reported the high production of (−)-α-bisabolol by fed-batch fermentation using engineered Escherichia coli (E. coli) expressing the exogenous mevalonate (MVA) pathway genes. The productivity of (−)-α-bisabolol must be improved before industrial application. Here, we report enhancement of initial (−)-α-bisabolol productivity to 3-fold higher than that observed in our previous study. We first harnessed a farnesyl pyrophosphate (FPP)-resistant mevalonate kinase 1 (MvaK1) from an archaeon Methanosarcina mazei (M. mazei) to create a more efficient heterologous MVA pathway that produces (−)-α-bisabolol in the engineered E. coli. The resulting strain produced 1.7-fold higher (−)-α-bisabolol relative to the strain expressing a feedback-inhibitory MvaK1 from Staphylococcus aureus (S. aureus). Next, to efficiently convert accumulated MVA to (−)-α-bisabolol, we additionally overexpressed genes involved in the lower MVA mevalonate pathway in E. coli containing the entire MVA pathway genes. (−)-α-Bisabolol production increased by 1.8-fold with reduction of MVA accumulation, relative to the control strain. Finally, we optimized the fermentation conditions including inducer concentration, aeration and enzymatic cofactor. The strain was able to produce 8.5 g/L of (−)-α-bisabolol with an initial productivity of 0.12 g/L h in the optimal fed-batch fermentation. Thus, the microbial production of (−)-α-bisabolol would be an economically viable bioprocess for its industrial application.

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Section: Feedback-resistant Mvak1mentioning

confidence: 99%

Enhanced (−)-α-Bisabolol Productivity by Efficient Conversion of Mevalonate in Escherichia coli

Kim

Seong

et al. 2019

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“…Although recent advances in synthetic biology have shed light on the importance of fine-tuning of biosensor dynamic range in various fields, the ability to design biosensors with moderate dynamic ranges remains limited 9, 18–20 . To investigate the key factors in biosensor dynamic range regulation, we used glucarate biosensor and explored its response strength by employing diverse concentrations of glucarate for induction ( Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

Ding

Zhou

Yuan

et al. 2020

Preprint

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23Currently, predictive translation tuning of regulatory elements to the desired output of 24 transcription factor based biosensors remains a challenge. The gene expression of a biosensor 25 system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding 26 site (RBS), to achieve fine-tuning of its dynamic range (i.e., fold change in gene expression between 27 the presence and absence of inducer) by adjusting the translation initiation rate of the transcription 28 factor and reporter. However, existing genetically encoded biosensors generally suffer from 29 unpredictable translation tuning of regulatory elements to dynamic range. Here, we elucidated the 30 connections and partial mechanisms between RBS, translation initiation rate, protein folding and 31 dynamic range, and presented a rational design platform that predictably tuned the dynamic range 32 of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). A library containing 33 24,000 semi-rationally designed cRBSs was constructed using DNA microarray, and was divided 34into five sub-libraries through fluorescence-activated cell sorting. To explore the relationship 35 between cRBSs and dynamic range, we established a classification model with the cRBSs and 36 average dynamic range of five sub-libraries to accurately predict the dynamic range of biosensors 37 based on convolutional neural network in deep learning. Thus, this work provides a powerful 38 platform to enable predictable translation tuning of RBS to the dynamic range of biosensors. 39 40

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“…Canonical aTFs like LacI [15], AraC [16] and TetR [17] have been applied as inducible gene expression for decades. More recently, aTFs have been employed as biosensors for the detection of other molecules including industrially valuable aliphatic [12,18,19] and aromatic [20][21][22] chemicals. Although aTF biosensors have been broadly used, the repertoire of effectors with a known aTF is limited [7].…”

Section: Introductionmentioning

confidence: 99%

Directed evolution of the PcaV allosteric transcription factor to generate a biosensor for aromatic aldehydes

Machado

Currin

Dixon

2019

Preprint

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Background:Genetically encoded biosensors are useful tools for the detection of metabolites and industrially valuable molecules, and present many potential applications in biotechnology and biomedicine. However, the most common approach to develop biosensors relies on employing a limited set of naturally occurring allosteric transcript factors (aTFs). Therefore, altering the substrate specificity of aTFs towards the detection of new effectors is an important goal. Results:Here, the PcaV repressor, a member of the MarR aTF family, was used to develop a biosensor for the detection of hydroxyl-substituted benzoic acids, including protocatechuic acid (PCA). The PCA biosensor was further subjected to directed evolution to alter its substrate specificity towards vanillin and other closely related aromatic aldehydes, to generate the Van2 biosensor. Substrate recognition of Van2 was explored in vitro using a range of biochemical and biophysical analyses, and extensive in vivo genetic-phenotypic analysis was performed to determine the role of each amino acid change upon biosensor performance. Conclusions:This is the first study to report directed evolution of a member of the MarR aTF family, and demonstrates the plasticity of the PCA biosensor by altering its substrate specificity to generate a biosensor for aromatic aldehydes.

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A Genetically Encoded Biosensor for Monitoring Isoprene Production in Engineered Escherichia coli

Cited by 54 publications

References 59 publications

Enhanced (−)-α-Bisabolol Productivity by Efficient Conversion of Mevalonate in Escherichia coli

Enhanced (−)-α-Bisabolol Productivity by Efficient Conversion of Mevalonate in Escherichia coli

Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

Directed evolution of the PcaV allosteric transcription factor to generate a biosensor for aromatic aldehydes

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