Artificial Caprolactam-Specific Riboswitch as an Intracellular

May 10, 2019 - Caprolactam is a monomer used for the synthesis of nylon-6, and a recombinant microbial strain for biobased production of nylon-6 was ...
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Artificial caprolactam-specific riboswitch as an intracellular metabolite sensor Sungyeon Jang, Sungho Jang, Dae-Kyun Im, Taek Jin Kang, Min-Kyu Oh, and Gyoo Yeol Jung ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.8b00452 • Publication Date (Web): 10 May 2019 Downloaded from http://pubs.acs.org on May 11, 2019

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Artificial caprolactam-specific riboswitch as an intracellular metabolite

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sensor

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Sungyeon Janga,1, Sungho Janga,1, Dae-Kyun Imb, Taek Jin Kangc, Min-Kyu Ohb and Gyoo Yeol

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Junga,d,*

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aDepartment

of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Korea

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bDepartment

Seongbuk-Gu, Seoul 02841, Korea

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cDepartment

of Chemical and Biochemical Engineering, Dongguk University-Seoul, 30 PildongRo 1-Gil, Jung-Gu, Seoul 04620, Korea

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of Chemical and Biological Engineering, Korea University, 145 Anam-Ro,

dSchool

of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Korea

15 16 1These

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*Corresponding

authors contributed equally to this work.

author at: Department of Chemical Engineering, Pohang University of Science

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and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Korea.

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E-mail address: [email protected] (G.Y. Jung)

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ABSTRACT

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Caprolactam is a monomer used for the synthesis of nylon-6, and a recombinant microbial strain

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for bio-based production of nylon-6 was recently developed. An intracellular biosensor for

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caprolactam can facilitate high-throughput metabolic engineering of recombinant microbial

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strains. Because of the mixed production of caprolactam and valerolactam in the recombinant

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strain, a caprolactam biosensor should be highly specific for caprolactam. However, a highly

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specific caprolactam sensor has not been reported. Here, we developed an artificial riboswitch

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that specifically responds to caprolactam. This riboswitch was prepared using a coupled in vitro-

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in vivo selection strategy with a heterogeneous pool of RNA aptamers obtained from in vitro

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selection to construct a riboswitch library used in in vivo selection. The caprolactam riboswitch

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successfully discriminated caprolactam from valerolactam. Moreover, the riboswitch was

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activated by 3.36-fold in the presence of 50 mM caprolactam. This riboswitch enabled

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caprolactam-dependent control of cell growth, which will be useful for improving caprolactam

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production and is a valuable tool for metabolic engineering.

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KEYWORDS: Caprolactam; Riboswitch; SELEX; Metabolite biosensor; High-throughput

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screening

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The bio-based manufacture of chemicals from renewable substrates using microorganisms is a

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promising approach that can substitute for chemical synthesis using petroleum-derived raw

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materials.1 One important area in bio-based manufacture is the production of monomers.2,3

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Caprolactam (also known as ε-caprolactam) is a cyclic amide of caproic acid and is widely used

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to produce nylon-6 through ring-opening polymerization.4 Because of the high economic value

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of caprolactam, there have been attempts to prepare microbes that produce caprolactam or its

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precursor.5,6 However, the production strains require substantial improvement in the yield and

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titer of caprolactam or its precursor. Metabolic engineering of microbial strains often requires

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metabolic flux optimization and enzyme engineering, which can be achieved using a high-

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throughput screening system for a target chemical.7,8

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Genetically encoded biosensors regulate gene expression according to the metabolite

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concentration in each cell. Biosensors offer unprecedented throughput for metabolite screening

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when combined with high-throughput methods such as fluorescence-activated cell sorting or

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artificial selection.9–12 High specificity is one of the most important characteristics of a biosensor

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because the cytoplasm contains diverse molecules and some metabolic pathways produce

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byproducts with structures analogous to a target chemical. For example, a caprolactam-

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producing recombinant Escherichia coli strain co-produced valerolactam which differs by only a

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single carbon (i.e. caprolactam: C6, valerolactam: C5) because of the promiscuity of the pathway

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enzymes.6

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Recently, a transcription factor (TF) -based lactam biosensor was developed.13 Although

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this sensor responded to caprolactam, the sensor could not discriminate between caprolactam and

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valerolactam. However, it is not straightforward to engineer the specificity of a TF to make it

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respond to another target molecule. First, detailed information regarding the structure of the

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ligand-binding pocket of the TF is necessary.14,15 Second, even if the ligand-binding pocket is

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identified, engineering of the TF often requires high-throughput screening, which is limited by

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transformation efficiency.15,16 Compared to protein-based sensors (e.g. TF), RNA-based sensors

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such as riboswitches have several advantages.17 An RNA aptamer that specifically binds to a

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target chemical can be developed from large libraries because this process is performed in vitro

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and does not involve transformation.18,19 Furthermore, an RNA-based sensor can reduce the

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unnecessary metabolic burden to a cell, as it directly interacts with the target molecule and does

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not rely on the expression of protein effectors.20

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It remains challenging to develop an RNA-based sensor for a small molecule which has

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robust intracellular activity using a specified tight-binding aptamer through in vitro selection.12,21

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To overcome these limitations, we recently reported a coupled in vitro–in vivo selection process

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employing a heterogeneous aptamer population from in vitro selection to construct a riboswitch

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library for in vivo selection.22 Utilization of the aptamer pool with diverse sequences increased

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the potential for discovering the riboswitch with in vivo functionality. Using this approach,

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naringenin-responsive riboswitches that function in E. coli cells were successfully discovered.

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Here, we developed a caprolactam-specific artificial riboswitch for high-throughput

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screening and selection in microbial cells (Fig. 1). The coupled in vitro–in vivo selection strategy

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was utilized to develop the riboswitch. We validated caprolactam-dependent gene expression

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activation of the selected riboswitch. The specificity of the riboswitch to caprolactam was

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confirmed against other lactams and a caprolactam precursor, 6-aminocaproic acid. Additionally,

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the fold-activation of the riboswitch was measured and compared with that of the TF-based

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sensor which was reported in a previous study.13 Finally, caprolactam-dependent control of cell

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growth was demonstrated using the riboswitch.

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RESULTS

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Development of artificial caprolactam riboswitch through coupled in vitro-in vivo selection

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The development of a caprolactam-responsive artificial riboswitch requires an RNA aptamer that

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specifically binds to caprolactam. However, the tradeoff between binding affinity and the ability

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to rearrange the structure of an RNA aptamer is a well-known problem in developing a

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riboswitch.21 Therefore, we employed a coupled in vitro-in vivo selection scheme22 to

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successfully produce artificial riboswitches for small molecules (Fig. 1).

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Following this scheme, an RNA aptamer library was enriched in vitro for binding to

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caprolactam. The initial RNA pool prepared by in vitro transcription of a double-stranded DNA

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template with a randomized sequence region was incubated with a solid matrix with caprolactam

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attached to the end of the linker. The RNA library contained a 60-nt-long randomized sequence

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region among which could serve as a caprolactam aptamer and the randomized region was

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flanked by two primer binding sites which were used to amplify the enriched RNA pool through

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reverse transcription and PCR (Fig. 2a). After washing, the bound RNA molecules were eluted

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and used to prepare the RNA pool for the next round by reverse transcription, PCR, and in vitro

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transcription. This selection cycle was repeated 10 times. At the 4th selection cycle, negative

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selection was carried out to eliminate possible false-positive RNAs that did not bind to

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caprolactam but bound to the solid matrix or its linker. The amount of RNA eluted in each step

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was measured to monitor the progress of in vitro selection (Fig. 2b). The in vitro selection was

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stopped after the 10th selection round and the eluted RNA fraction was 3.34%. The enriched

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RNA pool was amplified by reverse transcription and PCR and used directly to prepare a

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riboswitch library.

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The riboswitch library was transformed to E. coli and selected in vivo to identify a

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functional riboswitch for controlling gene expression responding to caprolactam. Using tetA as a

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dual-selectable marker gene,23,24 caprolactam-dependent gene expression activation was selected.

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Three negative selection steps (with 10 μM NiCl2) with increasing concentration of caprolactam

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in the culture medium (0, 10, and 100 mg/L caprolactam) were followed by positive selection

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(with 50 μg/mL tetracycline) in the presence of a high concentration (1000 mg/L) of caprolactam.

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This whole sequence was repeated twice. After selection, single colonies were isolated and 10

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colonies were tested for their ability to control gene expression in response to caprolactam. The

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gene expression level was quantified with or without caprolactam in the culture medium by

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measuring the fluorescence intensity from the superfolder green fluorescent protein. Colony #7

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activated gene expression by 2.21-fold when 10 mM caprolactam was added to the culture

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medium (Fig. 2c). The DNA sequence of the caprolactam riboswitch from colony #7 was

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GGGAATTCGAGCTCCTGACAgcacaatttgcctacagcgttaagacacgaaaaagcacaattagacacttacatgtgtgg

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ATTCGAAGACGTCCAGCTGAactagccggaaaggagcatctatgaaatctaacaatgcgctcatcgtcatc.

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Capital letters indicate the primer binding sites, underlined letters indicate the randomized linker

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region, and italicized letters indicate the 5′-untranslated region followed by the first 30 nts of tetA.

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Characterization of caprolactam riboswitch

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We further characterized the important properties of the selected riboswitch for use in metabolic

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engineering. First, we evaluated the dose-response curve of the riboswitch. The fluorescence

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from the cell was measured with varying concentrations of caprolactam (0 – 50 mM) added to

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the culture medium. In the meantime, we measured the intracellular caprolactam concentration to

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validate that the riboswitch actually responds to the intracellular caprolactam (see Methods

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section). Indeed, the riboswitch activated the gene expression in response to the increasing

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concentration of caprolactam (Fig. 3a, b). The operational range of the riboswitch was 3 – 50

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mM of the extracellular caprolactam concentration (Fig. 3a) and 28.3 – 348.5 mM of the

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intracellular caprolactam (Fig. 3b). Notably, the riboswitch showed 3.36-fold-activation with 50

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mM caprolactam, while the TF-based lactam sensor from the previous report showed 2.08-fold.13

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Thus, the caprolactam riboswitch had a higher fold-activation compared to the TF-based lactam

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sensor. Additionally, this riboswitch will be useful for future screening efforts because the best

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caprolactam producer at the moment produced only 79.60 μg/L (corresponds to 0.7 μM)

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caprolactam6 and the riboswitch is responsive up to 50 mM.

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Next, we investigated the specificity of the caprolactam riboswitch. A metabolite biosensor

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should be highly specific for a target molecule.9 The engineered E. coli strain utilized 6-

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aminocaproic acid as a precursor and co-produced caprolactam and valerolactam.6 Therefore, we

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tested the specificity of the caprolactam riboswitch against the precursor and two lactams with

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different carbon numbers (butyrolactam and valerolactam). First, 6-aminocaproic acid was not

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able to activate the riboswitch and even reduced the reporter gene expression slightly, showing

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0.59-fold, when the chemical was added at 50 mM in the culture medium (data not shown). Next,

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we tested the specificity of the riboswitch against the lactams. We supplemented the lactams at

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50 mM in the culture medium and measured the fluorescence. Further, we measured the

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intracellular concentration of the lactams because the difference of uptake efficiency of the

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chemicals could affect the response from the riboswitch. The riboswitch activated gene

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expression much higher when caprolactam was added as an inducer compared to the other

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chemicals although the intracellular concentrations of the chemicals were comparable (Fig. 3c).

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Therefore, the specificity of the riboswitch to caprolactam against the precursors and similar

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chemicals was confirmed.

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We further tested the specificity by mimicking the producer strain. Since the engineered strain

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co-produced caprolactam and valerolactam, we measured the fluorescence from the cell cultured

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with varying ratios of caprolactam and valerolactam while maintaining their total concentration

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constant. The fluorescence increased linearly with the increasing percentage of caprolactam (Fig.

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3d). Therefore, the riboswitch will be useful for screening caprolactam producers even with the

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promiscuous co-production of valerolactam.

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Caprolactam-dependent control of cell growth using the caprolactam riboswitch

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Finally, we demonstrated the ability of using the riboswitch as an artificial selection device.

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Because of its simple experimental setup and high throughput, growth-based selection is

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regarded as one of the best choices for the screening of metabolite production if a suitable

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selection system is provided.7,24,25 Because the riboswitch regulates the expression of a selection

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marker, tetA, in response to caprolactam, we assumed that the riboswitch could be utilized as a

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selection device. When tetracycline was not added to the culture medium, the growth rate of E.

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coli transformed with the riboswitch decreased slightly with increasing caprolactam

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concentrations because of the toxicity of caprolactam (Fig. 4). In the presence of tetracycline,

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however, the growth rate at a high caprolactam concentration was higher than that with a low

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concentration. Interestingly, the selection cutoff concentration was modulated by changing the

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tetracycline concentration. With 80 μg/mL tetracycline, only E. coli cells without caprolactam

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were unable to grow. When more tetracycline was added to the medium (150 or 300 μg/mL), the

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minimal caprolactam concentration required for growth increased accordingly. Therefore, the

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selection cutoff level can be controlled simply by adjusting the tetracycline concentration

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depending on the productivity of the parental strain used to prepare the producer mutant library.

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DISCUSSION

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In this study, we developed an artificial caprolactam riboswitch for quantitatively detecting the

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intracellular concentration of caprolactam. Key parameters such as specificity, fold-activation,

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and linear range of detection were evaluated to assess whether the riboswitch was an advanced

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selection device compared to the TF-based sensor. Finally, we utilized the riboswitch for

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artificial selection. A caprolactam riboswitch was successfully developed using a coupled in

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vitro-in vivo selection strategy. The riboswitch specifically responded to caprolactam and

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effectively discriminated it from butyrolactam and valerolactam. The fold-activation of the

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riboswitch was 3.36-fold and the riboswitch was able to detect caprolactam up to 50 mM. Finally,

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the riboswitch enabled the control of cell growth depending on the caprolactam concentration,

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indicating the potential application of the riboswitch in artificial selection.

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One of the significant improvements of the riboswitch as a caprolactam sensor compared

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to the TF-based sensor was its specificity for caprolactam. Valerolactam and caprolactam are co-

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produced because of the promiscuity of the pathway enzymes,6 necessitating a metabolite sensor

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that specifically responds to caprolactam. The TF-based sensor could not distinguish between

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caprolactam and valerolactam,13 while the two lactams were easily discernable using the

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riboswitch. The difference in specificity may be related to dissimilarity between sensor

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development strategies. The TF-based lactam sensor was discovered from nature by searching

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for a TF that responds to a molecule with a structure analogous to valerolactam. Therefore, the

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TF was more likely to respond to valerolactam rather than to caprolactam. Indeed, the fold-

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activation of the TF-based lactam sensor was higher for valerolactam than for caprolactam. In

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contrast, our strategy aimed to directly evolve an RNA aptamer that binds to caprolactam. As a

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result, the selected riboswitch responded more specifically to caprolactam than to valerolactam.

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We predicted that the development of a specific metabolite sensor could be streamlined by

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employing the coupled in vitro-in vivo selection strategy for the riboswitch, considering that

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engineering of specific TF-based sensors often requires detailed information about the protein

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structure and substantial efforts to design and select mutant libraries.

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In addition to its specificity, the caprolactam riboswitch showed promising properties as

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an intracellular metabolite sensor. The riboswitch activated gene expression by 3.36-fold at 50

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mM caprolactam while the TF-based sensor from the previous report activated expression by

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2.08-fold at the same concentration. Additionally, caprolactam-dependent control of cell growth

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was demonstrated using the riboswitch. The selection cutoff caprolactam concentration was

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easily modulated by adjusting the tetracycline concentration.

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Although the riboswitch developed in this study will be a useful tool, the best titer from

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an engineered strain to date (0.7 μM) is much lower than the detection limit of the riboswitch and

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small improvement from this strain would not be easy to be detected. If necessary, the sensitivity

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of the riboswitch could be improved in several ways. For example, the number of an aptamer can

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be modulated to adjust the sensitivity.26 Otherwise, a synthetic circuit for signal amplification

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could be utilized to improve the sensitivity of a riboswitch.27

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In summary, we developed a specific caprolactam riboswitch useful for monitoring the

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intracellular concentration of caprolactam. The riboswitch developed through coupled in vitro-in

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vivo selection responded specifically to caprolactam. The riboswitch exhibited good sensor

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parameters such as a high fold-activation (3.36-fold) and an operational range of up to 50 mM.

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Furthermore, the capability of the riboswitch as a caprolactam selection device was demonstrated.

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This riboswitch is a valuable tool for directed evolution of enzymes and pathway optimization

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for the microbial production of caprolactam.

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METHODS

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Bacterial strains, plasmids, and reagents

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Escherichia coli strains and plasmids used in this study are listed in Table S1. Oligonucleotides

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were synthesized by Cosmogenetech (Seoul, Korea) and are listed in Table S2. All chemicals

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used in this study were purchased from Sigma-Aldrich (St. Louis, MO, USA) unless otherwise

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stated.

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In vitro selection of RNA aptamers for caprolactam

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The RNA aptamer for caprolactam was selected in vitro through the systematic evolution of

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ligands by exponential enrichment (SELEX).28,29 The caprolactam-coupled solid matrix was

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prepared based on ECH Sepharose 4B (GE Healthcare Life Science, Little Chalfont, UK).

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Because this matrix can be coupled to a molecule with a primary amine group, we used L-(−)-α-

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amino-ε-caprolactam, which is differed from ε-caprolactam only by an additional primary amine

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group. The coupling reaction was carried out following the manufacturer’s instruction. The pre-

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swollen matrix was washed with distilled water (pH 4.5), followed by washing with 0.5 M NaCl.

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This matrix was mixed with an equal volume of ligand solution (0.1 M in distilled water, pH 4.5).

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Next, N-ethyl-N′-(3-dimethylaminopropyl) carbodiimide hydrochloride was added at a final

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concentration of 0.1 M. The reaction mixture was incubated overnight at room temperature.

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After the coupling reaction, the matrix was subjected to alternating washes using 0.1 M acetate

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buffer (pH 4.0, with 0.5 M NaCl) and 0.1 M Tris-HCl buffer (pH 8.0, with 0.5 M NaCl) three

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times. The coupled matrix was stored in 20% ethanol at 4°C until use.

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Preparation of the RNA pool and selection of aptamers were performed following the

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protocol from a previous SELEX experiment.22 A DNA template was prepared by PCR

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amplification of the SELEX_Template with SELEX_F and SELEX_R using Prime Taq DNA

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polymerase (GeNet Bio, Daejeon, Korea). The DNA template was transcribed by T7 RNA

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polymerase (New England Biolabs, Ipswich, MA, USA) and treated with DNaseI (Takara Bio,

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Shiga, Japan). The transcribed RNA pool was purified by polyacrylamide gel electrophoresis.

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One milliliter of RNA solution (3 µM) in selection buffer (50 mM Tris-HCl, 100 mM KCl, pH

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7.4) was heated at 95°C for 10 min and cooled at room temperature for 20 min. Next, MgCl2 was

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added at a final concentration of 10 mM and the solution was further incubated at room

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temperature for 15 min. The RNA solution was mixed with 1 mL of the caprolactam-coupled

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matrix and incubated overnight at room temperature with gentle rotation. Negative selection was

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performed at round 4 using a non-coupled matrix to eliminate RNA molecules that bound to the

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matrix.

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The RNA-matrix slurry was washed with 10 mL of washing buffer (50 mM Tris-HCl,

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100 mM KCl, 10 mM MgCl2, pH 7.4), and then 2 mL of elution buffer (25 mM Tris-HCl, 300

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mM NaCl, 5 mM EDTA, 4 M urea, pH 7.4) was heated to 95°C and added to the matrix. After

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vigorous mixing by pipetting, the slurry was filtered, and the elution buffer was precipitated

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using ethanol. RNA was quantified using a UV-1700 spectrophotometer (Shimadzu, Kyoto,

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Japan) at 260 nm.

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The DNA pool for the next round of selection was prepared by reverse transcription-

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polymerase chain reaction (RT-PCR). cDNA of the eluted RNA was synthesized by

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SuperScriptIII (Invitrogen, Carlsbad, CA, USA) using SELEX_R as a primer. The cDNA was

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amplified by Q5 DNA polymerase (New England Biolabs) using SELEX_RT_T7 and SELEX_R

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as primers. The amplified DNA pool was used as a template for in vitro transcription. After

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round 4, the washed RNA was used to prepare the solution for round 5.

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In vivo selection of caprolactam riboswitch

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The riboswitch library plasmid was cloned using BsaI sites in the backbone plasmid (pRibo_NC).

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The eluted RNA pool after SELEX round 10 was reverse-transcribed using the CapApt_N10_R

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primer. The cDNA was PCR amplified using CapApt_F and CapApt_N10_R as primers. The

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amplified DNA was digested by BsaI-HF (New England Biolabs) and ligated with the digested

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backbone plasmid using T4 DNA ligase (Takara Bio). The ligation mixture was precipitated by

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ethanol and transformed into MegaX DH10B T1R electrocompetent cells (Invitrogen). The

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cloned plasmid was extracted and transformed into E. coli W3110.

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The E. coli library transformed with the riboswitch library was inoculated into 3 mL of

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M9 medium containing 34 μg/mL chloramphenicol (CM9) at an OD600 of 0.05. This medium

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also contained an appropriate concentration of caprolactam for cell adaptation. The cells were

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cultured for 8 h at 37°C with shaking (220 rpm) and all other culture in this study was conducted

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under the same conditions. The culture broth was diluted to 3 mL with fresh CM9 containing

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caprolactam at the same concentration and appropriate selection pressure at an OD600 of 0.01.

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This selection broth was cultured until the OD600 reached 0.5. Selection was performed as

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follows: negative (0 mg/L caprolactam), negative (10 mg/L caprolactam), negative (100 mg/L

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caprolactam), and positive (1000 mg/L caprolactam). Either 10 μM NiCl2 or 50 μg/mL

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tetracycline was supplemented to the medium for negative or positive selection, respectively.

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The whole selection sequence was repeated twice. Ten colonies were isolated after the whole

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selection process on an LB agar plate containing chloramphenicol. The colonies were cultured in

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CM9 medium with or without 10 mM caprolactam. The OD600 and fluorescence of the culture

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broth were measured using a VICTOR3 1420 Multilabel Counter (PerkinElmer, Waltham, MA,

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USA). Fluorescence was detected using a 485-nm excitation filter and 535-nm emission filter

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with a 1-s measurement time. Specific fluorescence (S.F.) and fold-activation (F.A.) were

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calculated with the following equations: S.F. = (Fl.culture - Fl.background)/(OD600, culture - OD600,

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background),

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and OD600 measurements. All cultures were performed in biological triplicate.

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Characterization of caprolactam riboswitch

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The E. coli W3110 cells with the caprolactam riboswitch were cultured overnight in 3 mL of

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CM9. The culture broth was diluted at a final OD600 of 0.05 into 3 mL of CM9 and cultivated for

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8 h. Then, to evaluate the dose-response curve, the culture broth was inoculated into 6 mL of

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CM9 medium containing various concentrations of caprolactam (0, 1, 3, 10, 20, 30, 50 mM) at a

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final OD600 of 0.05. To investigate the specificity, 6-aminocaproic acid (50 mM), butyrolactam

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(50 mM), valerolactam (50 mM), or caprolactam (50 mM) was added to the culture medium. For

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the caprolactam-valerolactam mixture experiment, two lactams of different ratios were added to

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the culture medium while maintaining their total concentration at 70 mM. The culture broth was

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incubated for 12 h and the fluorescence and OD600 were measured using the VICTOR3 to

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calculate the specific fluorescence and fold-activation. Remained cells were used for the

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metabolite extraction. All cultures were performed in biological triplicate.

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Intracellular metabolite extraction

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4×109 Cells were collected by filtration using a mixed cellulose ester membrane disk filter of

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pore size 0.22 μm (Millipore, Bedford, MA). Then, the pre-chilled isotonic solution was filtrated

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to wash media components. Next, the filter was immersed in 5 mL of chloroform/methanol/water

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(32:60:8, v/v/v) solution and vortexed. The solution was centrifuged at 8000 rpm for 10 min at

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0 °C and 0.4 mL of the aqueous phase was transferred to 1.5 mL tube. Then the solution was

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fully dried using vacuum dryer (Hanil Science Industrial Co., Incheon, Korea) and kept on -

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80 °C until analyzed.

F.A. = S.F.+cap/S.F.-cap. The CM9 medium was used as a background for fluorescence

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Intracellular metabolite sample preparation and derivatization

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Caprolactam, butyrolactam, and valerolactam samples were dissolved in 200 μL of distilled

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water containing hexanol (Sigma Aldrich) as an internal standard. The samples were then

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centrifuged at 14,000 rpm for 10 min to pellet any undissolved residue, and the supernatant was

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transferred to a new glass insert for GC-MS injection.

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GC-MS analysis of intracellular metabolites

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Sample analysis was performed on a GC-MS system, GC 7890 coupled to an MSD 5977

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(Agilent Technologies, Inc., Santa Clara, CA) equipped with an HP-5MS capillary column (30 m

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×0.25 mm i.d.×0.25 μm; Agilent J&W Scientific). The injection volume was 1 μL and all

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samples were run in 1:10 split mode with an inlet temperature of 270 °C. Helium flow rate was

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set to 1 mL/min. The MS source temperature was maintained at 230 °C, and the MS quad

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temperature was held constant at 150 °C with electron energy of 70 eV. The oven temperature

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profile was 40 °C for 5 min; 3 °C/min to 160 °C; 20 °C/min to 300 °C; and held at 300 °C for 15

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min. The selected ions monitored were 113 m/z for caprolactam, 85 m/z for butyrolactam and 99

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m/z for valerolactam.

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Tetracycline resistance assay

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The E. coli W3110 cells with the riboswitch were cultured overnight in CM9. The culture broth

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was diluted to 3 mL of CM9 containing various concentrations of caprolactam (0, 10, 20, and 50

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mM) and cultured for 6 h at a final OD600 of 0.05. The culture broth was diluted with 3 mL of

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CM9 containing different combinations of caprolactam concentrations (0, 10, 20, and 50 mM)

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and tetracycline concentrations (0, 80, 150, and 300 µg/mL) at a final OD600 of 0.05. Growth

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rates were evaluated by measuring the OD600 of the cultures. All cultures were performed in

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biological triplicate.

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338 339

ASSOCIATED CONTENT

340

Supporting Information

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(Table S1) E. coli Strains and plasmids used in this study

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(Table S2) Oligonucleotides used in this study

343 344

AUTHOR INFORMATION

345

Corresponding Author

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*E-mail: (G.Y.J.) [email protected]

347

ORCID

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G. Y. Jung: 0000-0002-9742-3207

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Sungho Jang: 0000-0003-4368-3043

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Author contributions

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1S.J.

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designed the project. S.J., S.J. and D.-K. I. performed the experiments. S.J., S.J., D.-K. I., and

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G.Y.J. analyzed the data and wrote the manuscript. All authors read and approved the final

354

version of the manuscript.

355

Notes

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The authors declare no competing financial interest.

and S.J. contributed equally to this work. S.J., S.J, D.-K. I., T.J.K., M.-K. O. and G.Y.J.

357 358

ACKNOWLEDGMENTS

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This work was supported by the Global Research Laboratory Program [grant number NRF-

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2016K1A1A2912829] through the National Research Foundation of Korea (NRF) funded by the

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Korea Government (Ministry of Science and ICT) and by the Korea Institute of Energy

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Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy

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(MOTIE) of the Republic of Korea [grant number 20174030201600].

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FIGURE LEGENDS

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Figure 1. Overall development strategy for the caprolactam riboswitch.

367 368

Figure 2. Development of the caprolactam riboswitch. (a) Preparation of the RNA library for

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SELEX. PT7, T7 promoter; PBS, primer binding site; N60, randomized sequence region of 60 nt

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(b) SELEX profile. Negative selection was performed at round 4 using the intact matrix. (c)

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Fold-activation of ten colonies after in vivo selection. Specific fluorescence with 10 mM

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caprolactam was normalized to that with no caprolactam.

373 374

Figure 3. Caprolactam-specific gene activation by the caprolactam riboswitch. (a, b) Dose-

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response curves of the riboswitch for caprolactam using extracellular or intracellular caprolactam

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concentration. (c) Specificity of the riboswitch to caprolactam against other lactams. C4,

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butyrolactam; C5, valerolactam; C6, caprolactam (d) Specificity of the riboswitch to caprolactam

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when the mixture of caprolactam and valerolactam was added to the culture medium. All

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experiments were performed in biological triplicate and the error bars indicate the standard

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deviations.

381 382

Figure 4. Caprolactam-dependent control of cell growth using the caprolactam riboswitch. The

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numbers in squares are averaged growth rates from biological triplicate.

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REFERENCES

387

(1) Burk, M. J., and Van Dien, S. (2016) Biotechnology for chemical production: challenges and

388

opportunities. Trends Biotechnol. 34, 187–190.

389

(2) Chung, H., Yang, J. E., Ha, J. Y., Chae, T. U., Shin, J. H., Gustavsson, M., and Lee, S. Y.

390

(2015) Bio-based production of monomers and polymers by metabolically engineered

391

microorganisms. Curr. Opin. Biotechnol. 36, 73–84.

392

(3) Lee, J. H., Lama, S., Kim, J. R., and Park, S. H. (2018) Production of 1,3-propanediol from

393

glucose by recombinant Escherichia coli BL21(DE3). Biotechnol. Bioproc. Eng. 23, 250–258.

394

(4) Carraher, C. E. (1978) Synthesis of caprolactam and Nylon 6. J. Chem. Educ. 55, 51.

395

(5) Zhou, H., Vonk, B., Roubos, J. A., Bovenberg, R. A. L., and Voigt, C. A. (2015) Algorithmic

396

co-optimization of genetic constructs and growth conditions: application to 6-ACA, a potential

397

nylon-6 precursor. Nucleic Acids Res. 43, 10560–10570.

398

(6) Chae, T. U., Ko, Y.-S., Hwang, K.-S., and Lee, S. Y. (2017) Metabolic engineering of

399

Escherichia coli for the production of four-, five- and six-carbon lactams. Metab. Eng. 41, 82–91.

400

(7) Dietrich, J. A., McKee, A. E., and Keasling, J. D. (2010) High-throughput metabolic

401

engineering: advances in small-molecule screening and selection. Annu. Rev. Biochem. 79, 563–

402

590.

403

(8) Hou, C. Y., Ahn, C., Cho, B.-K., and Kang, T. J. (2018) Selection of Escherichia coli

404

glutamate decarboxylase active at neutral pH from a focused library. Biotechnol. Bioproc. Eng.

405

23, 473–479.

406

(9) Lim, H. G., Jang, S., Jang, S., Seo, S. W., and Jung, G. Y. (2018) Design and optimization of

407

genetically encoded biosensors for high-throughput screening of chemicals. Curr. Opin.

408

Biotechnol. 54, 18–25.

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409

(10) Selvamani, V., Ganesh, I., Maruthamuthu, M. kannan, Eom, G. T., and Hong, S. H. (2017)

410

Engineering chimeric two-component system into Escherichia coli from Paracoccus denitrificans

411

to sense methanol. Biotechnol. Bioproc. Eng. 22, 225–230.

412

(11) Cress, B. F., Trantas, E. A., Ververidis, F., Linhardt, R. J., and Koffas, M. A. (2015)

413

Sensitive cells: enabling tools for static and dynamic control of microbial metabolic pathways.

414

Curr. Opin. Biotechnol. 36, 205–214.

415

(12) Xiu, Y., Jang, S., Jones, J. A., Zill, N. A., Linhardt, R. J., Yuan, Q., Jung, G. Y., and Koffas,

416

M. A. G. (2017) Naringenin-responsive riboswitch-based fluorescent biosensor module for

417

Escherichia coli co-cultures. Biotechnol. Bioeng. 114, 2235–2244.

418

(13) Zhang, J., Barajas, J. F., Burdu, M., Ruegg, T. L., Dias, B., and Keasling, J. D. (2017)

419

Development of a transcription factor-based lactam biosensor. ACS Synth. Biol. 6, 439–445.

420

(14) Peet, D. J., Doyle, D. F., Corey, D. R., and Mangelsdorf, D. J. (1998) Engineering novel

421

specificities for ligand-activated transcription in the nuclear hormone receptor RXR. Chem. Biol.

422

5, 13–21.

423

(15) Taylor, N. D., Garruss, A. S., Moretti, R., Chan, S., Arbing, M. A., Cascio, D., Rogers, J. K.,

424

Isaacs, F. J., Kosuri, S., Baker, D., Fields, S., Church, G. M., and Raman, S. (2016) Engineering

425

an allosteric transcription factor to respond to new ligands. Nat. Methods 13, 177–183.

426

(16) Jha, R. K., Chakraborti, S., Kern, T. L., Fox, D. T., and Strauss, C. E. M. (2015) Rosetta

427

comparative modeling for library design: engineering alternative inducer specificity in a

428

transcription factor. Proteins 83, 1327–1340.

429

(17) Jang, S., Jang, S., Yang, J., Seo, S. W., and Jung, G. Y. (2018) RNA-based dynamic genetic

430

controllers: development strategies and applications. Curr. Opin. Biotechnol. 53, 1–11.

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Page 20 of 27

Page 21 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Synthetic Biology

431

(18) Ruscito, A., and DeRosa, M. C. (2016) Small-molecule binding aptamers: selection

432

strategies, characterization, and applications. Front. Chem. 4, 14.

433

(19) McKeague, M., Wong, R. S., and Smolke, C. D. (2016) Opportunities in the design and

434

application of RNA for gene expression control. Nucleic Acids Res. 44, 2987–2999.

435

(20) Lotz, T. S., and Suess, B. (2018) Small-molecule-binding riboswitches. Microbiol. spectr. 6.

436

(21) Porter, E. B., Polaski, J. T., Morck, M. M., and Batey, R. T. (2017) Recurrent RNA motifs

437

as scaffolds for genetically encodable small-molecule biosensors. Nat. Chem. Biol. 13, 295–301.

438

(22) Jang, S., Jang, S., Xiu, Y., Kang, T. J., Lee, S.-H., Koffas, M. A. G., and Jung, G. Y. (2017)

439

Development of artificial riboswitches for monitoring of naringenin in vivo. ACS Synth. Biol. 6,

440

2077–2085.

441

(23) Muranaka, N., Sharma, V., Nomura, Y., and Yokobayashi, Y. (2009) An efficient platform

442

for genetic selection and screening of gene switches in Escherichia coli. Nucleic Acids Res. 37,

443

e39.

444

(24) Jang, S., Yang, J., Seo, S. W., and Jung, G. Y. (2015) Riboselector: riboswitch-based

445

synthetic selection device to expedite evolution of metabolite-producing microorganisms.

446

Methods Enzymol. 550, 341–362.

447

(25) Yang, J., Seo, S. W., Jang, S., Shin, S.-I., Lim, C. H., Roh, T.-Y., and Jung, G. Y. (2013)

448

Synthetic RNA devices to expedite the evolution of metabolite-producing microbes. Nat.

449

Commun. 4, 1413.

450

(26) Kötter, P., Weigand, J. E., Meyer, B., Entian, K.-D., and Suess, B. (2009) A fast and

451

efficient translational control system for conditional expression of yeast genes. Nucleic Acids Res.

452

37, e120.

21 ACS Paragon Plus Environment

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453

(27) Goodson, M. S., Bennett, A. C., Jennewine, B. R., Briskin, E., Harbaugh, S. V., and Kelley-

454

Loughnane, N. (2017) Amplifying riboswitch signal output using cellular wiring. ACS Synth.

455

Biol. 6, 1440–1444.

456

(28) Tuerk, C., and Gold, L. (1990) Systematic evolution of ligands by exponential enrichment:

457

RNA ligands to bacteriophage T4 DNA polymerase. Science 249, 505–510.

458

(29) Ellington, A. D., and Szostak, J. W. (1990) In vitro selection of RNA molecules that bind

459

specific ligands. Nature 346, 818–822.

460

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Graphical Table of Contents 130x41mm (300 x 300 DPI)

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Figure 1 183x151mm (300 x 300 DPI)

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Figure 3 182x140mm (300 x 300 DPI)

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Figure 4 89x90mm (300 x 300 DPI)

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