Obtaining a Panel of Cascade Promoter-5â²-UTR ... - ACS Publications

Research Article pubs.acs.org/synthbio

Obtaining a Panel of Cascade Promoter-5′-UTR Complexes in Escherichia coli Shenghu Zhou,† Renpeng Ding,† Jian Chen,†,‡ Guocheng Du,† Huazhong Li,*,† and Jingwen Zhou*,† †

Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, and ‡National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China S Supporting Information *

ABSTRACT: A promoter is one of the most important and basic tools used to achieve diverse synthetic biology goals. Escherichia coli is one of the most commonly used model organisms in synthetic biology to produce useful target products and establish complicated regulation networks. During the fine-tuning of metabolic or regulation networks, the limited number of well-characterized inducible promoters has made implementing complicated strategies difficult. In this study, 104 native promoter-5′-UTR complexes (PUTR) from E. coli were screened and characterized based on a series of RNA-seq data. The strength of the 104 PUTRs varied from 0.007% to 4630% of that of the PBAD promoter in the transcriptional level and from 0.1% to 137% in the translational level. To further upregulate gene expression, a series of combinatorial PUTRs and cascade PUTRs were constructed by integrating strong transcriptional promoters with strong translational 5′-UTRs. Finally, two combinatorial PUTRs (PssrA-UTRrpsT and PdnaKJ-UTRrpsT) and two cascade PUTRs (PUTRssrA-PUTRinfC‑rplT and PUTRalsRBACE-PUTRinfC‑rplT) were identified as having the highest activity, with expression outputs of 170%, 137%, 409%, and 203% of that of the PBAD promoter, respectively. These engineered PUTRs are stable for the expression of different genes, such as the red fluorescence protein gene and the β-galactosidase gene. These results show that the PUTRs characterized and constructed in this study may be useful as a plug-and-play synthetic biology toolbox to achieve complicated metabolic engineering goals in fine-tuning metabolic networks to produce target products. KEYWORDS: metabolic engineering, promoter engineering, RT-qPCR, RNA-seq, synthetic biology, 5′-UTR

such as a modular optimization strategy have successfully improved the production of many important chemicals, such as terpenes,13 resveratrol,14 flavonoids,15 fatty acid,16 L-ornithine,17 alkanes,18 and fumarate.19 However, the limited number of vectors and promoters restricts the design of complicated metabolic engineering strategies. Multiplasmid expression systems not only require additional antibiotics to maintain plasmids, but could also lead to poor cell growth due to the metabolic burden of the maintenance of extra plasmids. On the basis of the development of DNA assembly20−22 and genome editing approaches,23,24 the metabolic burden can be effectively reduced by using promoters with broad strength to fine-tune complicated metabolic networks by nonvector expression strategies. Therefore, native cascade promoters should be investigated for the design of complicated metabolic engineering strategies of the genome. Promoter libraries with the ability to drive a wide range of expression levels could be established by error-prone PCR,9 degenerate primers,25,26 or nucleotide analogue mutagenesis.27 These mutational promoter libraries only have slight differences

Synthetic biology tools have been developed to redirect energy and substances to achieve a balance between cell growth and the synthesis of target products.1,2 With the development of synthetic biology and metabolic engineering, an increasing number of regulation strategies and multifunctional bioelements have been established.3,4 To optimize metabolic networks to achieve specific goals, balancing metabolic flux using various synthetic biology tools, including mRNA stability regulation by CRISPR/dCas95 and RNA interference approaches,6 and manipulating 5′-UTR structures7 as well as the ribosome binding site (RBS)8,9 and terminator sequences,10,11 have been developed. On the basis of these post-transcriptional metabolic strategies, gene expression can be extensively regulated. However, practical applications are still restricted by the relatively limited means available for a more straightforward regulation of transcription level. The promoter is the first switch of gene expression, and can directly determine the intracellular mRNA concentration at the transcription stage.12 Ideally, using a set of promoters to redirect metabolic flux could efficiently fine-tune metabolic networks to achieve specific goals.4 Recently, by combining different promoters with vectors in various copy numbers, transcriptional optimization strategies © 2017 American Chemical Society

Received: January 6, 2017 Published: March 2, 2017 1065

DOI: 10.1021/acssynbio.7b00006 ACS Synth. Biol. 2017, 6, 1065−1075

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ACS Synthetic Biology

Figure 1. Schematic of promoter screening and promoter-5′-UTR engineering. (a) The construction process of the pRed-PUTR-EGFP plasmids. One hundred and four PUTR-egf p DNA fragments were cloned into the multiple cloning site (MCS) site of the pRed-KIarsB plasmid to construct a series of pRed-PUTR-EGFP plasmids. The pRed-PUTR-EGFP plasmids carry an integration cassette which include two homologous arms (arsB-UP and arsB-DOWN), the kanamycin resistance gene flanked with an flippase recognition target (FRT) sequence, the series of PUTRs, and the egf p gene. (b) A schematic of the construction of the combinatorial PUTRs and cascade PUTRs. (b1) A schematic of a PUTR with a strong promoter and a weak 5′-UTR. RNA polymerases bind more readily with the core region to initiate transcription, while weak 5′-UTRs restrict mRNA binding with the ribosome for protein translation. (b2) A schematic of a PUTR with a weak promoter and a strong 5′-UTR. RNA polymerases have difficulty binding with the promoter to initiate transcription, while strong 5′-UTRs promote mRNA binding with the ribosome to translate proteins. (b3) A schematic of a combinatorial PUTR with both a strong promoter and a strong 5′-UTR. To construct a combinatorial promoter, a promoter from b1 and a 5′-UTR from b2 were fused, and two potential regulation sites (PRS), PRS2, and PRS3, were discarded. (b4) A schematic of cascade PUTR. A cascade PUTR was constructed by directly fusing two PUTRs with a strong promoter and a strong 5′-UTR, respectively. PRS1, PRS2, PRS3, and PRS4 were all retained. 1066


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ACS Synthetic Biology in sequence, leading to potentially unexpected recombination in expression cassettes.28,29 Native promoters from microorganism genomes not only have a wide range of transcriptional strength, but also show high diversity in promoter sequences.12 This provides an opportunity to precisely control gene expression and simultaneously bypass the interpromoter recombination of mutational promoters. Recently, the screening of native promoter-5′-UTR complexes (PUTRs) had attracted increasing attention. In Streptomyces albus J1074,30 Streptomycetes,31 and Bacillus subtilis,32 32, 116, and 84 PUTRs have been characterized, respectively. Furthermore, in a series of studies by Alon et al.,33−35 2000 E. coli native PUTRs were characterized. While these studies only focused on the genome-wide global promoter regulation characteristics, they did not screen gradient PUTRs with different transcriptional strengths. As one of the most common model microorganisms, E. coli has several advantages, such as having a well characterized genetic background, its fast growth, being amenable to genetic manipulation, and the availability of abundant gene expression tools. In this study, E. coli strain K12 MG1655 was chosen as the host to perform screening and engineering of native PUTRs. A total of 104 native PUTRs were characterized at the transcriptional and translational level based on a series of RNAseq data.36−38 These 104 PUTRs exhibited a wide span of transcriptional and translational expression abilities, with 0.1% to 137% and 0.007% to 4630% of that of the PBAD promoter, respectively, in the early stationary phase. To further improve the expression-driving abilities of these PUTRs, combinatorial PUTRs and cascade PUTRs were constructed. Four showed stronger activities than that of the PBAD promoter. In the expression of the red fluorescent protein gene (rfp) and βgalactosidase gene (lacZ), these PUTRs also exhibited stable expression levels. Hence, as an important synthetic biology component, these native PUTRs with gradient strengths and strong engineered PUTRs should be highly useful in the optimization of complicated metabolic networks for the biosynthesis of useful products.

Figure 2. Length and reads per kilobase per million mapped reads (RPKM) value of 104 PUTRs. Color of the points in the scatter diagram represent the RPKM values. Of all the PUTRs, 18.5% were shorter than 100 bp, 41.9% PUTRs were between 100 and 200 bp in length, 23.4% PUTRs were between 200 to 300 bp in length, and 11.3% PUTRs were between 300 and 400 bp in length.

to confirm that they were accurately inserted into the arsB site without any mutations. 2.2. Transcriptional Strength Measurement. The PUTRs were integrated into the genome rather than plasmids to avoid interference caused by a variation in plasmid copy number or the potential loss of plasmids. Because most of the chemical production stage consists of the stationary phase, RTqPCR was performed in the early stationary phase (11 h). The transcriptional strength of the library spanned a wide range, from 0.007% to 4630% of that of the PBAD promoter (detailed transcriptional information on the PUTR library is listed in Table S4). The strength of the commonly used arabinose promoter PBAD induced with 10 mM arabinose was defined as 100%. Eight PUTRs showed at least 2-fold higher expression than PBAD (Figure 3). Among them, PUTRssrA, PUTRdnaKJ, and PUTRrplNXE exhibited the highest activities, with 46.3-, 25.6-, and 16.7-fold higher expression than that of the PBAD promoter, respectively. The RT-qPCR results were significantly different from the RNA-seq data. This could be caused by at least two reasons: (1) because the potential regulation sites were removed, shorter PUTR regions may not actually reflect complicated genome regulatory networks; and (2) these PUTR regions may have some interactions with different ORFs and show different transcription levels. 2.3. Fluorescence Strength Measurements Across Different Growth Phases. To quantitatively determine PUTR expression strength, fluorescence levels were measured in five different growth phases: the lag phase (2 h), the early log phase (4 h), the midlog phase (6 h), the postlog phase (8 h), and the early stationary phase (11 h) (detailed translational information on the PUTR library is listed in Table S4). Compared with the strength of the PBAD promoter, five screened PUTRs, PUTR infC‑rplT , PUTR rpsU , PUTR lpp , PUTRrplNXE, and PUTRmdh, exhibited relatively high (more than 50% of that of PBAD) and stable expression levels in all five growth phases. Among them, PUTRinfC‑rplT exhibited the strongest expression level (37% higher than that of the PBAD promoter; Figure 4). Furthermore, PUTRcspA showed a high expression level in the early growth phase but weak expression after the postlog phase (Figure 5). This promoter could be useful to achieve specific metabolic engineering goals. In the early log phase, some PUTRs could be more active than during

2. RESULTS 2.1. PUTR Library Preparation. To screen a panel of native PUTRs with gradient strength, a series of RNA-seq data from previous studies was reanalyzed.36−38 A total of 104 native PUTRs were selected with reads per kilobase per million mapped reads (RPKM) values spanning from 6.36 to 76109.02 (detailed information on the PUTRs can be found in Table S3) (Figure 1a). Unlike the commonly used approach amplifying intergenic and additional upstream open reading frame (ORF) regions as PUTRs,32,35 in this study, the PUTRs were precisely amplified without superfluous sequences based on the E. coli K12 transcriptional regulatory database, RegulonDB39 (the sequences of the PUTRs are listed in Table S3). According to our results, 60.4% of the selected PUTRs were shorter than 200 bp, while only four PUTRs were longer than 400 bp (Figure 2). These short PUTRs could be helpful in reducing vector sizes and promoting DNA assembly. To precisely measure the expression-driving strength of these PUTRs, an efficient integration site, the arsB gene,40 was used to integrate expression of reporter genes with a single copy (Figure 1a). According to a report by Sabri et al., genes integrated into the arsB site exhibit a high level of expression without a negitive influence on cell growth. After all the 104 PUTRs were integrated into the genome, Sanger sequencing was performed 1067


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Figure 3. Transcriptional level of 104 promoters measured by RT-qPCR. Black bar represents the transcriptional level of the PBAD promoter with 10 mM arabinose induction, which was defined as 100%. Promoter strength higher than 2-fold of that of PBAD is shown in the inset histogram. Error bars are standard deviation of triplicate experiments. The detailed information on the PUTR library is listed in Table S4 and Supporting Information S1.

transcriptional level and translational level, respectively. When comparing these two methods, clear differences were observed. In the PUTR library, 13 noncoding RNA PUTRs, PUTRssrA, PUTRffs, PUTRssrS, PUTRcsrC, PUTRserV, PUTRvalUXY‑lysV, PUTRthrU, PUTRlysT, PUTRserU, PUTRrrnA, PUTRrrnG, and PUTRrrnH, exhibited very low fluorescence, but high mRNA levels. This could be because noncoding RNA lacks the RBS sites for binding with ribosomes, resulting in low translational levels. The 5 PUTRs (PUTRinfC‑rplT, PUTRrpsU, PUTRlpp, PUTRrplNXE, and PUTRmdh) with the highest fluorescence levels also showed discrepancies in their mRNA levels. PUTRinfC‑rplT and PUTRlpp exhibited higher fluorescence levels than mRNA levels, while PUTRrpsU, PUTRrplNXE, and PUTRmdh exhibited higher mRNA levels than fluorescence levels (Figure 6). In native PUTR structures, RBS sequence, small RNA regulation, and mRNA secondary structure greatly influence translational levels, which may partly explain discrepancies between the transcriptional and translational levels of the PUTR library. 2.5. PUTR Engineering for Enhanced Gene Expression. As described above, we found that native PUTRs with both high transcriptional level and high translational level are rare. However, stronger PUTRs with both high transcriptional and translational strength can be constructed by engineering novel PUTRs. Combinatorial PUTRs and cascade PUTRs were constructed based on the four PUTRs that were high transcription drivers (PUTRssrA, PUTRdnaKJ, PUTRgrpE, and PUTRalsRBACE) (Figure 3) and two PUTRs that were high translational drivers (PUTRinfC‑rplT and PUTRrpsT) (Figure 6), along with UTRstandard from the pRSF-Duet-1 plasmid with a strong RBS sequence.

Figure 4. The strengths of the five strongest native PUTRs and the PBAD promoter in different growth phases. PBAD promoter was induced by 10 mM arabinose, and fluorescence and OD600 were assayed in five different growth phases. The background expression was subtracted, and the promoter strength was defined as RFU/OD600 (relative fluorescence unit divided by the corresponding OD600). a.u.: arbitrary unit. Error bars are standard deviation.

other growth phases (Figure 5). The strength of the PUTR library in the lag phase, the early log phase, the midlog phase, the postlog phase, and the early stationary phase spanned from 0.07% to 120%, from 0.3% to 140%, from 0.05% to 123%, from 0.1% to 128%, and from 0.1% to 137% of that of the PBAD promoter, respectively. 2.4. Comparing the Results of RT-qPCR and Fluorescence Measurements. RT-qPCR and fluorescence measurements are representative of the PUTR strength in the 1068


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Figure 6. Heat map of PUTRs at the transcriptional and translational level. These 26 PUTRs had the highest fluorescence strength. The fluorescence (early stationary phase) and mRNA level resulting from the 10 mM arabinose-induced PBAD promoter was defined as 1, and the other PUTRs were normalized based on their strength relative to the PBAD promoter. The fluorescence levels of 13 of these PUTRs, PUTRinfC‑rplT, PUTRlpp, PUTRrplU‑rpmA, PUTRrpsT, PUTRrpmBG, PUTRrplJL, PUTRrpsL, PUTRinfC, PUTRcsrA, PUTRicd, PUTRacpP‑fabF, PUTRtpx, and PUTRopmX, were higher than their mRNA levels, which may show that they have strong 5′-UTR regions.

translational level (Figure 7a). Meanwhile, when UTRrpsT was combined with these four promoter regions, the combinatorial PUTRs PssrA-UTRrpsT, PdnaKJ-UTRrpsT, and PgrpE-UTRrpsT were constructed, which enhanced PUTR strength to 364%, 293%, and 170% of that of PUTRrpsT, respectively (Figure 7a). Compared with that of PBAD, PssrA-UTRrpsT and PdnaKJ-UTRrpsT exhibited strengths of 170% and 137%, respectively. In contrast, the strength of the combinatorial PUTRs PssrA-UTRinfC‑rplT, PdnaKJ-UTRinfC‑rplT, PgrpE-UTRinfC‑rplT, and PalsRBACE-UTRinfC‑rplT were unexpectedly reduced compared with that of PUTRinfC‑rplT (Figure 7a). This might result if potential regulation elements (PREs) upstream of UTRinfC‑rplT were destroyed in the PUTR engineering process (Figure 1b). In consideration of the destructive effect of the combinatorial PUTRs construction process, cascade PUTRs were constructed by directly fusing high transcriptional (PUTRssrA, PUTRdnaKJ, PUTRgrpE, and PUTRalsRBACE) and translational (PUTRinfC‑rplT and PUTRrpsT) PUTRs (Figure 1b). The strength of the cascade PUTRs, PUTRssrA-PUTR infC‑rplT, PUTRdnaKJ-PUTRinfC‑rplT, and PUTRalsRBACE-PUTRinfC‑rplT, were 394%, 125%, and 196% of that of PUTRinfC‑rplT, respectively (Figure 7b). Compared with that of the promoter PBAD, they exhibited strengths of 409%, 130%, and 203%, respectively. Meanwhile, when fused with PUTRrpsT, the strengths of PUTRssrAPUTRrpsT, PUTRdnaKJ-PUTRrpsT, PUTRgrpE-PUTRrpsT, and PUTRalsRBACE-PUTRrpsT were extremely reduced or only slightly improved (Figure 7b). To investigate the reason for this phenomenon, the 5′-UTR region mRNA secondary structure of PUTRrpsT (Figure 7c) and PUTRssrA-PUTRrpsT (Figure 7d) with the lowest strengths were simulated by the

Figure 5. Heat map of the expression level of 104 native PUTRs in five different growth phases. Whole cell fluorescence was measured in the lag phase (2 h), the early log phase (4 h), the midlog phase (6 h), the postlog phase (8 h), and the early stationary phase (11 h). Each experiment was performed in triplicate.

Combinatorial PUTRs were constructed by combining the promoter regions of PUTRssrA, PUTRdnaKJ, PUTRgrpE, and PUTRalsRBACE and the 5′-UTR region of PUTRinfC‑rplT, PUTRrpsT, and UTRstandard (Figure 1b). By combining with UTRstandard, PUTRs (PssrA-UTRstandard, PdnaKJ-UTRstandard, PgrpEUTRstandard, and PalsRBACE-UTRstandard) were constructed and exhibited gradient strength. The activity of the strongest PUTR, PssrA-UTRstandard, was 81.6% of that of the PBAD promoter in its 1069


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Figure 7. Expression strength and 5′-UTR region mRNA secondary structures of combinatorial PUTRs and cascade PUTRs. (a) Strength of combinatorial PUTRs. (b) Strength of cascade PUTRs. (c, d) 5′-UTR mRNA secondary structure of PUTRrpsT (c) and PUTRssrA-PUTRrpsT (d). The PBAD promoter was induced with 10 mM arabinose. *Group of combinatorial PUTRs which were constructed by combining UTRstandard with PssrA, PdnaKJ, PgrpE, and PalsRBACE, respectively. **Group of combinatorial PUTRs which were constructed by combining UTRrpsT with PssrA, PdnaKJ, PgrpE, and PalsRBACE, respectively. ***Group of combinatorial PUTRs which were constructed by combining UTRinfC‑rplT with PssrA, PdnaKJ, PgrpE, and PalsRBACE, respectively. ****Group of cascade PUTRs which were constructed by cascading PUTRssrA, PUTRdnaKJ, PUTRgrpE, and PUTRalsRBACE with PUTRinfC‑rplT, respectively. *****Group of cascade PUTRs which were constructed by cascading PUTRssrA, PUTRdnaKJ, PUTRgrpE, and PUTRalsRBACE with PUTRrpsT, respectively. a.u.: arbitrary unit. Error bars are standard deviations of experiments performed in triplicate.

PUTRinfC‑rplT, PUTRssrA-PUTRinfC‑rplT, and PUTRinfC‑rplT were slightly reduced compared with that of the other two report genes (Figure 8). To examine the mechanism underlying this phenomenon, the 5′-mRNA (including UTRinfC‑rplT and the 40 bp coding sequence) minimum free energies of egf p, rfp, and lacZ were calculated by the RNAfold WebServer. The results showed that the 5′-mRNA of rf p had the lowest minimum free energy (ΔG: −65.4 (rfp) vs −56 (egf p) vs −53.7 (lacZ) kcal/ mol). This may be a potential reason for the lower rf p expression level.

RNAfold WebServer (http://rna.tbi.univie.ac.at/) with default setting.41 The results indicated that the RBS of PUTRrpsT is exposed, while the RBS of PUTRssrA-PUTRrpsT is hidden in a hairpin structure. The hairpin structure covered the RBS of PUTRssrA-PUTRrpsT and impeded mRNA binding with the ribosome to start translation, which may be the potential mechanism underlying the low translational level.42 2.6. RFP and β-Galactosidase Expression by Engineered PUTRs. A challenge facing most native PUTRs is their unpredictable strength when expressing different genes. To evaluate the impact of mRNA secondary structure on the expression of different genes by the engineered PUTRs, two reporter genes, the rf p and lacZ, were expressed by engineered PUTRs. Overall, the engineered PUTRs had stable expression strength when expressing the egfp, rf p, and lacZ genes (Figure 8). Meanwhile, when expressing rf p, the strength of PssrA-

3. DISCUSSION As a commonly used heterologous gene expression platform, E. coli has been successfully used to produce many useful chemicals.43 However, one challenge is that the limited number of synthetic biology tools restricted the implementation of 1070


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the strength of 104 PUTRs at both the translational and transcriptional level. This could be more reliable to achieve more precise fine-turning metabolic networks. Generally, the protein translation initiation rate is controlled by the 5′-mRNA secondary structure, the RBS, the sequence between the RBS and the start codon, and the affinity between the start codon and its tRNA.7,46 Currently available prediction tools such as RBS calculator47 and 5′-UTR designer7 could be applied to rationally design 5′-UTR sequences with ideal strength to manipulate gene expression levels. Unfortunately, predicted sequence strength is usually discordant with actual strength. In this study, a series of strong 5′-UTR sequences that have been validated by wet-lab experiments were screened (Figure 6), which could be useful for enhancing gene expression. In a previous work, Kucharova et al. significantly improved protein expression levels by 60-fold by cloning an efficient 5′-mRNA sequence in front of the target gene.48 Inspired by their study, egfp expression level was further increased by 4-fold by combining PUTRs with high translational strength. Meanwhile, both a stable 5′-mRNA secondary structure49,50 and a long 5′-UTR51 restrained the expression of different genes. To further explore the effects of the 5′-mRNA secondary structure on the expression of target genes, two other reporter genes were expressed, and their 5′-mRNA minimum free energy was analyzed. The expression level was slightly reduced when expressing rf p, which has a lower 5′mRNA minimum free energy (Figure 8). The RBS47 and the sequence between the RBS and the start codon52 could also affect gene expression. Furthermore, to conclusively exclude the negative effects of a complicated 5′-mRNA secondary structure, a ribozyme-based insulator RiboJ could be used to split long 5′UTR sequences to simplify the 5′-mRNA secondary structure.53 To develop more synthetic biology tools to facilitate the flexible fine-tuning of genetic pathways, we screened and characterized 104 native PUTRs with gradient strength at both the translational and transcriptional level. PUTR engineering was also performed and led to a 3-fold improvement in translational level when compared with that of PBAD. Of these PUTRs, 60.4% were shorter than 200 base pairs in length. These short and gradient PUTRs could promote DNA manipulation and enhance gene expression. Furthermore, although computational metabolic pathway design strategies have been successfully developed,51,54 it is challenging to predict metabolic flux based on promoter strength. The promoters with gradient strength screened in this study could improve current prediction approaches. Combined with the development of ePathBrick vectors55 and genome editing approaches,23,56 these native and engineered PUTRs could be flexibly used as a series of plug-and-play synthetic tools for finetuning genetic pathways and reducing metabolic burden with single or nonvector expression systems.

Figure 8. PUTR expression strength for egfp, rf p, and lacZ. PBAD promoter was induced by 10 mM arabinose, and its strength was defined as 100%. Black bars represent egf p expression, gray bars represent rf p expression, and light gray bars represent lacZ expression.

complicated metabolic strategies. To address this challenge, 104 native PUTRs with gradient strength were screened based on a series of RNA-seq data.36−38 The PUTR regions were amplified based on the E. coli K12 regulatory database, RegulonDB.39 Furthermore, PUTR engineering was performed to construct stronger PUTRs. Finally, this resulted in the construction of PUTRssrA-PUTRinfC‑rplT, which showed 409% of the strength of that of the PBAD promoter. These PUTRs could be helpful in the achievement of complicated synthetic biology strategies. The most commonly used promoters in E. coli are inducible promoters, which were developed for the overexpression of single proteins in the 1990s or even earlier.44 Combining different inducible promoters to fine-tune the expression of multigene pathways requires the addition of extra inducers, leading to increased cell growth toxicity and production costs. To extend promoter strength, Alper et al. established a promoter library through the mutagenesis of a native promoter in E. coli.27 The mRNA level of this library spanned a range for 325-fold. In comparison, our study acquired a wider range, up to 6.6 × 105-fold in transcriptional level. Furthermore, RNA-seq technology provides an opportunity to detect the expression levels of genes genome-wide to screen for gradient promoters with different strengths.30 However, promoters, attenuators, and enhancers usually coregulate the expression of the same gene in prokaryotes,45 thus increasing the complexity of confirming which part of a sequence makes a significant contribution to downstream gene expression. A series of RNAseq data36−38 were reanalyzed, and we preliminarily screened 104 PUTRs with gradient strengths. Additionally, the PUTR regions were amplified based on the RegulonDB database39 to express egf p to precisely measure promoter strengths. The clear difference between our data and the RNA-seq data reported previously further shows the importance of the current work. In a previous study, Alon and co-workers also established a comprehensive PUTR library, which included 2000 native PUTRs.35 However, that study only investigated the global promoter regulation characteristics under different growth conditions.33,34 They did not provide information about precise PUTR strengths in translational and transcriptional levels by comparing with a well-characterized promoter. Here, a wellknown promoter, PBAD, was selected as a standard to calculate

4. MATERIALS AND METHODS 4.1. Strains and Plasmids. E. coli strain JM109 was used as a cloning host. E. coli strain K12 MG1655 was used as host for protein expression and for measuring promoter strength. E. coli K12 MG1655 (ΔlacZ) was used to express β-galactosidase. TVector pMD19 (Simple) was purchased from TaKaRa (Dalian, China). The details of the strains and plasmids used are listed in Table 1. 4.2. Medium and Culture Conditions. Promoter library strains were cultured in Luria−Bertani broth (LB) medium 1071


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the flippase recognition target (FRT) sequence was amplified by primer pair Kan-F/Kan-R(MCS) using the pKD13 plasmid as the template. The amplified kanamycin resistance gene contained a multiple cloning site (MCS). First, the kanamycin resistance gene was ligated with T-Vector pMD19 (Simple), resulting in pMD-Kan. Subsequently, up- and downstream homologous arms were inserted into the ApaI/EcoRI and KpnI/SalI sites of pMD-Kan, respectively. The resulting plasmid was named pRed-KIarsB (Figure 1a), which was used for knocking genes into the arsB locus by λ-Red recombination. A total of 104 PUTRs were amplified from the genome of E. coli K12 MG1655 by the primer pair Promoter-F/Promoter-R (the primers are listed in Table S1). The egf p reporter gene was amplified from pYES2-EGFP plasmids by the primer pair EGFP-F/EGFP-R.58 Then, the 104 PUTRs were fused with the egf p gene and cloned into the MCS of the pRed-KIarsB plasmid. The resulting series of plasmids was named pRedPUTR-EGFP (Figure 1a). To precisely measure the strength of these PUTRs, recombination cassettes (including two homologous arms, the kanamycin resistance gene, the egfp gene, and the promoter-5′-UTR complex) were recruited from pRedPUTR-EGFP plasmids by SmaI or ApaI/SalI digestion. The recruited recombination cassettes were inserted in the arsB locus of the genome by λ-Red recombination.59 Positive strains were screened by colony PCR before validation by sequencing (Sangon Biotech, Shanghai, China). The well-known strong inducible promoter PBAD (induced with 10 mM of arabinose) was used as the standard promoter for normalizing the strength of these PUTRs. 4.4. PUTR Engineering. The four strongest promoters (PssrA, PdnaKJ, PgrpE, and PalsAR) and the three strongest 5′-UTRs (UTRstandard, UTRrpsT, and UTRinfC) were amplified by PCR amplification (the primers are listed in Table S2). UTRstandard from the commonly used pRSFDuet-1 plasmid (Novagen) contains a strong RBS sequence (AAGGAG).60 Then, these promoters and 5′-UTRs were fused to construct 12 combinatorial PUTRs. Furthermore, the four PUTRs

Table 1. Strains and Plasmids Used in This Study strains and plasmids E. coli JM109 E. coli K12 MG1655 T-Vector pMD19 (Simple) pYES2-EGFP pUC57-RFP pUC19 pRed-arsB pRed-PUTREGFP pRed-PUTR-RFP pRed-PUTR-lacZ pKD46 pKD13 pRSF-Duet-1

properties

source

wild type wild type

this study Tianjin University TaKaRa

T vector, AmpR Carrying egf p gene, AmpR carrying rf p gene, AmpR carrying α-lacZ gene, AmpR homologous arms of arsB gene, KanR, AmpR promoter-egf p fragments were cloned into MCS of pRed-arsB PUTRs-rf p fragments were cloned into MCS of pRed-arsB PUTRs-lacZ fragments were cloned into MCS of pRed-arsB PBAD promoter, carrying exo, bet, gam genes, AmpR, carrying KanR gene KanR

this study GeneScript Tiangen this study this study this study this study this study this study Novagen

overnight. Then, 2.5% of these cultures was inoculated into MOPS minimal medium57 for the expression of the lacZ, rf p, and egf p genes at 37 °C with 220 rpm orbital shaking. Ampicillin (100 μg/mL) and kanamycin (50 μg/mL) were added to the media as required. 4.3. Promoter Library Construction. All PCR experiments described below were amplified by PrimeSTAR HS (premix) DNA polymerase (TaKaRa). DNA fragments were purified by a GeneJET Gel Extraction Kit (Thermo Fisher Scientific, Waltham, MA, USA). The primers used are listed in Table 2. Homologous arms of 1000 bp around the arsenite transporter gene (arsB) gene were amplified from the E. coli K12 MG1655 genome by the primer pairs LA-F/LA-R and RA-F/ RA-R, respectively. The kanamycin resistance gene flanked with Table 2. Primers Used in This Study sequencea (from 5′ to 3′)

primers

restriction site

ATGGGTAAGGGAGAAGAACTTTTC CGCGGTACCGAGCTCGGCCGCAAATTAAAGCCTT AATTGGGCCCCCCGGGTGAAAGCGTTTATGCGCATT

SacI, KpnI ApaI, SmaI

LA-R RA-F

CGGAATTCAATGCCTCCCGGATAAAACAC GGGGTACCTGAGATACTGATATGAGCAACATTACCA

EcoRI KpnI

RA-R Kan-F

ACGCGTCGACCCCGGGATTTTCTTCTACACACAATAAATGTAATGC AATTGGGCCCAATTGAATTCGAATTCTCCGTGGACCTGCAGTTCG

SmaI, SalI ApaI, EcoRI

Kan-R(MCS)

ACGCGTCGACACGCGGTACCGAGCTCGGATCCGCTAGCGTGTAGGCTGGAGCTGCTTCG

N-promoter-F N-promoter-R EGFPF(qPCR) EGFPR(qPCR) 16S−F(qPCR) 16S-R(qPCR)

Listed in S1 Table Listed in S1 Table GCCAACACTTGTCACTACTCTTAC

NheI, BamHI, SacI, KpnI, SalI NheI, BamHI

a

products egf p gene

EGFP-F EGFP-R LA-F

up-stream homologous arm down-stream homologous arm Kan resistance with a MCS site

promoters

CCGTCGTCCTTGAAGAAGATGG TCTTGACATCCACAGAACTT TAACCCAACATTTCACAACA

Underlined letters are restriction enzyme cut sites. 1072


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ACS Synthetic Biology (PUTRssrA, PUTRdnaKJ, PUTRgrpE, and PUTRalsAR) with the highest transcriptional levels and the two PUTRs (PUTRrpsT and PUTRinfC) with the highest translational levels were amplified and fused to construct eight cascade PUTRs (the primers are listed in Table S2). The reporter genes rf p and lacZ were amplified from the pUC57-RFP (synthesized by GeneScript, Nanjing, China) and pUC19 (Tiangen, Beijing, China) plasmids, respectively. Then, a second round of overlapping PCR was performed to fuse the constructed combinatorial PUTRs and cascade PUTRs with their relative reporter genes (Figure 1b). The resulting PCR products, PUTRs-rf p and PUTRs-lacZ, were inserted into the BamHI or KpnI sites of the pRed-KIarsB plasmid, respectively, resulting in a series of pRed-PUTR-RFP and pRed-PUTR-lacZ plasmids (Table 1). 4.5. Real-Time Fluorescence Quantitative PCR (RTqPCR). Overnight cultured strains were inoculated with 4 mL of MOPS medium at a ratio of 2.5%. After growing for 11 h at 37 °C with 220 rpm orbital shaking, the cells were harvested for mRNA isolation. The harvested cells were processed according to the manufacturer’s instructions using the RNAprep Pure Cell/Bacteria Kit (Tiangen, Beijing, China). cDNA was synthesized using the PrimeScript RT reagent Kit with gDNA Eraser (TaKaRa), using the extracted mRNA as template. RTqPCR was performed using SYBR Premix Ex Taq (Tli RNaseH Plus) on a LightCycler 480 II thermal cycler system (Roche, Mannheim, Germany). The internal standard gene, 16S rRNA, was amplified by the primer pair 16S-F(qPCR)/16S-R(qPCR), and the egf p gene was amplified by the primer pair EGFPF(qPCR)/EGFP-R(qPCR). 4.6. Fluorescence Measurements. The recombinant strains for egf p or rf p expression were grown in 4 mL of MOPS medium at 37 °C with 220 rpm orbital shaking. In different growth phases, the cells were harvested and washed twice by phosphate buffered saline (PBS) solution (pH 7.4). Whole cell fluorescence and cell density (OD600) were measured on a Cytation 3 imaging reader system (BioTek, Winooski, VT, USA). Fluorescence intensity was defined as RFU/OD600 (relative fluorescence unit divided by the corresponding OD600) according to our previous study.61 The wild-type strain E. coli K12 MG1655 was used as the negative control, and its fluorescence intensity was subtracted as background. The emission and excitation wavelength of EGFP are 520 and 488 nm, respectively. The emission and excitation wavelength of RFP are 610 and 584 nm, respectively. 4.7. Measurement of β-Galactosidase Activity. Strains carrying the lacZ gene were grown in 4 mL of MOPS medium at 37 °C with 220 rpm orbital shaking. The cells were harvested and washed twice by PBS solution (pH 7.4). β-Galactosidase activity was determined using a Bacterial Gal Colorimetric Assay Kit (GenMed Scientifics, Westbury, NY, USA) at 420 nm in a 96-well plate.62 A final concentration of 15 μg/mL of total protein was loaded into each well of a 96-well plate. The protein concentration was normalized by an enhanced BCA protein assay kit (Beyotime Biotechnology, Shanghai, China). E. coli K12 MG1655 (ΔlacZ) was used as the negative control.

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Transcriptional level of 104 promoters measured by RTqPCR (PDF) Primer sequences for amplification of PUTR candidates (XLSX) Primer sequences for construction engineered PUTRs (XLSX) Detailed PUTR information (XLSX) PUTR transcriptional and translational level (XLSX)

AUTHOR INFORMATION

Corresponding Authors

*Tel.: +86-510-85329031. Fax: +86-510-85918309. E-mail: [email protected]. *E-mail: [email protected] ORCID

Jingwen Zhou: 0000-0002-3949-3733 Author Contributions

J. Z., J. C., H. L., and S. Z. designed the study and wrote the manuscript. J. C., H. L., and G. D. critically revised the manuscript. S. Z., J. Z., and R. D. performed the experiments and analyzed the results. J. C., H. L., and G. D. designed and supervised the project. All authors discussed the results and commented on the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Major State Basic Research Development Program of China (973 Program, 2013CB733602), the National Natural Science Foundation of China (21390204, 31370130, 31670095), the Key Technologies R&D Program of Jiangsu Province, China (BE2014698), the Fundamental Research Funds for the Central Universities (JUSRP51701A), and the 111 Project (111-2-06).

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REFERENCES

(1) Wu, G., Yan, Q., Jones, J. A., Tang, Y. J., Fong, S. S., and Koffas, M. A. G. (2016) Metabolic burden: Cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol. 34 (8), 652−664. (2) Xu, P., Bhan, N., and Koffas, M. A. G. (2013) Engineering plant metabolism into microbes: from systems biology to synthetic biology. Curr. Opin. Biotechnol. 24 (2), 291−299. (3) Jones, J. A., Toparlak, O. D., and Koffas, M. A. G. (2015) Metabolic pathway balancing and its role in the production of biofuels and chemicals. Curr. Opin. Biotechnol. 33, 52−59. (4) Xu, P., Rizzoni, E. A., Sul, S.-Y., and Stephanopoulos, G. (2017) Improving metabolic pathway efficiency by statistical model-based multivariate regulatory metabolic engineering. ACS Synth. Biol. 6 (1), 148−158. (5) Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P., and Lim, W. A. (2013) Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152 (5), 1173−1183. (6) Hannon, G. J. (2002) RNA interference. Nature 418 (6894), 244−251. (7) Seo, S. W., Yang, J.-S., Kim, I., Yang, J., Min, B. E., Kim, S., and Jung, G. Y. (2013) Predictive design of mRNA translation initiation region to control prokaryotic translation efficiency. Metab. Eng. 15, 67−74. (8) Zhang, B., Zhou, N., Liu, Y. M., Liu, C., Lou, C. B., Jiang, C. Y., and Liu, S. J. (2015) Ribosome binding site libraries and pathway modules for shikimic acid synthesis with Corynebacterium glutamicum. Microb. Cell Fact. 14, 71.

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S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssynbio.7b00006. 1073


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ACS Synthetic Biology (9) Markley, A. L., Begemann, M. B., Clarke, R. E., Gordon, G. C., and Pfleger, B. F. (2015) Synthetic biology toolbox for controlling gene expression in the cyanobacterium Synechococcus sp strain PCC 7002. ACS Synth. Biol. 4 (5), 595−603. (10) Curran, K. A., Karim, A. S., Gupta, A., and Alper, H. S. (2013) Use of expression-enhancing terminators in Saccharomyces cerevisiae to increase mRNA half-life and improve gene expression control for metabolic engineering applications. Metab. Eng. 19, 88−97. (11) Yamanishi, M., Ito, Y., Kintaka, R., Imamura, C., Katahira, S., Ikeuchi, A., Moriya, H., and Matsuyama, T. (2013) A genome-wide activity assessment of terminator regions in Saccharomyces cerevisiae provides a ″terminatome″ toolbox. ACS Synth. Biol. 2 (6), 337−347. (12) Zhou, S., Du, G., Kang, Z., Li, J., Chen, J., Li, H., and Zhou, J. (2017) The application of powerful promoters to enhance gene expression in industrial microorganisms. World J. Microbiol. Biotechnol. 33 (2), 23. (13) Zhou, Y. J., Gao, W., Rong, Q., Jin, G., Chu, H., Liu, W., Yang, W., Zhu, Z., Li, G., Zhu, G., Huang, L., and Zhao, Z. K. (2012) Modular pathway engineering of diterpenoid synthases and the mevalonic acid pathway for miltiradiene production. J. Am. Chem. Soc. 134 (6), 3234−3241. (14) Lim, C. G., Fowler, Z. L., Hueller, T., Schaffer, S., and Koffas, M. A. (2011) High-yield resveratrol production in engineered Escherichia coli. Appl. Environ. Microb 77 (10), 3451−60. (15) Wu, J. J., Du, G. C., Zhou, J. W., and Chen, J. (2013) Metabolic engineering of Escherichia coli for (2S)-pinocembrin production from glucose by a modular metabolic strategy. Metab. Eng. 16, 48−55. (16) Xu, P., Gu, Q., Wang, W. Y., Wong, L., Bower, A. G. W., Collins, C. H., and Koffas, M. A. G. (2013) Modular optimization of multigene pathways for fatty acids production in E. coli. Nat. Commun. 4, 8. (17) Qin, J. F., Zhou, Y. J. J., Krivoruchko, A., Huang, M. T., Liu, L. F., Khoomrung, S., Siewers, V., Jiang, B., and Nielsen, J. (2015) Modular pathway rewiring of Saccharomyces cerevisiae enables highlevel production of L-ornithine. Nat. Commun. 6, 8224. (18) Sheppard, M. J., Kunjapur, A. M., and Prather, K. L. J. (2016) Modular and selective biosynthesis of gasoline-range alkanes. Metab. Eng. 33, 28−40. (19) Chen, X. L., Zhu, P., and Liu, L. M. (2016) Modular optimization of multi-gene pathways for fumarate production. Metab. Eng. 33, 76−85. (20) Coussement, P., Maertens, J., Beauprez, J., Van Bellegem, W., and De Mey, M. (2014) One step DNA assembly for combinatorial metabolic engineering. Metab. Eng. 23, 70−77. (21) Gibson, D. G., Young, L., Chuang, R. Y., Venter, J. C., Hutchison, C. A., and Smith, H. O. (2009) Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6 (5), 343−345. (22) Jin, P., Kang, Z., Zhang, J. L., Zhang, L. P., Du, G. C., and Chen, J. (2016) Combinatorial evolution of enzymes and synthetic pathways using one-step PCR. ACS Synth. Biol. 5 (3), 259−268. (23) Gao, F., Shen, X. Z., Jiang, F., Wu, Y., and Han, C. (2016) DNAguided genome editing using the Natronobacterium gregoryi Argonaute. Nat. Biotechnol. 34, 768−773. (24) Cong, L., Ran, F. A., Cox, D., Lin, S. L., Barretto, R., Habib, N., Hsu, P. D., Wu, X. B., Jiang, W. Y., Marraffini, L. A., and Zhang, F. (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339 (6121), 819−823. (25) Jensen, P. R., and Hammer, K. (1998) The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. Appl. Environ. Microb 64 (1), 82−87. (26) Jones, J. A., Vernacchio, V. R., Lachance, D. M., Lebovich, M., Fu, L., Shirke, A. N., Schultz, V. L., Cress, B., Linhardt, R. J., and Koffas, M. A. G. (2015) ePathOptimize: A Combinatorial Approach for Transcriptional Balancing of Metabolic Pathways. Sci. Rep. 5, 11301. (27) Alper, H., Fischer, C., Nevoigt, E., and Stephanopoulos, G. (2005) Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. U. S. A. 102 (36), 12678−12683.

(28) Vogl, T., Sturmberger, L., Kickenweiz, T., Wasmayer, R., Schmid, C., Hatzl, A.-M., Gerstmann, M. A., Pitzer, J., Wagner, M., Thallinger, G. G., Geier, M., and Glieder, A. (2016) A toolbox of diverse promoters related to methanol utilization: Functionally verified parts for heterologous pathway expression in Pichia pastoris. ACS Synth. Biol. 5 (2), 172−186. (29) Montiel, D., Kang, H. S., Chang, F. Y., Charlop-Powers, Z., and Brady, S. F. (2015) Yeast homologous recombination-based promoter engineering for the activation of silent natural product biosynthetic gene clusters. Proc. Natl. Acad. Sci. U. S. A. 112 (29), 8953−8958. (30) Luo, Y., Zhang, L., Barton, K. W., and Zhao, H. (2015) Systematic identification of a panel of strong constitutive promoters from streptomyces albus. ACS Synth. Biol. 4 (9), 1001−1010. (31) Li, S., Wang, J., Li, X., Yin, S., Wang, W., and Yang, K. (2015) Genome-wide identification and evaluation of constitutive promoters in streptomycetes. Microb. Cell Fact. 14 (1), 172−172. (32) Song, Y., Nikoloff, J. M., Fu, G., Chen, J., Li, Q., Xie, N., Zheng, P., Sun, J., and Zhang, D. (2016) Promoter screening from Bacillus subtilis in various conditions hunting for synthetic biology and industrial applications. PLoS One 11 (7), e0158447. (33) Keren, L., Zackay, O., Lotan-Pompan, M., Barenholz, U., Dekel, E., Sasson, V., Aidelberg, G., Bren, A., Zeevi, D., Weinberger, A., Alon, U., Milo, R., and Segal, E. (2013) Promoters maintain their relative activity levels under different growth conditions. Mol. Syst. Biol. 9, 701. (34) Madar, D., Dekel, E., Bren, A., Zimmer, A., Porat, Z., and Alon, U. (2013) Promoter activity dynamics in the lag phase of Escherichia coli. BMC Syst. Biol. 7 (1), 136. (35) Zaslaver, A., Bren, A., Ronen, M., Itzkovitz, S., Kikoin, I., Shavit, S., Liebermeister, W., Surette, M. G., and Alon, U. (2006) A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat. Methods 3 (8), 623−628. (36) Shiroguchi, K., Jia, T. Z., Sims, P. A., and Xie, X. S. (2012) Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proc. Natl. Acad. Sci. U. S. A. 109 (4), 1347−1352. (37) Meysman, P., Sonego, P., Bianco, L., Fu, Q., Ledezma-Tejeida, D., Gama-Castro, S., Liebens, V., Michiels, J., Laukens, K., Marchal, K., Collado-Vides, J., and Engelen, K. (2014) COLOMBOS v2.0: an ever expanding collection of bacterial expression compendia. Nucleic Acids Res. 42 (D1), D649−D653. (38) Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., Emili, A., and Xie, X. S. (2010) Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329 (5991), 533−538. (39) Gama-Castro, S., Salgado, H., Santos-Zavaleta, A., LedezmaTejeida, D., Muniz-Rascado, L., Garcia-Sotelo, J. S., AlquiciraHernandez, K., Martinez-Flores, I., Pannier, L., Castro-Mondragon, J. A., Medina-Rivera, A., Solano-Lira, H., Bonavides-Martinez, C., PerezRueda, E., Alquicira-Hernandez, S., Porron-Sotelo, L., Lopez-Fuentes, A., Hernandez-Koutoucheva, A., Del Moral-Chavez, V., Rinaldi, F., and Collado-Vides, J. (2016) RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res. 44 (D1), D133−D143. (40) Sabri, S., Steen, J. A., Bongers, M., Nielsen, L. K., and Vickers, C. E. (2013) Knock-in/Knock-out (KIKO) vectors for rapid integration of large DNA sequences, including whole metabolic pathways, onto the Escherichia coli chromosome at well-characterised loci. Microb. Cell Fact. 12, 60. (41) Gruber, A. R., Lorenz, R., Bernhart, S. H., Neuböck, R., and Hofacker, I. L. (2008) The vienna RNA websuite. Nucleic Acids Res. 36, W70−W74. (42) de Smit, M. H., and Van Duin, J. (1990) Secondary structure of the ribosome binding site determines translational efficiency: a quantitative analysis. Proc. Natl. Acad. Sci. U. S. A. 87 (19), 7668−7672. (43) Chen, X. Z., Zhou, L., Tian, K. M., Kumar, A., Singh, S., Prior, B. A., and Wang, Z. X. (2013) Metabolic engineering of Escherichia coli: A sustainable industrial platform for bio-based chemical production. Biotechnol. Adv. 31 (8), 1200−1223. 1074


Research Article

ACS Synthetic Biology (44) Terpe, K. (2006) Overview of bacterial expression systems for heterologous protein production: from molecular and biochemical fundamentals to commercial systems. Appl. Microbiol. Biotechnol. 72 (2), 211−222. (45) Zhou, D., and Yang, R. (2006) Global analysis of gene transcription regulation in prokaryotes. Cell. Mol. Life Sci. 63 (19), 2260−2290. (46) Borujeni, A. E., Channarasappa, A. S., and Salis, H. M. (2014) Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites. Nucleic Acids Res. 42 (4), 2646−2659. (47) Salis, H. M., Mirsky, E. A., and Voigt, C. A. (2009) Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 27 (10), 946−950. (48) Kucharova, V., Skancke, J., Brautaset, T., and Valla, S. (2013) Design and optimization of short DNA sequences that can be used as 5′ fusion partners for high-level expression of heterologous genes in Escherichia coli. Appl. Environ. Microb 79 (21), 6655−6664. (49) Li, G.-W. (2015) How do bacteria tune translation efficiency? Curr. Opin. Microbiol. 24, 66−71. (50) Goodman, D. B., Church, G. M., and Kosuri, S. (2013) Causes and effects of N-terminal codon bias in bacterial genes. Science 342 (6157), 475−479. (51) Farasat, I., Kushwaha, M., Collens, J., Easterbrook, M., Guido, M., and Salis, H. M. (2014) Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria. Mol. Syst. Biol. 10 (6), 731. (52) Chen, H., Bjerknes, M., Kumar, R., and Jay, E. (1994) Determination of the optimal aligned spacing between the Shine− Dalgarno sequence and the translation initiation codon of Escherichia coli mRNAs. Nucleic Acids Res. 22 (23), 4953−4957. (53) Lou, C. B., Stanton, B., Chen, Y. J., Munsky, B., and Voigt, C. A. (2012) Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nat. Biotechnol. 30 (11), 1137−1142. (54) Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M., and Palsson, B. O. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol. Syst. Biol. 7, 535. (55) Xu, P., Vansiri, A., Bhan, N., and Koffas, M. A. G. (2012) ePathBrick: A synthetic biology platform for engineering metabolic pathways in E. coli. ACS Synth. Biol. 1 (7), 256−266. (56) Ran, F. A., Hsu, Patrick, D., Lin, C.-Y., Gootenberg, Jonathan, S., Konermann, S., Trevino, A. E., Scott, David, A., Inoue, A., Matoba, S., Zhang, Y., and Zhang, F. (2013) Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154 (6), 1380−1389. (57) Neidhardt, F. C., Bloch, P. L., and Smith, D. F. (1974) Culture medium for enterobacteria. J. Bacteriol. 119 (3), 736−747. (58) Zhou, J., Huang, L., Liu, L., and Chen, J. (2009) Enhancement of pyruvate productivity by inducible expression of a F0F1-ATPase inhibitor INH1 in Torulopsis glabrata CCTCC M202019. J. Biotechnol. 144 (2), 120−126. (59) Datsenko, K. A., and Wanner, B. L. (2000) One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. U. S. A. 97 (12), 6640−6645. (60) Hayes, W. S., and Borodovsky, M. (1998) Deriving ribosomal binding site (RBS) statistical models from unannotated DNA sequences and the use of the RBS model for N-terminal prediction. Pac Symp. Biocomput., 279−290. (61) Hu, Y., Wan, H., Li, J., and Zhou, J. (2015) Enhanced production of L-sorbose in an industrial Gluconobacter oxydans strain by identification of a strong promoter based on proteomics analysis. J. Ind. Microbiol. Biotechnol. 42 (7), 1039−1047. (62) Miller, J. H. (1972) Experiments in molecular genetics, Vol. 58, p 893−924, Cold Spring Harbor Laboratory, New York.

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