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Prediction and characterization of promoters and ribosomal binding sites of Zymomonas mobilis in system biology era

Contributed equally
Biotechnology for Biofuels201912:52

https://doi.org/10.1186/s13068-019-1399-6

  • Received: 29 December 2018
  • Accepted: 8 March 2019
  • Published:

Abstract

Background

Zymomonas mobilis is a model bacterial ethanologen with many systems biology studies reported. Besides lignocellulosic ethanol production, Z. mobilis has been developed as a platform for biochemical production through metabolic engineering. However, identification and rigorous understanding of the genetic origins of cellular function, especially those based in non-coding region of DNA, such as promoters and ribosomal binding sites (RBSs), are still in its infancy. This knowledge is crucial for the effective application of Z. mobilis to new industrial applications of biotechnology for fuels and chemicals production.

Results

In this study, we explored the possibility to systematically predict the strength of promoters based on systems biology datasets. The promoter strength was clustered based on the expression values of downstream genes (or proteins) from systems biology studies including microarray, RNA-Seq and proteomics. Candidate promoters with different strengths were selected for further characterization, which include 19 strong, nine medium, and ten weak ones. A dual reporter-gene system was developed which included appropriate reporter genes. These are the opmCherry reporter gene driven by the constitutive PlacUV5 promoter for calibration, and EGFP reporter gene driven by candidate promoters for quantification. This dual reporter-gene system was confirmed using the inducible promoter, Ptet, which was used to determine the strength of these predicted promoters with different strengths. In addition, the dual reporter-gene system was applied to determine four synthetic RBSs with different translation initiation rates based on the prediction from bioinformatics server RBS calculator. Our results showed that the correlations between the prediction and experimental results for the promoter and RBS strength are relatively high, with R2 values more than 0.7 and 0.9, respectively.

Conclusions

This study not only identified and characterized 38 promoters and four RBSs with different strengths for future metabolic engineering in Z. mobilis, but also established a flow cytometry-based dual reporter-gene system to characterize genetic elements including, but not limited to the promoters and RBSs studied in this work. This study also suggested the feasibility of predicting and selecting candidate genetic elements based on omics datasets and bioinformatics tools. Moreover, the dual reporter-gene system developed in this study can be utilized to characterize other genetic elements of Z. mobilis, which can also be applied to other microorganisms.

Keywords

  • Zymomonas mobilis
  • Systems biology
  • Promoter
  • Ribosomal binding site (RBS)
  • Reporter genes
  • Dual reporter-gene system

Background

With the increasing consumption of fossil energy, significant efforts have focused on the development of sustainable alternative renewable energy needed to meet the energy demands for economic development and environmental protection. Correspondingly, biofuels produced through biochemical conversion from biomass-based feedstock by microorganisms have attracted significant attention. Z. mobilis is a natural ethanologenic bacterium with many desirable characteristics necessary to produce lignocellulosic biofuels and their intermediates through metabolic engineering, including ethanol and 2,3-butanediol [14].

To meet the needs of metabolic engineering and synthetic biology approaches, Z. mobilis genetic elements from coding regions (genes) and non-coding regions [e.g., promoters, ribosomal binding site (RBS), untranslated region (UTR), and terminators] have been broadly investigated [5]. Different from those in the coding region, genetic elements from the non-coding region can affect gene expression at the transcriptional or translational levels; as well as modulate their activity in response to environmental conditions [69]. Although some non-coding sequence elements of Z. mobilis were discovered [7, 10], there are no systematic and efficient approaches to identify and quantify elements that were already discovered. Many genetic elements remain undiscovered today.

With the development and deployment of technology, such as next-generation sequencing (NGS) and mass spectrometry, many systems biology studies were carried out with enormous omics data accumulated [1113], including studies of Z. mobilis [1327]. These systems biology datasets are vast, and thus contain information useful for deep mining and modeling [28, 29]. For example, genetic elements such as promoters can be sorted out by multiple omics data analysis [30]. However, promoters of Z. mobilis have not been systematically characterized, although the accurate information of genome and plasmid sequence; as well as genome annotation of Z. mobilis are available [14, 31, 32].

Traditionally, native promoters and RBSs are often discovered as a result of random genomic digestion, which is further facilitated by genome sequencing and annotation [6]. These genetic elements of different strengths are often required in metabolic engineering practices. For example, strong promoters were usually used to overexpress target genes to increase the titer of heterologous products [33]. Up to now, only a small number of strong promoters, such as Pgap, Ppdc, and Peno, have been investigated and used to construct metabolic pathways into wild-type strains suitable for efficient xylose utilization or enhanced inhibitor tolerance in Z. mobilis [3443].

The application of strong promoters on metabolic engineering could cause metabolic burden on cellular growth, and thus reduce the titer, yield, or productivity. To address this issue, inducible promoters, such as Ptet, were recently used to direct metabolic flux for balanced cellular growth and production [44]. However, the strengths of these promoters have not been investigated quantitatively, making it difficult to meet the needs of metabolic engineering and synthetic biology for genetic elements with different strengths [45]. Moreover, no RBS sequences have been specifically studied for Z. mobilis.

Some in vitro methods, such as electrophoresis mobility shift assay (EMSA) [46] and atomic force microscope (AFM) [47], have been developed to characterize promoter activity, but are not widely used due to their low accuracy in characterization of genetic elements quantitatively in vivo. The classical strategy to quantify the promoter activity in vivo is to measure the expression of downstream reporter genes driven by the candidate promoter [48]. Reporter genes, such as β-glucuronidase, β-galactosidase and fluorescent proteins, are commonly used for this purpose.

Fluorescent reporter proteins, which were established in 1996 [49] and developed later [50, 51], are preferred to be used with their unique advantages, which include being relatively small and nontoxic [52]. However, the expression of fluorescent reporter genes is affected by many internal and external factors, including mRNA degradation, translation, protein folding; as well as plasmid copy numbers [53], which then influence the promoter characterization in vivo [54]. These combined factors make it difficult to accurately measure the promoter activity in vivo, especially when using a single report gene in individual experiment without an intrinsic control [55, 56]. To reduce these influences, a dual reporter-gene system was developed and used for promoter characterization in different species, such as E. coli, Clostridium sp., and virus [55, 57, 58].

In this study, a dual reporter-gene system was developed which contains: (1) the opmCherry reporter gene driven by the constitutive PlacUV5 promoter for calibration, and (2) the EGFP reporter gene for quantifying the candidate genetic elements located upstream of EGFP. A series of promoters with different strengths were predicted based on previous systems biology data and four synthetic RBSs with different translation initiation rates were predicted and synthesized. These promoters and RBSs were then experimentally characterized using the dual reporter-gene system to explore the relationship between the prediction and experimental data.

Results

Establishment of the dual reporter-gene system

To develop a reliable dual reporter-gene system, fluorescent protein pairs should meet some requirements. Firstly, the pair should have distinguishable excitation and emission spectrum to avoid interplay between channels. Secondly, similar mature time rates are preferred, since different mature time rates will affect the simultaneous measurement. Thirdly, bright fluorescence intensity is preferred to help detect the signal intensity of relatively weak promoters. Based on these criteria, seven single fluorescent proteins, including EGFP, mCherry, RFP and CFP; as well as codon-optimized EGFP (opEGFP), mCherry (opmCherry), and CFP (opCFP), were synthesized and characterized under the control of a constitutive promoter PlacUV5 using the shuttle vector pEZ15Asp [44] (Fig. 1a). The emission and excitation wavelength of CFP were 433 nm and 475 nm, which was failed to detect by our detector and not included in this work. Flow cytometry results indicated that EGFP and opmCherry produced the brightest fluorescence signal among the seven fluorescent proteins tested (Fig. 1b).
Fig. 1
Fig. 1

The schematic illustration of the reporter-gene system including a constitutive promoter PlacUV5 to drive the reporter gene to be characterized and a terminator of rrnB gene (a), and the fluorescence intensity of selected fluorescent proteins of EGFP, opEGFP, mCherry, opmCherry and RFP driven by a strong promoter PlacUV5 (b). The emission/excitation wavelength (nm) of fluorescence protein: EGFP and opEGFP (488/507), mCherry and opmCherry (587/610), as well as RFP (555/584)

Since opmCherry (excitation maximum 587 nm, emission maximum 610 nm) and EGFP (excitation maximum 488 nm, emission maximum 507 nm) are spectrally distinguishable with good photostability [59, 60] and relatively comparable faster mature rates (40 min and 25 min, respectively) [59, 61] than that of RFP (100 min) [62], they were then selected to construct the dual reporter-gene system with opmCherry driven by the constitutive promoter PlavUV5 as the intrinsic control and EGFP under the control of the targeted genetic element, such as the candidate promoter or RBS (Fig. 2a).
Fig. 2
Fig. 2

The schematic illustration of the dual reporter-gene system based on the pEZ15A shutter vector, which includes the reporter gene opmCherry driven by the constitutive promoter PlavUV5 and terminated by rrnB T1 terminator as the intrinsic control, another reporter gene EGFP under the control of the targeted genetic element and terminated by rrnB T1 terminator, and a terminator BBa_B0014 inserted between these two reporter-gene expression cassettes (a); evaluation of the dual reporter-gene system under Ptet inducible system using different approaches of qPCR (b, c), flow cytometry (FCM, d, e) and Western blot (f), as well as the correlations of FCM results with qPCR and Western blot results, respectively (g). The concentrations of tetracycline are 0, 0.2, 0.4, 0.6, 0.8, or 1.0 μg/mL, respectively

To confirm whether the dual reporter-gene system works, we first tested the system using an inducible promoter Ptet to simulate the strengths of different promoters and determined the appropriate tetracycline inducer concentration. Our results indicated that the cellular growth decreased when tetracycline concentration was greater than 2 μg/mL, whereas the tetracycline concentration within 0–1 μg/mL did not affect cellular growth significantly (Additional file 2: Figure S1).

The correlation between the concentration of tetracycline inducer and the promoter strength was then investigated. The ratio of EGFP fluorescence intensity for tested promoter versus the opmCherry calibration fluorescence intensity was used to represent the promoter strength. qPCR, Western blot and flow cytometry analyses were carried out to evaluate the target gene expression at the transcriptional and translational levels. The results indicated that the expression of opmCherry is at a relatively constant level, whereas the expression of EGFP was enhanced correspondingly to the increase of the tetracycline inducer concentration within 0–1 μg/mL (Fig. 2b, d, f). In addition, the value of EGFP/opmCherry, a normalized ratio to eliminate the internal and external noises [55], has a linear correlation with the gradient tetracycline concentrations (Fig. 2c, e).

The correlations between the results of flow cytometry and other technologies, such as qPCR or Western blot, were also relatively high (Fig. 2g), which supports the possibility of utilizing the high-throughput quantitative approach of flow cytometry to quantify different candidate genetic elements.

Identification of candidate promoters with different strengths using omics datasets

Systems biology datasets from 109 microarrays, eight RNA-seq, and four proteomic experiments of previous studies were used to identify potential promoters with different strengths. The strength of upstream promoters was presented as the normalized average value of downstream gene expression in normal conditions of each omics dataset. To select promoters with different strengths, we sorted the normalized value and defined strength of promoters arbitrarily based on their quantile values: in each omics dataset, those sorted in the top 90% were defined as strong promoters, those sorted in the last 10% were defined as weak promoters, and those sorted within 40–60% were defined as medium ones.

Then, hierarchical cluster analysis cross different omics data was used to identify genes with consistent expression patterns (strong, medium, or weak), as shown in Venn diagram (Fig. 3). Six other weak promoters were also selected for a broad representation of weak promoters. Considering the existence of operon may affect the determination of the location for its upstream promoter, we used DOOR2 to predict the operons [63, 64]. A total of 38 candidate promoters with diverse strengths from strong to weak were then selected, which include 19 strong, nine medium, and ten weak ones (Table 1). In this study, the entire intergenic region of those genes was extracted as candidate promoters. These candidate promoters are listed in Table 1, and the information of promoter sequence and length are listed in supplementary materials (Additional file 1: Table S1).
Fig. 3
Fig. 3

Identification of promoter candidates with different strengths of strong (a), medium (b), or weak (c) using the omics data of array, RNA-Seq, and proteomics. The sorted normalized values were used to defined the strength of promoters arbitrarily based on their quantile values: in each omics dataset, those sorted in the top 90% were defined as strong promoters, those sorted in the last 10% were defined as weak promoters, and those sorted within 40–60% were defined as medium ones. For a broad representation of weak promoters, six other weak promoters closing to the selection threshold were also included for characterization

Table 1

Candidate promoters with different strengths selected based on omics datasets

Gene ID

Gene name

Operon

Gene function

Array

RNA-Seq

Proteomics

EGFP/opmCherry (log phase)

EGFP/opmCherry (sta phase)

Candidate promoters with strong strength

 ZMO0177

gap

 

Glyceraldehyde-3-phosphate dehydrogenase, type I

15.06

11.2

9.23

0.38 ± 0.0202

0.47 ± 0.0191

 ZMO1360

pdc

 

Thiamine pyrophosphate protein TPP binding domain-containing protein

14.52

11.78

8.59

0.24 ± 0.0220

0.22 ± 0.0086

 ZMO0516

Tuf

ND

Elongation factor Tu

15.33

11.58

8.08

0.24 ± 0.0162

0.30 ± 0.0148

 ZMO1608

eno

 

Phosphopyruvate hydratase

15.22

13.2

8.97

0.23 ± 0.0145

0.34 ± 0.0093

 ZMO0997

eda

Yes

2-Dehydro-3-deoxyphosphogluconate aldolase/4-hydroxy-2-oxoglutarate aldolase

14.97

14.6

8.46

0.18 ± 0.0178

0.15 ± 0.0167

 ZMO0367

zwf

 

Glucose-6-phosphate 1-dehydrogenase

14.92

11.19

7.38

0.16 ± 0.0057

0.14 ± 0.0124

 ZMO1719

frk

 

ROK family protein

15.06

12.2

6.85

0.12 ± 0.0038

0.17 ± 0.0098

 ZMO1609

  

Hypothetical protein

15.26

12.77

5.78

0.12 ± 0.0061

0.14 ± 0.0038

 ZMO0689

gfo

 

Oxidoreductase domain-containing protein

14.49

11.68

6.38

0.10 ± 0.0075

0.13 ± 0.0130

 ZMO1721

gloA3

ND

Glyoxalase/bleomycin resistance protein/dioxygenase

14.35

12.66

5.97

0.09 ± 0.0044

0.11 ± 0.0022

 ZMO0514

rpsG

Yes

30S ribosomal protein S7

15.31

10.79

5.54

0.07 ± 0.0062

0.07 ± 0.0015

 ZMO0515

 

Yes

Elongation factor G

15.07

11.14

5.83

0.07 ± 0.0062

0.07 ± 0.0015

 ZMO1596

adhB

 

Iron-containing alcohol dehydrogenase

15.32

10.98

7.07

0.07 ± 0.0084

0.07 ± 0.0030

 ZMO1141

ilvC

Yes

Ketol-acid reductoisomerase

15.41

12.39

6.76

0.05 ± 0.0012

0.04 ± 0.0006

 ZMO0241

atpD

Yes

F0F1 ATP synthase subunit beta

15.09

11.73

7.77

0.05 ± 0.0030

0.04 ± 0.0029

 ZMO0244

  

Histone family protein DNA-binding protein

14.79

12.98

6.1

0.04 ± 0.0013

0.04 ± 0.0007

 Po1721

      

0.04 ± 0.0020

0.03 ± 0.0007

 ZMO0493

glnA

Yes

Glutamine synthetase, type I

14.53

10.02

6.17

0.03 ± 0.0023

0.03 ± 0.0005

 ZMO1779

 

Yes

Antibiotic biosynthesis monooxygenase

15.08

11.24

7.52

0.02 ± 0.0005

0.02 ± 0.0008

Candidate promoters with medium strength

 ZMO1351

clcD1

Yes

Carboxymethylenebutenolidase

12.93

6.81

3.14

0.14 ± 0.0064

0.16 ± 0.0036

 ZMO0056

glmS

 

Glucosamine–fructose-6-phosphate aminotransferase

12.93

6.91

2.45

0.12 ± 0.0021

0.11 ± 0.0024

 ZMO0559

  

Hypothetical protein

12.68

6.75

3.1

0.11 ± 0.0064

0.08 ± 0.0039

 ZMO1385

  

Toxic anion resistance family protein

12.83

6.93

2.55

0.06 ± 0.0012

0.05 ± 0.0018

 ZMO0127

 

Yes

S1/P1 nuclease

12.84

7.11

3.13

0.05 ± 0.0015

0.05 ± 0.0014

 ZMO1100

 

Yes

Nucleotidyl transferase

12.58

7.18

2.82

0.05 ± 0.0012

0.05 ± 0.0023

 ZMO1392

  

Hypothetical protein

12.46

7.43

2.45

0.04 ± 0.0012

0.04 ± 0.0012

 ZMO0326

  

7-cyano-7-deazaguanine reductase

12.65

7.41

2.7

0.03 ± 0.0018

0.04 ± 0.0012

 ZMO0570

prmA

 

Ribosomal L11 methyltransferase

12.38

7.32

2.45

0.03 ± 0.0030

0.04 ± 0.0013

Candidate promoters with weak strength

 ZMO1231

recJ

 

Single-stranded-DNA-specific exonuclease RecJ

11.03

5.7

0.07

0.08 ± 0.0023

0.07 ± 0.0029

 ZMO1980

gidB

Yes

Methyltransferase GidB

10.59

5.27

0.07

0.05 ± 0.0025

0.05 ± 0.0029

 ZMO1484

  

UvrD/REP helicase

10.93

5.4

0.07

0.05 ± 0.0013

0.05 ± 0.0026

 ZMO0145

 

Yes

Peptidase M28

11.37

4.98

0.07

0.04 ± 0.0018

0.04 ± 0.0006

 ZMO0101

  

NAD-dependent epimerase/dehydratase

10.61

5.05

0.07

0.04 ± 0.0013

0.04 ± 0.0014

 ZMO1194

dprA

Yes

DNA protecting protein DprA

10.7

4.77

0.07

0.04 ± 0.0011

0.03 ± 0.0006

 ZMO1644

  

DEAD/DEAH box helicase domain-containing protein

10.61

4.98

0.07

0.03 ± 0.0013

0.03 ± 0.0007

 ZMO1582

  

Uracil-DNA glycosylase superfamily protein

10.33

4.63

0.07

0.03 ± 0.0004

0.03 ± 0.0003

 ZMO0005

cysD

Yes

Sulfate adenylyltransferase subunit 2

11.5

5.28

0.07

0.03 ± 0.0012

0.03 ± 0.0009

 ZMO0300

xseA

 

Exodeoxyribonuclease VII large subunit

11.6

4.71

0.07

0.03 ± 0.0010

0.03 ± 0.0013

The operon information is predicted by DOOR2 prediction server. The values of array, RNA-Seq and proteomics of each gene are the average log2-transformed values under different growth conditions

ND non-determined, Log phase exponential phase, Sta phase stationary phase

Characterization of candidate promoters quantitatively using flow cytometry

Selected candidate promoters were assembled into the upstream of EGFP reporter gene in the dual reporter-gene system using a modified Gibson assembly approach [65]. The relative strength of these promoters was quantified in exponential and stationary phase by flow cytometry (Table 1). Each sample was operated in triplicates with at least two independent experiments conducted by two researchers in the lab at different times.

The results showed that Pgap, Ppdc, and Peno have high EGFP/opmCherry ratios as determined by our dual reporter-gene system, which actually is consistent with our omics data-driven predictions and with previous studies that showed Pgap, Ppdc, Peno and PadhB are strong promoters in Z. mobilis [42, 43, 66]. The Pgap, encoding the glyceraldehyde-3-phosphate dehydrogenase (ZMO0177) in glycolysis pathway, is the strongest promotor in Z. mobilis characterized in this study (Table 1). Other promoters driving genes in glycolysis pathway also have high EGFP/opmCherry ratios (Table 1).

Our results demonstrated that different flow cytometry profiles were identified and can be classified into strong, medium, and weak categories (Fig. 4a). This result suggested that the flow cytometry-based dual reporter-gene system can clearly distinguish different strengths of promoters based on their profiles. In addition, the correlations between omics data prediction and EGFP/opmCherry ratios are relatively high, with an R2 more than 0.7 for RNA-Seq and proteomics datasets (Fig. 4b).
Fig. 4
Fig. 4

Flow cytometry (FCM) patterns of different promoters, in which the X- and Y-axis represents the fluorescent signal of EGFP and opmCherry, respectively. The background represents the fluorescence of the strain harboring an empty vector pEZ15Asp; EGFP and opmCherry represent the fluorescence of the strain harboring PlacUV5 driving single reporter gene, EGFP and opmCherry, respectively. The strong, medium and weak pattern is the pattern of representative promoters of ZMO0177, ZMO1351, and ZMO0300, respectively (a); and the correlation of experimental data of EGFP/opmCherry ratios in log phase with log2-based values of omics data of array, RNA-Seq and proteomics, respectively (b)

Quantification of predicted RBSs with different translation initiation rates

The dual reporter-gene system developed in this study was further explored to measure the RBSs with different translation initiation rates. The promoter used in this experiment was the inducible Ptet; however, the RBS sequence was replaced by the synthesized RBS sequences with different translation initiation rates predicted by RBS calculator V2.0 (https://salislab.net/software/), which is based on the 16S rRNA sequence of Z. mobilis. From weak to strong translation initiation rates, four synthetic RBSs (Table 2) were selected for quantification in Z. mobilis using the dual reporter-gene system (Fig. 2).
Table 2

The nucleotide sequences of synthetic RBS with different translation initiation rates based on the prediction using RBS calculator (https://salislab.net/software/forward)

Name

Strength

Predicted RBS sequence (5′–3′)

ZM4-Ptet-GFP-10

10

CCATAATCTAGAGAAAGTAAGCAC

ZM4-Ptet-GFP-1000

1000

AGGCTAAGAACTAACGGAGAGGTAAAT

ZM4-Ptet-GFP-10000

10,000

ATCACAGGGTCTAGAAGGAGGTCGAA

ZM4-Ptet-GFP-Max

15,000

GAGCGAGAAGGAGGTAAAGT

The results showed that within a fixed strength of translation initiation rate, the relative strength of RBS was enhanced with the increase of tetracycline inducer concentration gradient (Fig. 5a, Additional file 2: Figure S2). In addition, the predicted RBS strengths and tetracycline concentrations also had relatively high correlations with an R2 value of more than 0.93 (Fig. 5b). These results demonstrated that the dual reporter-gene system performed well in characterizing the strength of RBS, which suggests that the dual reporter-gene system can be used to characterize the non-coding sequence elements of RBSs.
Fig. 5
Fig. 5

The quantification of synthetic RBS strengths by dual reporter-gene system under the induction of different concentrations of tetracycline (Tc). The relative strength of RBS is a calibrated strength that the value of EGFP/opmCherry with Tc induced minus that without Tc induction (a), and the correlation between tetracycline concentrations and RBS strengths of EGFP/opmCherry ratios for each predicted RBS with different strengths of 10, 1000, 10,000 and 15,000, respectively (b). The concentrations of tetracycline (Tc) used were 0, 0.2, 0.4, 0.6, 0.8, or 1.0 μg/mL, respectively

Discussions

In this study, a flow cytometry-based dual reporter-gene system was developed (Fig. 1), which was confirmed with an inducible promoter Ptet using different approaches of qPCR, flow cytometry, and Western blot (Fig. 2), and then applied to quantify biological parts such as promoters and RBS predicted by bioinformatics approaches with good correlations (Figs. 3, 4, 5). The high correlation between different concentrations of the tetracycline inducer and EGFP/opmCherry ratios suggested the capability of this dual reporter-gene system to characterize the strengths of different genetic elements, and the relatively high correlations between predicted candidates and experimental results for promoters and RBSs also indicate that it is practicable to predict the strengths of genetic elements based on omics datasets and bioinformatics tools, and therefore can be used to guide the selection of promoters with different strengths at the transcriptional level in other microbial systems.

Additionally, our result indicated that the codon-optimized EGFP actually had a decreased fluorescence, while the codon-optimized mCherry had enhanced fluorescence intensity (Fig. 1b). This system can, therefore, be applied to select and test reporter genes with desired fluorescence through protein engineering approaches such as codon optimization, rational design, and directed evolution.

Furthermore, this approach is straightforward with high reproducibility, and the fluorescence intensity of both EGFP and opmCherry as well as the corresponding correlation between results from different researchers in our group at different operation times is high with an R2 value of 0.92 (Additional file 2: Figure S3). However, future work is still needed to further develop this dual reporter-gene system for broad applications such as the inclusion of short-life reporter genes and advanced imaging techniques to monitor the dynamic expression changes of diverse genetic elements including promoters and RBSs investigated in this work as well as other genetic elements to be explored such as UTRs, terminators, and sRNA.

Conclusion

In this study, we conducted the systems biology data mining and bioinformatics analyses to predict the strengths of genetic elements, such as promoters and RBSs. We also developed a flow cytometry-based dual reporter-gene platform for Z. mobilis to measure the strengths of selected promoters and synthesized RBSs. We found a relatively high correlation between prediction and experimental results for the strengths of promoters and RBSs. The feasibility of identifying and characterizing predicted genetic elements, such as promoters and RBSs, demonstrated in this study suggests that other genetic elements from Z. mobilis, such as 5′UTRs and terminators, could also be quantitatively characterized by similar approaches. Furthermore, the high correlation between prediction and experimental results indicates that a similar strategy could be applied to other microorganisms leading to the identification of genetic elements using systems biology data and bioinformatics tools. Moreover, the seven native and synthesized fluorescent proteins and genetic elements including 38 promoters and four RBSs characterized in this study can be used for future metabolic engineering and synthetic biology practices in Z. mobilis.

Methods

Strains, vectors, and media

Escherichia coli XL10-Gold from Invitrogen (USA), was cultured in Lysogeny broth (LB, 10 g/L NaCl, 10 g/L tryptone, 5 g/L yeast extract). Medium was prepared according to the description in the Manual of Molecular Cloning [67]. Wild type Z. mobilis ZM4 was revived from frozen glycerol stocks in rich media with 5% glucose (RMG5: 50 g/L glucose, 10 g/L yeast extract, and 2 g/L KH2PO4) at 30 °C for 6–8 h without shaking. Shuttle vector pEZ15Asp includes origins of replication from both E. coli and Z. mobilis [44]. Strains and plasmids used in this study are listed in Table 3.
Table 3

Strains and plasmids used in this study

Strains and plasmids

Description

Source

Strains

 E. coli XL10-Gold

Ultracompetent cells (tetracycline and chloramphenicol resistant)

Invitrogen

 Z. mobilis ZM4

Z. mobilis wild-type strain

Lab stock

Plasmids

 pEZ15Asp

P15A_ori, Zymo_Ori, Ptet, SpeR

[44]

 pEZ15A-PlacUV5-EGFP

P15A_ori, Zymo_Ori, PlacUV5::EGFP, SpeR

This work

 pEZ15A-PlacUV5-opmCherry

P15A_ori, Zymo_Ori, PlacUV5::opmCherry, SpeR

This work

 pEZ-Dual

P15A_ori, Zymo_Ori, Ptet, SpeR, PlacUV5::opmCherry, Ptet::EGFP

This work

Selection of candidate promoters

Public and in-house transcriptomic datasets from RNA-seq and microarray studies, as well as proteomic datasets were retrieved, collected, and hierarchically clustered separately using the JMP Genomics software 6.0 (SAS Inc., NC) with the conventional processing flows using default parameters [15, 16, 21, 23]. The common promoters showing strong, medium, or weak expression levels or abundance based on gene expression intensity or protein peptide hits abundance of downstream genes or proteins were selected using the Venn diagram generator at Venny 2.0.2 program (http://bioinfogp.cnb.csic.es/tools/venny/).

In this study, promoters were loosely defined as the region between the gene of interest and the preceding open reading frame (ORF). If genes were found to be transcribed as part of an operon, the operon promoter was selected. Genome-wide operon prediction in Z. mobilis is available at DOOR2 website server (http://csbl.bmb.uga.edu/DOOR/).

Plasmid construction of candidate genetic elements

The vector pEZ15Asp containing an origin of replication with promoters for E. coli as well as Z. mobilis [44] was modified to construct the candidate genetic elements into the dual reporter-gene system. The reporter gene opmCherry driven by a constitutive promoter LacUV5 was assembled to the suffix sequences of Z. mobilis replication promoter [68]. The reporter gene EGFP driven by tetracycline inducible promoter Ptet was assembled to the prefix sequences of E. coli replication promoter.

For construction of candidate promoters into the dual reporter-gene system, the Ptet was replaced by candidate promoters individually. All candidate promoters were PCR amplified from Z. mobilis genomic DNA using primer sets listed in Additional file 1: Table S2. Each primer contains about 15–20 nucleotides overlapping region of vector without Ptet promoter. Assembly was operated as described below. Recombinant vectors were selected by colony PCR with primer pairs of Pdual-F and Pdual-R. The schematic diagram of dual-reporter system is shown in Fig. 2a. To construct RBSs into the dual reporter-gene system, the candidate RBSs replace the original RBS in the Ptet dual reporter-gene system by same operation as described above.

For plasmid construction, the protocol used was based on Gibson et al. [65]. Briefly, primers were designed to contain 15–20 nucleotides overlapping regions with adjacent DNA fragments. PCR products amplified by primer pairs were separated by gel electrophoresis, followed by gel purification, and subsequently quantified using NanoDrop 2000 (Thermo Fisher Scientific, USA). Fragments and vector were allocated in a molar ratio of 3:1, 0.5 U T5 exonuclease (NEB, USA), 0.5 μL buffer 4 (NEB, USA), and the final volume was set to 5 μL with ddH2O.

All regents were mixed and reacted on the ice for 5 min; E. coli chemical competent cells were subsequently added. After incubation on the ice for 30 min, the mixture above was heat-shocked for 45 s at 42 °C, and then held on the ice for 2 min. Subsequently, 100 μL NZY was added into the mixture and incubated at least 1 h at 37 °C with shaking (250 rpm).

Cells were plated on LB agar plates containing spectinomycin, and recombinants were selected by colony PCR and confirmed by Sanger sequencing (Tsingke, China). The correct recombinant plasmids were transformed into Z. mobilis ZM4 competent cells, which were prepared as described previously [44], via electroporation (0.1-cm electrode gap, 1600 V, 200 Ω, 25 μF) using a Gene Pulser® (Bio-Rad, USA). Colonies with correct PCR product sizes were selected as candidate strains.

Flow cytometry analysis

The protocol used for flow cytometry analysis of the promoter strength in terms of fluorescence intensity was modified slightly from a previous study [69]. Briefly, cells were washed with phosphate-buffered saline (PBS) twice and then resuspended into PBS to a concentration of 107 cells/mL. Cells were analyzed by flow cytometry using Beckman CytoFLEX FCM (Beckman Coulter, USA) with the PBS as the sheath fluid. The fluorescence of EGFP was excited with the 488 nm and detected with FITC; opmCherry was excited with the 561 nm and detected with PC5.5 [59, 70, 71].

As recommended by the manufacturer, compensation was applied to ensure that the EGFP has minimal affection on the detection of opmCherry. To avoid rare events which could affect the population distribution, at least 20,000 events of each sample were analyzed. Data were processed via FlowJo software (FlowJo, LLC, USA) based on the user manual with the recommended parameters. The mean fluorescence intensity of triplicates was calculated; then the ratio of average EGFP/average opmCherry was used to quantify the strength of each promoter. In addition, the standard deviation (STDEV) was set as the error bar.

Quantitative real-time PCR analysis

The transcription levels of EGFP and opmCherry in the dual reporter-gene system were estimated at the same thermal cycling conditions. The cell extract samples were taken at exponential phase and used for quantitative real-time PCR (qPCR) analysis. Total RNA from Z. mobilis ZM4 strains harboring the inducible dual reporter-gene system was extracted using TRIzol reagent (Invitrogen, USA), and RNA quality was examined by NanoDrop 8000 (Thermo-Fisher, USA). The residual DNA removal and the reverse transcription was performed using the iScript™ gDNA Clear cDNA Synthesis Kits according to the manufacturer’s instructions (Bio-Rad, USA).

The qPCR reactions were carried out using iTaq™ Universal SYBR® Green Supermix (Bio-Rad, USA) on a CFX96 Real-Time System (Bio-Rad, USA) as described previously [13, 15, 21, 23]. Briefly, highly purified salt-free primer pairs for EGFP and opmCherry were synthesized (Genscript, China) with an similar annealing temperature of 60 °C (Additional file 1: Table S1). PCR product specificity was confirmed through melting curve analysis. The following run protocol was used: 95 °C 5 min for denaturation; (95 °C 15 s, 60 °C 10 s, and 72 °C 30 s) 40 times for amplification and quantification with a single florescence measurement. A melting curve program (60–95 °C with heating rate of 0.1 °C per second and a continuous fluorescence measurement) was used to confirm the specificity of the primer pairs. An absolute quantification based on an internal calibration curve was applied for qPCR data analysis.

Western blot analysis

Log phase and stationary phase cells were harvested to conduct the flow cytometry analysis. Cells were lysed and protein was extracted using Protein Extraction Kit (Zomanbio, China). Total protein concentrations of total cellular lysates were measured by the Bradford method with 200 ng of total protein loaded for each sample. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) was performed with a 5% stacking and a 12% running gel, followed by stained with Coomassie Brilliant Blue R-250. Molecular weight was estimated using a pre-stained protein ladder (10–170 kDa, Thermo, Lithuania) [72].

For Western blot analysis, after the electrophoresis, gels were transferred to methanol-activated PVDF membranes using the Trans-Blot® Semi-Dry Electrophoretic Transfer Cell (Bio-Rad, USA) and run for 20 min at 25 V. PVDF membranes was then blocked with 5% non-fat milk in phosphate-buffered saline with Tween 20 (PBST) for 1 h at room temperature, and subsequently EGFP or opmCherry were probed with primary antibody (1:5000, Proteintech, China), respectively. Peroxidase-conjugated goat anti-Mouse IgG (1:5000, Proteintech, China) was used as secondary antibodies. Color development was performed by West Dure Extended Duration Substrate Kit (AntGene, China). All images were visualized using AI600 Imaging System (GE, USA), and analyzed by ImageJ (National Institutes of Health, USA).

Notes

Abbreviations

AFM: 

atomic force microscope

CFP: 

cyan fluorescent protein

EGFP: 

enhanced green fluorescent protein

EMSA: 

electrophoresis mobility shift assay

FCM: 

flow cytometry

NGS: 

next-generation sequencing

opCFP: 

codon-optimized CFP

opEGFP: 

codon-optimized EGFP

opmCherry: 

codon-optimized mCherry

PBS: 

phosphate-buffered saline

PBST: 

phosphate-buffered saline with Tween 20

qPCR: 

quantitative real-time PCR

RBS: 

ribosomal binding sites

RFP: 

red fluorescent protein

SDS-PAGE: 

sodium dodecyl sulphate polyacrylamide gel electrophoresis

UTR: 

untranslated region

Declarations

Authors’ contributions

SY conceived and designed the experiments with inputs from all authors. YY and WS constructed the dual reporter-gene vector and plasmids, conducted qPCR, Western blot, and FACS experiments with help from JH, RL, and YX. YY and SY wrote the manuscript with help from WS, and MEH and HW conducted extensive review. All authors contributed to data analyses. All authors revised the final manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank Drs. Mingxiong He and Bo Wu from Biogas Institute of Ministry of Agriculture and Rural Affairs for helpful discussion. We also acknowledge Mei Meng for her support with flow cytometry technology and insightful discussions.

HW, YCH, MZ and MEH were supported by the funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office (BETO). This work was authored in part by Alliance for Sustainable Energy, LLC, the Manager and Operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets used for this study are available from NCBI GEO and SRA databases based on previous publication.

Consent for publication

All authors read and approved the final manuscript.

Ethics approval and consent to participate

The authors declare that this study does not involve human subjects, human material and human data.

Funding

This work was funded by State Key Laboratory of Biocatalysis and Enzyme Engineering, and Technical Innovation Special Fund of Hubei Province, China (2018ACA149).

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Environmental Microbial Technology Center of Hubei Province, and School of Life Sciences, Hubei University, Wuhan, 430062, China
(2)
Biosciences Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
(3)
National Bioenergy Center, National Renewable Energy Laboratory, Golden, CO 80401, USA

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