This work presents a method of high-throughput screening using a universal genetic enzyme screening system that can be theoretically applied to over 200 enzymes. Here, the single screening system identifies three different enzymes (lipase, cellulase, and alkaline phosphatase) by simply changing the substrate used (p-nitrophenyl acetate, p-nitrophenyl-β-D-cellobioside, and phenyl phosphate).
The recent development of a high-throughput single-cell assay technique enables the screening of novel enzymes based on functional activities from a large-scale metagenomic library1. We previously proposed a genetic enzyme screening system (GESS) that uses dimethylphenol regulator activated by phenol or p-nitrophenol. Since a vast amount of natural enzymatic reactions produce these phenolic compounds from phenol deriving substrates, this single genetic screening system can be theoretically applied to screen over 200 different enzymes in the BRENDA database. Despite the general applicability of GESS, applying the screening process requires a specific procedure to reach the maximum flow cytometry signals. Here, we detail the developed screening process, which includes metagenome preprocessing with GESS and the operation of a flow cytometry sorter. Three different phenolic substrates (p-nitrophenyl acetate, p-nitrophenyl-β-D-cellobioside, and phenyl phosphate) with GESS were used to screen and to identify three different enzymes (lipase, cellulase, and alkaline phosphatase), respectively. The selected metagenomic enzyme activities were confirmed only with the flow cytometry but DNA sequencing and diverse in vitro analysis can be used for further gene identification.
A recently developed high-throughput single-cell assay technique allows novel enzymes to be screened from a large-scale genetic library based on their functional activities1. At the single cell level, proteins regulating transcription are employed to trigger reporter gene expression by sensing small molecules that are produced as a result of a target enzyme activity. One early approach involved the isolation of a phenol-degrading operon from Ralstonia eutropha E2 using the substrate-induced genetic expression screening (SIGEX) method, in which the substrate induces the expression of a reporter protein2. NhaR of Pseudomonas putida was used to select benzaldehyde dehydrogenase3, and LysG from Corynebacterium glutamicum was utilized for the high-throughput screening of a new L-lysine-producing strain from diverse mutant libraries4.
Previously, a genetic enzyme screening system (GESS) was proposed as a generally applicable screening platform5. This system uses the phenol-recognizing dimethylphenol regulator, DmpR, of P. putida. DmpR(E135K), and a mutant of DmpR, can also be employed in GESS (pNP-GESS) for the detection of p-nitrophenol (pNP). In the presence of target enzymes producing phenolic compounds, GESS in E. coli cells emits a fluorescence signal, enabling the rapid isolation of single cells using a fluorescence-activated cell sorter (FACS). But the expression of metagenomic enzyme appears to be weaker than that of conventional recombinant enzymes; therefore, GESS was designed to detect phenolic compounds with maximum sensitivity by investigating the combination of ribosomal binding site (RBS) and terminator sequences along with optimal operating condition5.
One of the fundamental advantages of GESS is that this single method theoretically allows the screening of over than 200 different types of enzymes in the BRENDA database (Table 1, http:// www.brenda-enzymes.info, 2013.7) by simply employing different substrates. It was shown that cellulase, lipase, and methyl parathion hydrolase (MPH) can be detected using pNP-GESS with appropriate substrates of p-nitrophenyl butyrate, p-nitrophenyl-cellotrioside, and methyl parathion, respectively5. Recently, it was proved that an alkaline phosphatase (AP), which is one of the novel enzymes identified using pNP-GESS, is the first thermolabile AP found in cold-adapted marine metagenomes6.
Here, details of the screening process is presented with pNP-GESS detecting the activities of three different types of enzymes- lipase, cellulase, and alkaline phosphatase -and rapidly identifying novel candidate enzymes from a metagenomic library5,6. The processes include metagenome preprocessing with pNP-GESS and operating a flow cytometry sorter. While the hits obtained will need to be sequenced for further identification, this protocol covers the procedure up to the steps of enzyme activity confirmation using flow cytometry.
1. Preparing the Metagenomic Library with pNP-GESS
2. Removing False Positives from the Metagenomic Library
3. Metagenomic Enzyme Screening
4. Hit Selection and Enzyme Activity Confirmation
The three phenolic substrates were examined to identify novel metagenomic enzymes from a metagenome library of ocean-tidal flat sediments in Taean, South Korea by following the proposed protocol. For the library construction, average 30 – 40 kb metagenome sequences were inserted into fosmids, which are based on the E. coli F factor replicon and presented as a single copy in a cell. Note that fosmids have been widely used for constructing complex genomic libraries due to their stable propagation8.
Figure 1 shows a brief flowchart of the screening protocol including the removal of false positives with negative sorting (Figure 1 A) followed by positive hit selection with R1 and R2 sorting gates (Figure 1 B). The hits were evaluated by comparing the fluorescence of the samples with and without substrates (Figure 1 C). In case of p-nitrophenyl acetate mediated screening, five candidates of lipase were confirmed where the fluorescence of substrate treated cells were higher than that of control cells (Figure 2 A). In spite of the broad distributions of the three candidates of cellulase in Figure 2 B, they also showed clear differences of average FITC intensity of the populations. Four phosphatase candidates also showed high fluorescence level compared to the control cells (Figure 2 C). The more fluorescence difference between negative and the candidate cells the stronger enzyme activity can be deduced since the fluorescence reporter of pNP-GESS shows quantitative responses to the phenol concentrations5.
In this protocol, only the fluorescence differences were confirmed using a flow cytometry but various identification assays can be applied as mentioned in Step 4.2. Figure 3 demonstrates the example of AP identification6. A selected clone among forty-seven highly fluorescent colonies from the screening was sequenced and an ORF of the candidate AP was identified. The ORF inserted into a plasmid vector was confirmed to have AP activity with flow cytometry. In Figure 3, the hit shows stronger fluorescence signal than the cells carrying empty plasmid (negative). Further analysis of sequence and in vitro enzymatic activity discovered that this AP was highly similar to other known APs in its enzymatic characteristics except high catalytic activity at lower temperatures (35 °C) and a remarkable thermal instability (65 °C) within 15 min6. In addition, the screened AP was comparable to commercially available APs in removing terminal phosphates from cohesive and blunt ends of linearized plasmid DNA6 .
Figure 1: Multi Enzyme Screening Process. (A) Removal of false-positive cells showing fluorescence without substrate. The sorting gate is located in the middle of the cell population density so that the gate includes 10% of the total cells. (B) Sorting high fluorescent (FITC-A) cells (positive) satisfying R1 and R2 gates in the presence of an appropriate substrate. (C) After the selection and single cell isolation, the activity of the candidate enzymes was examined with and without the substrate (denoted as W/ and W/O substrates, respectively). The clear signal difference between the two samples implies the fosmid vector of the cells contains target genetic information of interest. Please click here to view a larger version of this figure.
Figure 2: Enzyme Activity Evaluation Using Flow Cytometry. Metagenomic enzyme candidates screened by pNP-GESS with three different substrates (A) p-nitrophenyl acetate (pNPA), (B) p-nitrophenyl-β-D-cellobioside (pNPG2), and (C) phenyl phosphate. All the candidates show clear signal difference between cells with and without substrate. Controls are the cells without their corresponding substrate treatment. Please click here to view a larger version of this figure.
Figure 3: Alkaline Phosphatase Identification. Left panel shows clear fluorescence difference between the cells containing the enzyme hit and an empty plasmid vector (Negative). On the right, phosphatase assay of cells harboring the phosphatase candidate gene in a plasmid vector on LB agar containing 5-bromo-4-chloro-3-indolyl phosphate as a colorimetric substrate is shown. Blue colonies indicate that the selected cells contain the metagenomic gene encoding a phosphatase. See 6 for more details. Please click here to view a larger version of this figure.
EC Number | Description | No. of p-nitrophenol producing enzymes | No. of phenol producing enzymes | |
EC 1 | 1.3 | Acting on the CH-CH group of donors | – | 1 |
Oxidoreductases | 1.11 | Peroxygenase | 2 | – |
1.14 | Acting on paired donors, with incorporation or | – | 2 | |
reduction of molecular oxygen | ||||
EC 2 | 2.3 | Acyltransferases | 1 | – |
Transferases | 2.4 | Glycosyltransferases | 8 | – |
2.5 | Transferring alkyl or aryl groups, other than methyl groups | 1 | 1 | |
2.7 | Transferring phosphorus-containing groups | 1 | 1 | |
2.8 | Transferring sulfur-containing groups | 3 | 1 | |
EC 3 | 3.1 | Act on ester bonds | 67 | 16 |
Hydrolases | 3.2 | Glycosylases | 75 | 21 |
3.4 | Act on peptide bonds – peptidase | 26 | 4 | |
3.5 | Act on carbon-nitrogen bonds, other than peptide bonds | 5 | 3 | |
3.6 | Act on acid anhydrides | 4 | – | |
3.7 | Act on carbon-carbon bonds | 2 | – | |
EC 4 | 4.1 | Carbon-carbon lyases | – | 3 |
Lyases | 4.2 | Carbon-oxygen lyases | 1 | – |
4.3 | Carbon-nitrogen lyases | 1 | – | |
Subtotal number of enzymes | 197 | 53 | ||
Total number of enzymes (excluding overlapped enzymes) | 211 |
Table 1: The Number of Enzymes that Produce p-nitrophenol or Phenol from the BRENDA Database.
Increasing production efficiency of biocatalysts is a key for the success of bio-chemical based industry9 and metagenome is considered one of the best natural enzyme source. In this sense, it is essential to screening novel enzymes from the metagenome where majority of the genetic resources have not been explored10. Several screening methods have been developed which directly detect enzyme products using transcriptional activators11, 12 but these techniques require specific metabolite-responsive transcriptional system. On the other hand, GESS is broadly applicable depending on the use of phenol or pNP tagged substrates. Although most of the reported enzymes producing phenol or pNP compounds in BRENDA database (Table 1), (including the three examples in this work) belong to the hydrolase (EC number 3) class enzymes, exploring more diverse phenolic substrates and even designing an artificial substrate subjecting to target enzyme activity, as alternatives of natural substrates, could significantly extend the applicability of the screening system. Note that choosing an appropriate phenol or pNP tagged substrate is one of the crucial steps of pNP-GESS application since the membrane permeability and cellular effect of the substrate considerably influence the screening protocol. Here, we show that industrially important three different types of enzymes can be identified in a high-throughput manner using the single screening system.
The approach of using FACS, in addition to the high-throughput screening of metagenomic enzymes, can be also applied to engineering transcriptional regulators (TRs) for altered TR specificity. One example is a study in which an AraC variant that detected mevalonate instead of arabinose was isolated using FACS, and then employed to screen cells producing mevalonate13. Also, the alteration of the specificity of TRs has allowed for further engineering of enzyme activity. Auto-regulated TR, PcaU, was engineered to detect 3,4-dihydroxybenzoate (34DHB) and then applied to screen for dehydroshikimate dehydratase enzymes (AsbF) at their maximum activity of converting endogenous dehydroshikimate to 34DHB14. The basic concept of these studies is similar to that of pNP-GESS, since pNP-GESS uses a DmpR variant to detect pNP and shows high-throughput performance in screening from a large-scale genetic library. However, specificity engineering may cause unintended false positives among the hits, as it is possible to trigger reporter expression by other inducers as a result of broadened specificity. Therefore, it would be desirable to investigate the orthogonality of TR response before the TR is applied. On the other hand, increasing the sensitivity and dynamic range of the TR could compensate for false positive generation. For example, the sensitivity of GESS was improved by inserting optimized RBS and terminator sequences into the system. False positives of the screening system possibly come from malfunction of the genetic circuit, unintended phenolic chemicals activating the TR, heterogeneous cell growth, and free diffusion of phenol or pNP. The negative selection processes of Steps 2.1 – 2.9 were expected to remove false positive cells releasing fluorescence without substrate treatment. These false positive cells can be also reduced by maximizing the signal to noise ratio of the genetic circuit. Along with the RBS and terminator engineering, the use of M9-glucose media significantly improved the signal (7-fold higher than LB media) in pNP-GESS performance5. But, in this protocol, LB was used as a compromise between the unknown metagenomic gene expression and GESS signal intensity. Investigating more delicate conditions of pNP-GESS cell growth and substrate effect in M9-glucose enables to improve the signal intensity. Tight regulation of reaction time of the substrates, in Step 3.5, is also important to prevent false positives triggered by phenol or pNP diffusion.
GESS enables the conversion of enzyme function into transcription-mediated reporter protein expression at a single-cell level but the indirect identification via reporter protein could be one of the innate limitations. So, in vitro enzyme assay and LC/GC-MS analysis of a product need to be followed for definite identification of a putative enzyme candidate6,7. Also it is possible to use GESS without the need for expensive FACS analysis by attaching alternative reporters such as antibiotic resistance genes or chromogenic enzymes with fluorescent reporters. For further improvement of GESS applications, it is necessary to establish a method to characterize enzyme activity and identify the enzymes in a high-throughput manner. Along with other high-throughput technologies, such as next-generation sequencing, GESS will be a widely used strategy for protein and enzyme catalyst engineering in the biology-based industry.
The authors have nothing to disclose.
This research was supported by grants from the Intelligent Synthetic Biology Center of Global Frontier Project (2011-0031944), the Next-Generation Biogreen 21 Program (PJ009524), NRF-2015M3D3A1A01064875 and the KRIBB Research Initiative Program.
CopyControl | Epicentre | CCFOS110 | Fosmid library production kit |
CopyControl Induction Solution | Epicentre | CCIS125 | Fosmid copy induction solution |
EPI300 | Epicentre | EC300110 | Electrocompetent cell |
pCC1FOS | Epicentre | CCFOS110 | Fosmid vector |
Gene Pulser Mxcell | Bio-Rad | Electroporation cuvette and electroporate system | |
FACSAria III | Becton Dickinson | Flow Cytometry (FACS machine) | |
AZ100M | Nikon | Microscope | |
UltraSlim | Maestrogen | LED illuminator | |
50-mL conical tube | BD Falcon | ||
14-mL round-bottom tube | BD Falcon | ||
5-mL round-bottom tube | BD Falcon | ||
p-nitrophenyl phosphate | Sigma-Aldrich | N7653 | Substrate |
p-nitrophenyl β-D-cellobioside | Sigma-Aldrich | N5759 | Substrate |
p-nitrophenyl butylate | Sigma-Aldrich | N9876 | Substrate |
Luria- Bertani (LB) | BD Difco | 244620 | Tryptone 10g/L, Yeast extract 5g/L, Sodium Chloride 10g/L |
Super Optimal broth (SOB) | BD Difco | 244310 | Tryptone 20g/L, Yeast extract 5g/L, Sodium Chloride 0.5g/L, Magnesium Sulfate 2.4g/L, Potassium Chloride 186mg/L |
Super Optimal broth with Catabolite repression (SOC) | SOB, 0.4 % glucose | ||
2x Yeast Extract Tryptone (2xYT) | BD Difco | 244020 | Pancreatic digest of Casein 16g/L, Yeast extract 10g/L, Yeast extract 5g/L |
Cell storage media | 2xYT broth, 15 % Glycerol, 2 % Glucose | ||
pGESS(E135K) | A DNA vector containing dmpR, egfp genes with their appropriate promoters, RBS, and terminator. See the reference 5 in the manuscript for more details. |
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Chloramphenicol | Sigma | C0378 | |
Ampicillin | Sigma | A9518 | |
BD FACSDiva | Becton Dickinson | Flow Cytometry Software Version 7.0 | |
PBS | Gibco | 70011-044 | 0.8% NaCl, 0.02% KCl, 0.0144% Na2HPO4, 0.024% KH2OP4, pH 7.4 |