1. Project Objectives and Significance of the Study
This project aims to construct an engineered Escherichia coli expression system capable of synthesizing citronellal through the introduction of a multi-enzyme cascade pathway. By employing microbial fermentation as an alternative to traditional plant extraction, citronellal can be produced in a stable and cost-effective manner, reducing dependence on agricultural resources and contributing to environmental conservation.
The significance of this project lies in establishing a safe and effective source of natural mosquito repellents, thereby reducing reliance on chemical insecticides, lowering the risk of vector-borne disease transmission, and addressing public demand for natural health products. Furthermore, this biosynthetic strategy may be extended to the production of other plant-derived natural compounds, promoting the advancement of the bio-manufacturing industry.
UN Sustainable Development Goals Alignment:
- Good Health and Well-being (SDG3)
- Responsible Consumption and Production (SDG12)
2. Project Design
2.1 Selection of Synthetic Pathway
We designed a three-enzyme biosynthetic pathway that enables E. coli to synthesize citronellal from scratch:
1. Geranyl diphosphate synthase (GPS)
Generates geranyl diphosphate (GPP), the key precursor of citronellal.
2. CsTPS1 (citral synthase)
A terpene synthase putatively derived from Cymbopogon species that catalyzes the conversion of GPP to citronellol (3,7-dimethyl-6-octen-1-ol, a monoterpene alcohol).
3. Geraniol dehydrogenase (GeDH)
Oxidizes citronellol into the target product, citronellal.
Through this sequential enzymatic cascade, the engineered strain channels host metabolic flux from the isoprenoid pathway into citronellal biosynthesis. This pathway design leverages a natural biosynthetic route, ensuring both efficiency and specificity in citronellal production.
2.2 Strain Construction and Verification
To achieve multi-enzyme co-expression, seamless cloning was employed to assemble the three enzyme genes into compatible plasmid vectors, which were subsequently introduced into E. coli competent cells.
2.2.1 Cloning Strategy
| Enzyme | Plasmid Vector | Resistance Marker |
|---|---|---|
| GPS | pET28a | Kanamycin |
| CsTPS1 | pET21a | Ampicillin |
| GeDH | pBAD23 | Chloramphenicol |
Although initial transformations did not yield positive clones, subsequent optimization of experimental procedures resulted in successful construction of the engineered strain.
2.2.2 Verification Process
- Colony PCR using gene-specific primers
- Gel electrophoresis confirmation of expected amplicon sizes
- DNA sequencing validation of complete insert accuracy
- Glycerol stock preservation at –80°C for long-term use
Growth assays comparing engineered strains with wild-type and empty-vector controls revealed no significant differences in growth rates under antibiotic selection, indicating minimal metabolic burden and suitability for high-density fermentation.
2.3 Expression Regulation Strategy
To coordinate enzyme expression, we adopted a dual-induction system:
T7 Promoter System
- GPS and CsTPS1 expressed under T7 promoter
- Induced with IPTG in E. coli BL21(DE3)
pBAD Promoter System
- GeDH expressed from pBAD23
- L-arabinose-inducible pBAD promoter
This design enabled simultaneous induction of all three enzymes with tunable expression levels by adjusting IPTG and L-arabinose concentrations. Further optimization of induction timing and culture temperature was applied to maximize protein expression and folding efficiency.
3. Product Detection and Optimization
3.1 Initial Analysis
Following the construction of the engineered strain, we conducted both qualitative and quantitative analyses of citronellal production. High-performance liquid chromatography (HPLC) was employed to detect fermentation products using an authentic citronellal standard calibration curve.
3.1.1 Initial Results
- Initial citronellal titer: ~0.89 g/L
- Comparable concentrations in culture supernatant and cell lysate
- Cross-validated by UV absorbance measurements
4. Response Surface Methodology (RSM) Optimization
Response Surface Methodology was applied to optimize key fermentation and induction parameters using a Box–Behnken experimental design.
4.1 Optimization Variables
| Variable | Description | Levels Tested |
|---|---|---|
| A | Induction temperature | 3 levels |
| B | IPTG concentration | 3 levels |
| C | L-arabinose concentration | 3 levels |
4.2 Optimized Conditions
- Temperature: 20°C
- IPTG: 0.46 mM
- L-arabinose: 0.2%
4.3 Optimization Results
- Improved citronellal titer: 1.03 g/L
- Yield improvement: ~14% increase over pre-optimization levels
5. Construction of Mutant Proteins
5.1 Enzyme Engineering Strategy
The terminal step of the biosynthetic pathway—the oxidation of geraniol to citronellal, catalyzed by geraniol dehydrogenase (GeDH)—was identified as rate-limiting. An enzyme engineering strategy was applied to improve the catalytic efficiency of GeDH.
5.1.1 Site-Directed Mutagenesis
Guided by in silico simulations, four key residues were selected for modification:
| Position | Original | Mutation | Expected Effect |
|---|---|---|---|
| 60 | Q | R | Strengthen substrate interaction and/or accelerate catalytic turnover |
| 125 | L | P | |
| 147 | F | Y | |
| 300 | A | D |
5.1.2 Mutant Performance
The recombinant strain harboring the mutant GeDH demonstrated marked improvement in citronellal production under optimized fermentation conditions.
Maximum citronellal concentration: 1.36 g/L
Significant increase compared to wild-type GeDH strain
6. In Silico-Assisted Design
6.1 Computational Design Workflow
Figure 1. Schematic workflow for the computational design of GeDH enzyme variants:
- (a) Sequence conservation analysis (e.g., PSSM scoring) to identify critical catalytic residues and potential mutation sites
- (b) Three-dimensional structural models of candidate variants constructed and subjected to substrate docking simulations to predict changes in binding affinity
- (c) Integration of docking scores and molecular dynamics simulations to screen optimized mutation combinations (Q60R, L125P, F147Y, A300D)
6.2 Impact of Computational Strategies
The in silico component provided essential guidance for wet-lab experiments through:
- Protein structural modeling
- Molecular docking-based screening
- Production yield prediction
- Culture condition optimization
By integrating computational and experimental methodologies, the project achieved a more efficient and targeted workflow, substantially increasing the likelihood of success while reducing experimental uncertainty.