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−	<figcaption><b>Fig. 8 </b>. (A)  Nile Red Plate：PHB-producing strain vs. non-PHB-producing strain; (B)PHB-producing strains streaked on Nile red plate and non-Nile red plate control</figcaption>	+	<figcaption><b>Fig. 9 </b>. (A)  Nile Red Plate：PHB-producing strain vs. non-PHB-producing strain; (B)PHB-producing strains streaked on Nile red plate and non-Nile red plate control</figcaption>
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Revision as of 16:58, 11 October 2023

PHB production Regulation: PesaS-B0034-gltA-PesaRwt-B0034-PHBcab-PYU16-B0034-SRRz

PesaS-B0034-gltA-PesaRwt-B0034-PHBcab-PYU16-B0034-SRRz

Usage and Biology

Many production processes using microorganisms face the dilemma of conflicting production products and key metabolic pathways. This means that simply introducing product-synthesising genes into engineered bacteria can greatly affect the growth of the microorganism, leading to a situation where production is too low. There are a number of current solutions to this problem. For example, metabolic engineering can regulate metabolic flow using methods such as gene knockdown, promoter replacement, etc. These static strategies are effective for productivity improvement, but are not responsive to changes in the cell or environment. Dynamic control is a favourable solution for the conditional knockdown of essential genes and balances the flow in the metabolic pathway.

The pathway of PHB synthetsis is conflict with TCA cycle. Both TCA cycle and PHB production pathways use acetyl-coA as raw material, so if only the PHB production gene circuit is simply added to the engineered bacteria, the growth of the bacteria will be greatly affected, and the final result is low PHB production. Quorum sensing system can automatically sense cell density to regulate downstream genetic on/off. It is independent of metabolic pathways and do not need exogenous inducers, which make it a perfect tool to solve this problem. PHB are a form of carbon storage by bacteria. PHB products take up most of the space inside the cell, but will not be released from the cell. The method of mechanical crushing or chemical solvent extraction used in traditional industry is not only expensive, but also brings great pressure to the environment, so we hope to design an auto-lysis system with specific expression time.

This part is about PHB Production Regulation based on the Three-layer Dynamic Regulation Model. It dynamically regulate the PHB production by dividing the process into 3 phases: Growth Phase, Production Phase and Product-release Phase. In this way, the cell can firstly grow up and then put their all effort into PHB production. Finally, in the late stationary phase of cell growth, the enigneered bacteria can express lysis gene.

1. Three-layer Dynamic Regulation Model

2. The first and second layer--Growth and production control

Quorum Sensing is a way for cells to regulate downstream gene expression based on their own density. The concentration of the signaling molecule - AHL - secreted by the cell increases as the cell density increases. When the concentration of AHL reaches a certain level, it can bind to the corresponding binding protein and alter the expression of downstream genes.

In the first and second layer, this part using a QS-switch to regulate the flow of acetyl-coA. At the early stage of growth, using QS-switch turn on the TCA cycle and turn off the PHB production pathway, so that acetyl-coA flowed into the TCA cycle and the cells grew. When the cell grows to a certain extent, the TCA cycle is turned off, while the PHB production pathway is turned on, and the acetyl-coA flows to the PHB production pathway for PHB production.

4. The Third layer--Product-release control

In the third layer, this part uses a late stationary phase promoter and an lysis gene. The late stationary phase promoter is used to regulate the expressing time of the downstream gene. When the bacterial reach the stationary phase, the promoter will turn on and then the cell lysis.

Characterization

1. Design

• Step 1: Plasmid construct.
• Step 2: Verify the expression time difference between the first and second layers, and between the second and third layers。
• Step 3: Overall characterization

2. Protocols

Our experimental conditions for characterizing this part were as follows:

• E. coli MG1655
• 30^oC, 48h, under vigorous shaking
• Plasmid Backbone: PACYC, pUC
• Equipment: Multi-Detection Microplate Reader (Synergy HT, Biotek, U.S.) and Molecular Devices SpectraMax i3x.

We used GFP (excitation at 485 nm and emission at 535 nm), mKate (excitation at 490 nm and emission at 645 nm), and BFP (excitation at 400 nm and emission at 450 nm) to characterize this part. As our focus was mainly on the expression time, we processed the obtained fluorescence data by means of the following equation: x'=(x-min)/(max-min). This treatment makes all data fall between 0 and 1, which is easier to use for comparisons between different fluorescence data (since our focus is on expression time).

• Fermentation analysis: 1% inoculated in 100mL fermentation medium at 37℃ at 250rpm. Three parallel experiments were conducted for each strain. Samples were taken every 3-4 hours in the first 20 hours, OD600 and glucose concentrations were measured every 6-10 hours after 20 hours, and PHB concentrations were measured after 11 hours. During this period, glucose is supplemented to approximately 20g/L in the fermentation broth based on glucose concentration data.

3. The Characterization of PesaS and PesaRwt/PesaRc/PesaRp

We characterized PesaS-RBS(B0034)-GFP together with PesaRwt-RBS(B0034)-mkate, PesaRc-RBS(B0034)-mkate and PesaRp-RBS(B0034)-mkate. The green curve in the figure shows the results for PesaS. PesaS expression peaks at around 4-6 h and then rapidly declines to 0 at around 8-10 h.

4. The Characterization of PesaRwt/PesaRc/PesaRp and PYU3/PYU7/PYU16/PYU92

In the previous section, we first verified that the Growth Phase is temporally decoupled from the Production Phase, and then verified that the Production Phase and the Product-release Phase are temporally decoupled. To be more scientific, we decided to co-characterize the growth phase, the production phase and the product launch phase, and to check that all three were expressed in chronological order and at the required time. Due to the certain red-green crosstalk problem during detection, we constructed PACYC-PYU3-BFP, PACYC-PYU7-BFP, PACYC-PYU16-BFP, PACYC-PYU92-BFP plasmids by ligating the blue fluorescent protein gene BFP with PYU3,7,16,92 promoter in order to make the results clearer and the overall expression time sequence. We constructed 10 strains that each have 3 plasmids.

5. Selection and Characterization of lysis gene

We transformed the constructed plasmids into L19 and L31 to observe its lysis effect. The left side of the figure shows L19 containing the cleavage plasmid, and the right side does not contain the cleavage plasmid. After 1h of resting, it can be seen that the left side has been cleaved and clarified, and the right side is turbid, and the bacterium is sinking (Fig. 8).

6. Validation of the effectiveness of PHB synthesis genes

From the figure, we can see that the PHB-producing strains can be observed to fluoresce, while the non-PHB-producing strains cannot fluoresce under UV irradiation. And Nile Red works very effectively because there is no fluorescence on the non-Nile Red plate. This is proof that our PHBcab gene is effective.

7. PHB fermentation analysis

• Samples were taken at 3,7,11,14,24,30,38,48h for OD600 and glucose concentrations, and PHB concentrations were measured at the same time from the 11th hour onwards (Fig. 15).
• Glucose: After diluting the samples 50-fold (Fig. 16), the glucose concentration was measured using a glucose tester and glucose was replenished to 20 g/L at 14h and 25.5h of fermentation.
• OD600: Spectrophotometer is used to measure OD600.
• PHB: To test the PHB content, we took 2.85mL of culture solution for each group and added 0.15mL of chloroform (5%). Mix by gently inverting up and down, then centrifuge at 3400xg for 8 minutes at 4°C. Remove the chloroform-PHB phase (lower layer) in a glass vial with a pipette gun. To it, 150ul of sulfuric acid was added, 850ul of methanol was added, 1000 µl of chloroform was added, and oil bath was used for 1 h. It was taken out, cooled down to room temperature and then 1ml of dd water was added, and the shaker was shaken for 30 s. It was left to stand for more than 30 min for layering, and 80ul-150ul of the chloroform layer was taken into the gas-phase vial. Determination was carried out by gas chromatography.

Failure and Solutions

However, when we tested it, the results showed that there was no PHB. Since we had previously performed a step-by-step validation to ensure that each part could function properly, we suspected that there was a problem with the method of extracting PHB or the method of conducting the test. In order to verify our conjecture, we took 1mL of the 48h culture solution and centrifuged it, then removed the supernatant and took a small amount of the bacterium and observed it under the fluorescence microscope.

We then added 3 ml of chloroform to 15 mL of culture medium at 48 h and centrifuged it for 8 min at 4°C, which was used to extract extracellular PHB. 100 μL of the chloroform layer was then taken, and 0.5 μL of Nile Red-stained chloroform layer (working concentration 0.5 μg/ml) was added and measured by fluorescence intensity using a Multi-Detection Microplate Reader (Synergy HT, Biotek, U.S.)

All of these results demonstrate that the extracellular PHB production of the strains containing the three-layer regulatory system was much higher than that of the strains without the addition of the lysis system or without the use of the Quorum Sensing system to regulate the metabolic flow.

Summary of Results

• We have developed the Three-Layer Dynamic Regulation Model, which is a model that separates the growth stage, production stage and product release stage. . And it was proved to be working. We provided 10 valid models!
• Apply the Three-layer Dynamic Regulation model to the production of PHB.

Our part provides an example for future iGEM team

Theoretically, any use of E. coli as a platform for the production of a specific product, such as amino acids, can be applied to our model. we have explained how to apply the model well and how to adjust the model in the Contribution part of our Wiki so that future iGEM teams can easily cope with it.

Limitations

Sequence and Features