Engineering
2023 SDU-CHINA
Introduction
Three-layer dynamic regulation model
1st iteration
2nd iteration
3rd iteration
References
We aim to implement cell intelligence to boost the production of PHB. Cell intelligence (CI) refers to the automation of the production process, resulting in increased productivity and reduced costs. Our hardware and software have been specifically designed to attain this objective. We followed the DBTL principle, developed our three-layer dynamic regulation model.
2.4 Three-Layer dynamic regulation model
Overall, we would like to divide the process of PHB production by E. coli into three stages: the growth stage, the production stage and the product release stage. In this way, E. coli can be fed first, and then work at full capacity; when their "stomach" is full of PHB, we crack it and release the products automatically. We plan to use the Esa I/R quorum sensing system to switch between growth and production phases. A promoter expressed in the late stationary phase, linked to a lysis gene, will be used to control bacterial lysis at the end of the production phase.
Three-layer Dynamic Regulation Model
Growth Phase and Production Phase
We chose to use the Esa I/R system to control the switch between Growth Phase and Production Phase. The EsaI/R QS system is homologous to the LuxI/R QS system, but EsaR can act as both transcriptional activator and repressor[1]. Our work was based on 3 strains that that already have the system (see Experiments for more details). At low cell density, the PesaR is turned off and the PesaS is turned on. At high cell density, the PesaR is turned on and the PesaS is turned off (see Experiments for more details). We firstly used fluorescence to characterize.
Product-release Phase
We firstly used 4 stationary phase promoters (Pfic, P1.1, P2.1, P3.1)[2].
Combining the different plasmids and transferring them into E. coli, we obtained the following bacteria containing two plasmids.
These 6 strains are for QS- switch characterization:
These 6 strains are for QS- switch characterization
The following combinations are transformed into both L19 and L31. The total number is 24.
The following combinations are transformed into both L19 and L31
QS-switch Characterization
We used a Multi-Detection Microplate Reader (Synergy HT, Biotek, U.S.) to detect the fluorescence.
We found that the characterization results for all six combinations were as expected: At low cell density, the PesaR is turned off and the PesaS is turned on, while at high cell density, the PesaR is turned on and the PesaS is turned off (see Experiments for more details).
Stationary Phase Promoter Characterization
We found that combinations were not as expected: no significant temporal separation of expression occurred.
In the 2nd iteration, we aimed to find promoters expressed in the late stationary phase and characterize them.
This time, by reading scientific articles, we found four promoters expressed in the late stationary phase (PYU3, PYU7, PYU16, PYU92)[3].
Using the same steps in the 1st iteration build 1.0, we constructed the following 24 strains.
Our 24 strains
Stationary Phase Promoter Characterization—2.0
These are the eleven groups out of 24 combinations that fulfilled the expected criteria: there was a significant difference in the expression times of the two promoters.
In the 2nd iteration, we find promoters that meet the need based on the results of the previous iteration and characterize them in our system. Out of 24 combinations, we found one combination that met the expectation. This means that each part of our model has been successfully characterized separately. We hope to further validate our system within the same bacteria, which will be more rigorous and scientific.
In the previous iterations, we have validated the feasibility of each component separately. However, our advisors pointed out that we need to characterize the individual components inside the same bacterium, which would be more rigorous and scientifically sound.
In this iteration, we decide to transform 3 plasmids combinations into L19 and L31. We used BFP for characterization of PYU promoter.
We construct the following 10 strains that each contains 3 plasmids based on 1st and 2nd iteration:
Our 10 strains
Three-Layer Dynamic Regulation Model Characterization
Here are the results:
After three iterations, we successfully developed the model. This model worked well in E. coli MG1655. Subsequently, we applied it to the production of PHB.
References