Project
Human Practices
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Wet Lab
Jamboree
Synthetic biology constitutes an effort towards making biology easy to engineer [1]. This means that basic principles of engineering find their place in biological systems;
We treat biomers as spare, interchangeable parts, the same way that we would approach the construction of any mechanical device. This necessitates the standardization of parts, in order to establish objectivity regarding their effectivates and to promote the acceleration of knowledge [2,3]. For our project, to identify the optimal components and regulatory mechanisms for our system, we employed the Design-Build-Test-Learn cycle, allowing us to manipulate the expression of our constructs throughout different phases of the bacterial life cycle. To minimize cellular stress, we strategically divided our system into two phases: the exponential phase and the stationary phase. During the exponential phase, we designed our system to express T7 polymerase and produce dsRNA molecules. In our pursuit of an autoinducible promoter active during the exponential phase, we explored the work of Zobel et al. and tested three of their synthetic promoters. Through our focused examination of their autoinducible properties, we determined that BG37 BBa_K5299008 emerged as the optimal choice for regulating our system and we decided to further characterize BG37 by assessing its performance across various bacterial chassis, employing different plasmid backbones and carbon sources. By thoroughly characterizing this new basic part, we believe it will serve as a valuable tool for future teams aiming for orthogonal expression during the exponential phase.
During our experimental design, we aimed to answer key biological questions about how the BG37 promoter functions across different bacterial chassis, environmental conditions, and plasmid backbones. We sought to understand whether BG37 could maintain consistent, orthogonal activity during the exponential phase and how factors like growth media, carbon sources, and vector backbones influence its performance for optimal system regulation.
The characterization plan aimed to address several key biological questions, including:
1. Is BG37 an effective orthogonal promoter across different bacterial chassis?
We aimed to collect more data on how BG37 functions in various bacterial strains to determine if it maintains consistent activity independent of host regulatory systems, ensuring its broad applicability and modularity[5].
2. How does BG37's activity compare to well-characterized promoters?
This comparison aimed to establish BG37's relative strength and its activation time point, providing a benchmark against known standards. We conducted a thorough characterization by comparing BG37 with the standard Anderson J23119 promoter and the stationary osmY promoter, which allowed us to generate reliable and comparable data. This approach ensured that we could accurately assess BG37's performance in relation to well-established promoters. Characterization, being the process of estimating quantitative measures of part behavior, enabled us to quantify BG37's strength and activation time, providing a solid foundation for its use in future applications.
3. How do environmental factors, such as carbon sources and growth media, impact BG37’s performance?
Since various carbon sources lead to distinct metabolic products that can alter cellular physiology and energy availability, we aimed to assess if these metabolic shifts influence the promoter's activity. By testing BG37 with different carbon sources, we sought to determine whether changes in the cell's metabolic state would affect our system’s expression levels, ensuring its reliability in diverse growth environments [6].
4. Does BG37 function similarly across different plasmid backbone vectors?
This question aimed to assess whether the promoter’s activity remains consistent across different vectors, which is crucial for its versatility in synthetic biology. Moving a genetic construct from one context to another, such as different host organisms or plasmid backbones, significantly impacts the behavior of genetic devices. The genetic context, including host genome interactions and plasmid architecture, plays a pivotal role in determining the success of a genetic device [7]. One example of this is the influence of plasmid backbones. Key factors such as plasmid copy number, origin of replication, and plasmid stability can drastically alter gene expression levels [8].
Since various plasmid backbones are needed to work with different chassis due to different compatible origins of replication. While working, with E. coli DH5a cells, working with pDGB3a vector that has pBR32 We compared the activation of BG37 in pSEVA23g19[g1] vector with the activation of BG37 in the Golden Braid pDGB3a1 vector, starting with E. coli DH5α cells as an intermediate step before transitioning to P. putida. By doing this, we gathered valuable data about the behavior of BG37 in different backbones, allowing us to understand how the promoter operates across diverse cloning vectors [5].
The general spirit for our characterization was standardization to the best of our ability. Given our need for an orthogonally sound promoter, we focused on the reproducibility of results, testing on different chassis, adequate controls to pinpoint phase activation and strength, and measurements that covered the entirety of the bacterial population’s life cycle. This lead to the gathering of detailed supporting data fit for a highly characterized Registry part. We hope that our efforts prove fruitful for future teams, as we add an autoinducible, synthetic, exponential phase- activated promoter.
To determine the most suitable promoter for the T7 polymerase production system we tested three BG synthetic promoters (BG17, BG37, BG42) from the Zobel et al. library [1]. We selected the promoters BG17, BG42, and BG37 based on findings from Zobel et al., which demonstrated their significant activation during the exponential growth phase. Our objective was to evaluate their temporal activation profiles and relative strength. We used E. coli BL21 (DE3) cells, which are able to express the T7 polymerase system , so they can serve as a relevant model for our engineered P. putida chassis, allowing us to evaluate our constructs’ effectiveness in a system closely resembling our final design.
The promoter J23119 serves as a positive control because it is a well-characterized constitutive promoter from the Anderson family, allowing for direct comparison of promoter strength and consistent activation throughout all growth phases [9]. Additionally, osmY is a well-characterized promoter specifically induced during the stationary phase, making it a valuable positive control for assessing time-dependent activation and promoter strength during this phase [10].
Our goal was to evaluate the performance of our designed device featuring the BG37 promoter. Due to time constraints, we decided to test T7 polymerase production by using an sfGFP reporter under the control of a T7 promoter. For this reason, the J23119 Anderson and BG37 constitutive promoters were used to drive T7 polymerase gene expression. The J23119 promoter served as a positive control for continuous T7 production, while E. coli cells with the pDGB3omega plasmid were used as a negative control. Additionally, we aimed to assess the timing of BG37 promoter activation, focusing on whether it activates specifically during the exponential phase. We used E. coli DH5a cells for this experiment, which are optimized for maintaining plasmids, with specific mutations (such as recA1 and endA1) that prevent unwanted recombination and degradation of foreign DNA and lack the T7 RNA polymerase system [11].
Since various plasmid backbones have different characteristics (e.g. origin of replication) that are needed to work in different chassis, we compared the pSEVA23g19g1 vector with the Golden Braid pDGB3a1 vector, working with E. coli DH5α as an intermediate step before transitioning to P. putida. Simultaneously, we validated that its function is consistent across different genetic backbones, which reduces the likelihood of vector-specific behavior or artifacts [12].
We used an M9 medium with two different carbon sources, citrate and glucose, and blanked the M9 with the corresponding carbon source for accurate measurements. The reason for selecting these carbon sources was to replicate the experiment in P. putida, which can metabolize both, but due to time constraints, we were unable to complete this step [13]. At the same time, literature suggests that citrate is not the optimal carbon source for E. coli, likely adding additional stress to the cells. By using osmy as a positive control, which is induced when σs is expressed (indicative of RNA polymerase activity under stress), and measuring the OD simultaneously, we aimed to understand the correlation between promoter activation and bacterial growth phase. Additionally, J23119 was used as a positive control for constitutive expression, allowing us to compare promoter strength across different growth phases and environmental conditions.
We chose to work with P. putida as it is our proposed chassis. The J23119 promoter was used as a positive control due to its well-documented robust performance in P. putida [14]. Moreover, since the stress response sigma factor σ^S is conserved between Pseudomonas species and E. coli, we employed osmy as a negative control to assess promoter activity under stress conditions, enabling us to understand the promoter behavior across various growth phases [15].
It is worth noting that before conducting any experiment we used SnapGene to 1) in silico simulate the cloning of our constructs, 2) to verify the design and ensure compatibility of the parts, and 3) run a virtual agarose gel electrophoresis to identify the most suitable restriction enzymes for diagnostic digestion, and confirm banding patterns before stepping into the lab, thus optimizing our workflow and ensuring accurate results. We began by cloning the parts we needed (BG37, BG42, BG17, J23119, osmY, ter-BBa_B0015, sfGFP, T7pol), which included Golden Braid overhangs, into the pUPD2 vector. The promoters were synthesized by IDT with their respective RBSs already integrated. We used E. coli DH5α cells for the cloning procedure due to their high transformation efficiency. Next, we transferred our basic parts from the pUPD2 vectors into the Golden Braid vectors: pDGB3a1 and pDGB3a2 vectors to assemble our level alpha constructs. This step enabled us to generate complete, functional genetic modules for further experimentation and analysis. Once we obtained our level a constructs in the pDGB3a1 and pDGB3a2 vectors, we proceeded to create our level omega constructs (BG37-RBS2-T7polymerase-ter + T7pro-sfGFP-T7ter, PJ23119-RBS2-T7polymerase-ter + T7pro-sfGFP-T7ter) by cloning them into the pDGB3omega vector. Simultaneously, we transferred our basic parts from the pUPD2 vectors into the Golden Standard vector, pSEVA23g19[g1], to generate Level 1 constructs. This vector was selected for its pBBR1 origin of replication, which enables compatibility with P. putida. After assembling our plasmid constructs, we successfully transformed them into their respective chassis: E. coli BL21 (DE3), E. coli DH5α, and P. putida KT2440. The following day, we prepared liquid cultures to promote the growth and acclimatization of the transformed bacteria, allowing them to incubate overnight. On the third day, we centrifuged the cultures and washed the resulting pellets twice with 0,8% saline solution (NaCl). Subsequently, we performed a 1:100 dilution to measure the optical density at 600 nm (OD600). To achieve an OD600 of 0.1 with a final volume of 2 mL of M9 culture with the corresponding carbon source, we applied the dilution equation Cinitial x Vinitial= C final x V final. M9 medium was selected for this experiment due to the fluorescence interference associated with LB medium. We then transferred 200 μL of each diluted culture into the wells of a 96-well plate with a clear bottom, as illustrated in Figure 3. Each construct was tested in five technical repeats. The plate was incubated in a plate reader for 15 hours at 37°C, which is optimal for E. coli growth, while shaking at 180 rpm. Measurements were automatically recorded every hour, monitoring OD at 600 nm and sfGFP fluorescence at 515 nm.
1. Identification of the Most Suitable Promoter for T7 Polymerase Production
By observing Figure 5 and analyzing the OD600 measurements, we notice significant bacterial growth between the 1h and 8h time points for every promoter except BG37. Specifically, for the E. coli cells with the BG37 promoter, there is an increase up to the 12h mark, with the steady increase in OD600 between 6h and 12h suggesting that the cells are in the exponential phase.
Based on the graphs (Figure 5 and Figure 6) and OD600 data, which show the cells remaining in the exponential phase at 12 hours, BG37 demonstrates strong activity during this period. More specifically, at 6 hours, BG37 shows a significant increase in fluorescence, surpassing the other promoters and indicating its responsiveness during exponential growth. By 10 hours, it continues to exhibit higher regulation than BG17 and osmy. Even at 15 hours, as cells transition out of exponential growth, BG37 remains active, showing its sustained effectiveness.
In comparison, while J23119 and BG42 also perform well, BG37 stands out for its early activation (at 6 hours), making it ideal for systems needing robust expression during early exponential growth. Although BG42 and J23119 are more consistent across phases, BG37 is clearly the best promoter for targeted regulation during the exponential phase. Its early and sustained activity make it the most suitable choice for driving T7 polymerase production during exponential growth.
2. Evaluation of T7 polymerase production device
The growth curve shows that although BG37 and J23119 effectively drive strong expression of T7 polymerase and sfGFP, they cause a slight reduction in growth compared to the pDGB3 omega control. Simultaneously, the similarity between the growth profiles of J23119 and BG37 suggests that both promoters impose a comparable burden on the cells.
3. Influence of different backbone vectors on BG37 activation
Focusing on Figure 9, we observe that the exponential phase occurs between 2h and 10h. The cells containing only the pDGB3a1 (- control) exhibit the most robust growth. This is likely due to the absence of additional stress from the production of sfGFP, which is present in the other constructs, leading to a slower growth rate.
In Figure 10, we observe that the BG37 promoter is predominantly activated between 6h and 15h, after which its activity begins to decline. The J23119 promoter shows lower activity compared to BG37, and its activation correlates closely with the bacterial growth curve. On the other hand, the osmy promoter appears to be primarily activated during the stationary phase, starting after 10h.
Promoter J23119 in Figure 11 appears to have an extended lag phase lasting until the 7h time point. The other cells, including those containing only the pSEVA23g19[g1] (- control), exhibit growth curves that show the exponential phase occurring between the 4 and 11h time points.
In Figure 12 we observe that promoter J23119 has very high fluorescence levels, likely due to its prolonged lag phase. The BG37 promoter seems to activate around the time point 10h, outside of the bacterial exponential phase. At the same time, osmY promoter has very low activation levels even in the stationary phase.
When comparing fluorescence intensity for BG37-RBS2-sfGFP-ter in two different plasmid vectors, we observe that the promoter is activated during the exponential phase but is also high in the stationary phase. Simultaneously, we observe higher levels of sfGFP’s expression when it is regulated by the BG37 promoter inserted in the pDGB3a1 vector.
4. Influence of different carbon sources on BG37 activation
In M9 medium with citrate as a carbon source E. coli DH5α cells with BG37, J23119 and osmY promoters, as well as with only pDGB3a1 vector, appear to grow in a similar way. The exponential phase seems to be between 4-12h time points.
With citrate as a carbon source E. coli DH5a with J23119 and BG37 promoters seem to emit fluorescence signals even in the stationary phase, where we see the abrupt activation of the osmY promoter.
5. Further characterization of BG37 in our design chassis: P. putida
In Figure 15, we observe that bacteria with all constructs appear to have similar growth curves, with the exponential phase starting at the 4h time point and ending around the 12h time point.
In Figure 16 we see that J23119 and BG37 promoters’ signal starts to rise between the 6h and 10h point times, while the higher signal is observed at the 15h mark. At that point we also see high activation of the osmY promoter, indicating that we have reached the stationary phase.
This Figure 18 presents an evaluation of promoter’s activity in different bacterial chassis over time:
• In E. coli DH5a (pSEVA23g19[g1]) BG37 promoter is activated in the exponential phase and continues to give a stable signal even in the stationary phase.
• In E. coli BL21 (DE3) (pDGB3a1) and in P. putida KT2440 BG37 promoter is activated in the exponential phase but has a stronger signal in the early stationary phase.
• In E. coli DH5a (pDGB3a1): BG37 promoter seems to give a strong signal in the late exponential phase that continues into the stationary phase.
Overall, each chassis and plasmid combination shows a distinct promoter activation profile, likely reflecting differences in growth rates, metabolic efficiency, and plasmid copy numbers across these bacterial strains.
Characterizing a genetic part requires a comprehensive approach, testing its behavior across multiple biological contexts, including plasmid backbones, host chassis, and varying growth conditions such as different carbon sources. Each of these factors plays a pivotal role in determining the functionality of a genetic circuit, underscoring the need for thorough characterization to ensure predictable performance. The choice of plasmid backbone, for instance, affects gene expression due to differences in copy number dictated by the origin of replication. Additionally, while many promoters are designed to function consistently across species, host-specific factors such as regulatory networks, stress responses, and metabolic capabilities can profoundly influence promoter activity. Carbon source variability further impacts promoter performance, as it directly affects metabolic load, energy availability, growth rates, and protein production. Ultimately, testing the promoter within a functional device, such as a level omega construct, provides the most reliable assessment of its utility and predictability in real-world applications.
References
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