Difference between revisions of "Part:BBa K4387000"

Revision as of 08:05, 12 October 2022

Nitric Oxide Sensing Promoter pNorVβ

Usage and Biology

The inducible pNorVβ is an optimized nitric oxide sensitive promoter controlling the NorRVW operon in E. coli. The corresponding integration host factor IHF2 binding site was removed from the promoter pNorV to exhibit good sensitivity and dosage response at a low range of inducer DETA/NO, the used nitric oxide source in our experiments, activating the downstream genes' transcription [1]. In our constructs below, this promoter was coupled to a superfolder GFP and the transcriptional regulator NorR which creates a feedback loop to finetune the amount of NorR to the number of free NO molecules. It is also an improved part of the 2016 ETH iGEM team pNorV promoter.

This promoter was tested in the bacterial strain E.coli Nissle 1917.

Sequence and Features

Characterization and Measurements

Measuring parts with different approaches and comparing them to provide a more insightful and multilayered characterization is essential in Synthetic Biology. Here, we focused on two methods:

(i) time-lapse plate reader assays to measure the sensitivity of our circuit to NorR in a dynamic manner and under different concentrations of inducer; and

(ii) endpoint flow cytometry assays to measure the behavior of our circuits at the single-cell scale.

With the first assay, we uncovered essential kinetic information about the circuits on the populational level (every measurement is an average of the individual expression patterns in the sample). With the second assay, we delved deeper into the cell populations to characterize other essential properties of our system, such as expression noise and dose-dependent responses to different inducer concentrations.

To make our experiments reproducible, during plate reader assays (PHERAstar FSX - λEx: 485 nm, λEm: 530 nm), we measured each sample for 16 hours at 37°C and constant orbital shaking, using three biological replicates (three individual colonies per circuit) and three technical replicates (three wells per biological replicate).

We performed the data analysis as follows:

1. Subtracted blanks from raw data
2. Normalize GFP by OD600
3. Calculate technical means of GFP/OD600 normalized data
4. Calculate the biological means of GFP/OD600 normalized data
5. Calculate the biological standard deviation of GFP/OD600 normalized data
6. When necessary, perform imputation. Usually, the first normalized measurements are noisy and unreliable as OD600 values can be very low and significantly impact normalization. Thus, when individual normalized values are extremely high or low (sometimes negative due to blank correction), imputation was used following a na_kalman() function from the ImputeTS R package.
7. Additional transformations, such as log transformations.

Hence, our plots show the averages and standard deviations for the biological replicates for each sample for each time point.

For the flow cytometry experiment, cell cultures were grown overnight in LB medium supplemented with antibiotic, diluted in 2mL of M9 (supplemented with glucose, cas amino acids and an antibiotic) in a 1:10 ratio (v/v), induced with different NO concentrations and grown for 7 hours in a shaker (37C, 220 RPM). Samples were then chilled in ice to halt cell growth and diluted in 1mL of cold PBS (1:500 v/v ratio). A total of 100,000 cells per sample was measured in a BD FACSCanto II flow cytometer (FSC: 625V, SSC: 420V, FITC: 650V, Event threshold: FSC & SSC > 200, Channel: FITC (λEx 488 nm / λEm. 530/30 nm, High flow rate: ~ 10,000 events/s).

We performed all analyses using in-house R scripts.

Nitric Oxide Sensing Genetic Circuit With the ETH promoter pNorV

This part consists of the ETH promoter pNorV, a superfolder GFP preceded by one strong ribosomal binding site (BBa_B0034), the NorR regulator, and a double forward terminator. We chose a high-copy backbone from Twist Bioscience for this part. We wanted to compare the ETH NorV promoter to the pNorVβ promoter and see which one was better suited for sensing nitric oxide.

Time-Lapse Plate Reader Assay

Figure 1a: Response of the Genetic Circuit pNorV (2016 ETH Team) including 1 RBS to DETA/NO induction. We observe that the GFP expression is ~2300 GFP/OD for NO-concentration of 2mM. Comparing this construct with the others in figure 1b and 1c, we could prove that this genetic circuit with the 2016 ETH promoter is the least effective out of the other constructs with pNorVβ.

To make our experiments reproducible, during plate reader assays (PHERAstar FSX - λEx: 485 nm, λEm: 530 nm), we measured each sample for 16 hours at 37°C and constant orbital shaking, using three biological replicates (three individual colonies per circuit) and three technical replicates (three wells per biological replicate).

We performed the data analysis as follows:

1. Subtracted blanks from raw data
2. Normalize GFP by OD600
3. Calculate technical means of GFP/OD600 normalized data
4. Calculate the biological means of GFP/OD600 normalized data
5. Calculate the biological standard deviation of GFP/OD600 normalized data
6. When necessary, perform imputation. Usually, the first normalized measurements are noisy and unreliable as OD600 values can be very low and significantly impact normalization. Thus, when individual normalized values are extremely high or low (sometimes negative due to blank correction), imputation was used following a na_kalman() function from the ImputeTS R package.
7. Additional transformations, such as log transformations.

Hence, our plots (Figure 1a and Figure 1b) show the averages and standard deviations for the biological replicates for each sample for each time point.

Figure 1b: Response of Different Gene Circuits to DETA/NO Induction. Induction of plasmids BBa_K4387003 to BBa_K4387007. For each plasmid, it plots the emission of GFP over time, after induction with 6 different DETA/NO concentrations. Construct pNorVβ including 2 RBS has the highest GFP emission, whereas pNorV of the 2016 ETH team has the lowest GFP emission.

Figure 1c: Dose Response of all Constructs to DETA/NO. Plotted are the highest GFP expression levels under different concentrations of DETA/NO

Because the standard deviations overlap, we thought that we might be able to reduce the standard deviations and get a clearer result if we had more samples. To get more samples, we also performed a flow cytometry.

Endpoint Flow Cytometry Assay

For the flow cytometry experiment, cell cultures were grown overnight in LB medium supplemented with antibiotic, diluted in 2mL of M9 (supplemented with glucose, cas amino acids and an antibiotic) in a 1:10 ratio (v/v), induced with different NO concentrations and grown for 7 hours in a shaker (37C, 220 RPM). Samples were then chilled in ice to halt cell growth and diluted in 1mL of cold PBS (1:500 v/v ratio). A total of 100,000 cells per sample was measured in a BD FACSCanto II flow cytometer (FSC: 625V, SSC: 420V, FITC: 650V, Event threshold: FSC & SSC > 200, Channel: FITC (λEx 488 nm / λEm. 530/30 nm, High flow rate: ~ 10,000 events/s).

Figure 2a: Violin Plots and Boxplots for the Genetic Circuit Containing 2022 UZurich's pNorVβ Promoter Including 1 RBS and the Genetic Circuit Containing 2016 ETH's pNorV Promoter Including 1 RBS. These plots represent the distribution of FITC-H (green fluorescence) values for the two different promoters at 4 different concentrations of DETA/NO. Each measurement consisted of 100'000 cells.

Figure 2b: Noise Levels of 2022 UZurich's pNorVβ Promoter and 2016 ETH's pNorV Promoter.: Noise levels are measured as the coefficient of variation for construct sample and NO concentration. The constructs show low noise levels at high concentrations.

The results showed that our construct with pNorVβ has a higher response to induction with DETA/NO than the construct with pNorV (figure 2a). It further revealed that the constructs with pNorVβ and pNorV have relatively low noise compared to the negative control (figure 2b). Due to these reasons and the large sample size (100'000 sample points per plasmid), we can infer that pNorVβ is more responsive to DETA/NO induction than pNorV.

Nitric Oxide Sensing Genetic Circuit With One Ribosomal Binding Site

This part consists of the inducible pNorVβ promoter, a superfolder GFP preceded by one strong ribosomal binding site (BBa_B0034), the NorR regulator, and a double forward terminator. We chose a high-copy backbone from Twist Bioscience for this part. Due to the competitive binding of the activated and inactivated NorR on the promoter, we decided on this construct with a positive feedback loop that adjusted the levels of NorR based on the amount of nitric oxide present [1]. The presence of nitric oxide would activate pNorVβ to induce GFP and NorR expression. Thereby, we ensure that high amounts of NorR will be produced only when NO is present.

Time-Lapse Plate Reader Assay

Figure 3a: Response of the Genetic Circuit pNorVβ including 1 RBS to DETA/NO induction. We observe that the GFP expression is ~3000 GFP/OD for NO-concentration of 2mM. Comparing this construct with the others in figure 3b and 3c, we could prove that pNorVβ is more powerful than the ETH promoter but less effective than the constructs with additional ribosomal binding sites.

To make our experiments reproducible during plate reader assays (PHERAstar FSX - λEx: 485 nm, λEm: 530 nm), we measured each sample for 16 hours at 37°C and constant orbital shaking, using three biological replicates (three individual colonies per circuit) and three technical replicates (three wells per biological replicate).

We performed the data analysis as follows:

1. Subtracted blanks from raw data
2. Normalize GFP by OD600
3. Calculate technical means of GFP/OD600 normalized data
4. Calculate the biological means of GFP/OD600 normalized data
5. Calculate the biological standard deviation of GFP/OD600 normalized data
6. When necessary, perform imputation. Usually, the first normalized measurements are noisy and unreliable as OD600 values can be very low and significantly impact normalization. Thus, when individual normalized values are extremely high or low (sometimes negative due to blank correction), imputation was used following a na_kalman() function from the ImputeTS R package.
7. Additional transformations, such as log transformations.

Hence, our plots show the averages and standard deviations for the biological replicates for each sample for each time point.

Figure 3b: Response of Different Gene Circuits to DETA/NO Induction. Induction of plasmids BBa_K4387003 to BBa_K4387007. For each plasmid, it plots the emission of GFP over time, after induction with 6 different DETA/NO concentrations. Construct pNorVβ including 2 RBS has the highest GFP emission, whereas pNorV of the 2016 ETH team has the lowest GFP emission.

Figure 3c: Dose Response of all Constructs to DETA/NO. Plotted are the highest GFP expression levels under different concentrations of DETA/NO

Because the standard deviations overlap, we thought that we might be able to reduce the standard deviations and get a clearer result if we had more samples. To get more samples, we also performed a flow cytometer.

Endpoint Flow Cytometry Assay

For the flow cytometry experiment, cell cultures were grown overnight in LB medium supplemented with antibiotic, diluted in 2mL of M9 (supplemented with glucose, cas amino acids, and an antibiotic) in a 1:10 ratio (v/v), induced with different NO concentrations and grown for 7 hours in a shaker (37C, 220 RPM). Samples were then chilled on ice to halt cell growth and diluted in 1mL of cold PBS (1:500 v/v ratio). A total of 100,000 cells per sample was measured in a BD FACSCanto II flow cytometer (FSC: 625V, SSC: 420V, FITC: 650V, Event threshold: FSC & SSC > 200, Channel: FITC (λEx 488 nm / λEm. 530/30 nm, High flow rate: ~ 10,000 events/s).

Figure 4a: Violin Plots and Boxplots for Our 6 Different Constructs. These plots represent the distribution of FITC-H (green fluorescence) values for our six different plasmids at four different concentrations of DETA/NO. Each measurement consisted of 100'000 cells.

Figure 4b: Noise Levels of Our 6 Different Constructs. Noise levels are measured as the coefficient of variation for construct sample and NO concentration. Most constructs show low noise levels at high concentrations.

While figure 4a shows that the construct with pNorVβ and 2 RBS (BBa_K4387006) has higher overall GFP expression values, it is also leakier than this construct with pNorVβ and 1 RBS (BBa_K4387005) or the construct with pNorVβ and 3 RBS (BBa_K4387007). If high GFP expression is required, but some leakiness does not matter much, we recommend choosing BBa_K4387006. If lower leakiness is essential, but GFP expression does not need to be very high, we recommend using parts BBa_K4387005 or BBa_K4387007 instead.

Nitric Oxide Sensing Genetic Circuit With Two Ribosomal Binding Sites

In the frame of our project, we wanted to improve the construct BBa_K4387005 by adding one more ribosomal binding site to see if we could achieve a higher GFP response.

Thus this part consists of the inducible pNorVβ promoter, a superfolder GFP preceded by two strong ribosomal binding sites (BBa_B0029, BBa_B0034), the (NorR regulator), and a double forward terminator. We chose a high-copy backbone from Twist Bioscience for this part. Due to the competitive binding of the activated and inactivated NorR on the promoter, we decided on this construct with a positive feedback loop that adjusted the levels of NorR based on the amount of nitric oxide present [1]. The presence of nitric oxide would activate pNorVβ to induce GFP and NorR expression. Thereby, we ensure that high amounts of NorR will be produced only when NO is present.

Time-Lapse Plate Reader Assay

Figure 5a: Response of the Genetic Circuit pNorVβ including 2 RBS to DETA/NO induction. We observe that the GFP expression is ~20'000 GFP/OD for NO-concentration of 2mM. Comparing this construct with the others in figure 5b and 5c, we could prove that this genetic circuit with pNorVβ including 2 RBS is the most effective construct between all six.

To make our experiments reproducible, during plate reader assays (PHERAstar FSX - λEx: 485 nm, λEm: 530 nm), we measured each sample for 16 hours at 37°C and constant orbital shaking, using three biological replicates (three individual colonies per circuit) and three technical replicates (three wells per biological replicate).

We performed the data analysis as follows:

1. Subtracted blanks from raw data
2. Normalize GFP by OD600
3. Calculate technical means of GFP/OD600 normalized data
4. Calculate the biological means of GFP/OD600 normalized data
5. Calculate the biological standard deviation of GFP/OD600 normalized data
6. When necessary, perform imputation. Usually, the first normalized measurements are noisy and unreliable as OD600 values can be very low and significantly impact normalization. Thus, when individual normalized values are extremely high or low (sometimes negative due to blank correction), imputation was used following a na_kalman() function from the ImputeTS R package.
7. Additional transformations, such as log transformations.

Hence, our plots show the averages and standard deviations for the biological replicates for each sample for each time point.

Figure 5b: Response of Different Gene Circuits to DETA/NO Induction. Induction of plasmids BBa_K4387003 to BBa_K4387007. For each plasmid, it plots the emission of GFP over time, after induction with 6 different DETA/NO concentrations. Construct pNorVβ including 2 RBS has the highest GFP emission, whereas pNorV of the 2016 ETH team has the lowest GFP emission.

Figure 5c: Dose Response of all Constructs to DETA/NO. Plotted are the highest GFP expression levels under different concentrations of DETA/NO

Because the standard deviations overlap, we thought that we might be able to reduce the standard deviations and get a clearer result if we had more samples. To get more samples, we also performed a flow cytometry.

Endpoint Flow Cytometry Assay

For the flow cytometry experiment, cell cultures were grown overnight in LB medium supplemented with antibiotic, diluted in 2mL of M9 (supplemented with glucose, cas amino acids, and an antibiotic) in a 1:10 ratio (v/v), induced with different NO concentrations and grown for 7 hours in a shaker (37C, 220 RPM). Samples were then chilled on ice to halt cell growth and diluted in 1mL of cold PBS (1:500 v/v ratio). A total of 100,000 cells per sample was measured in a BD FACSCanto II flow cytometer (FSC: 625V, SSC: 420V, FITC: 650V, Event threshold: FSC & SSC > 200, Channel: FITC (λEx 488 nm / λEm. 530/30 nm, High flow rate: ~ 10,000 events/s).

Figure 6a: Violin Plots and Boxplots for Our 6 Different Constructs. These plots represent the distribution of FITC-H (green fluorescence) values for our six different plasmids at four different concentrations of DETA/NO. Each measurement consisted of 100'000 cells.

Figure 6b: Noise Levels of Our 6 Different Constructs. Noise levels are measured as the coefficient of variation for construct sample and NO concentration. Most constructs show low noise levels at high concentrations.

While figure 6a shows that the construct with pNorVβ and 2 RBS (BBa_K4387006) has higher overall GFP expression values, it is also leakier than this construct with pNorVβ and 1 RBS (BBa_K4387005) or the construct with pNorVβ and 3 RBS (BBa_K4387007). If high GFP expression is required, but some leakiness does not matter much, we recommend choosing BBa_K4387006. If lower leakiness is essential, but GFP expression does not need to be very high, we recommend using parts BBa_K4387005 or BBa_K4387007 instead.

Nitric Oxide Sensing Genetic Circuit With Three Ribosomal Binding Sites

In the frame of our project, we wanted to further improve the construct BBa_K4387005 by adding two more ribosomal binding sites to see if we could achieve a higher GFP response.

Thus this part consists of the inducible pNorVβ promoter, superfolder GFP preceded by three strong ribosomal binding sites (BBa_K4387020 [2], BBa_B0029, BBa_B0034), the NorR regulator, and a double forward terminator. We chose a high-copy backbone from Twist Bioscience for this part. Due to the competitive binding of the activated and inactivated NorR on the promoter, we decided on this construct with a positive feedback loop that adjusted the levels of NorR based on the amount of nitric oxide present [1]. The presence of nitric oxide would activate pNorVβ to induce GFP and NorR expression. Thereby, we ensure that high amounts of NorR will be produced only when NO is present.

According to figure__, we could prove that the construct with two ribosomal binding sites and the codon-optimized NorR BBa_K4387006 had the highest GFP response.

Nitric Oxide Sensing Genetic Circuit Without the NorR regulator

In the frame of our project, we wanted to improve the sensitivity of our construct BBa_K4387005. For this purpose, we removed the codon-optimized NorR, creating a circuit that would rely on endogenous NorR.

Thus this part consists of the inducible pNorVβ promoter, superfolder GFP preceded by two strong ribosomal binding sites (BBa_B0029, BBa_B0034), and a double forward terminator. We chose a high-copy backbone from Twist Bioscience for this part.

According to figure__, we could prove that the construct with two ribosomal binding sites and the presence of the codon-optimized NorR BBa_K4387006 had the highest GFP response.

Exchanging the sfGFP with a Single Domain Antibody

This composite part contains the nitric oxide sensor pNorVß (BBa_K4387000), followed by 2 different ribosome binding sites. The promoter induces upon NO binding the expression of a monovalent anti-TNFα nanobodoy BBa_K4387996. The additional NorR at the end of the composite part enhances the positive feedback-loop, increasing the response of the promoter to NO. [1] Together with the hemolysin A secretion machinery BBa_K4387987, a complete genetic circuit is obtained that allows secretion of nanobodies [3] or if exchanged, secretion of other proteins of interest.

References

• [1] Xiaoyu J. Chen, Baojun Wang, Ian P. Thompson, and Wei E. Huang et al. Rational Design and Characterization of Nitric Oxide Biosensors in E. coli Nissle 1917 and Mini SimCells ACS Synthetic Biology 2021 10 (10), 2566-2578 DOI: 10.1021/acssynbio.1c00223
• [2] Ayelet Levin-Karp, Uri Barenholz, Tasneem Bareia, Michal Dayagi, Lior Zelcbuch, Niv Antonovsky, Elad Noor, and Ron Milo et al. Quantifying Translational Coupling in E.coli Synthetic Operons Using RBS Modulation and Fluorescent Reporters ACS Synthetic Biology 2013 2 (6), 327-336 DOI: 10.1021/sb400002n
• [3] Silence, Karen, Lauwereys, Marc, De Haard, Hans, et al. "Single domain antibodies directed against tumour necrosis factor-alpha and uses therefor", Int. Publication Number: WO 2004/041862 A2, 21 May 2004