Part:BBa_K2116002:Experience

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Contribution by UZurich 2022

• Group: UZurich 2022
• Author: Jana Mehdy, Lea Brüllmann, Marine Mausy
• Summary: We compared the GFP expression rates of the ETH promoter to the pNorVβ promoter to sense nitric oxide.

Usage and Biology

We wanted to compare this ETH NorV promoter to the pNorVβ promoter and see which one was better suited for sensing nitric oxide at lower concentration ranges and which one expresses more GFP. Therefore we created a genetic circuit where we implemented this ETH promoter followed by one strong ribosomal binding site (BBa_B0034), which was attached to a superfolder GFP, the regulator NorR, and a double forward terminator. This composite part can be found here, and it is similar to the genetic circuit BBa_K4387005, which had the pNorVβ promoter instead of this ETH promoter. We chose a high-copy backbone from Twist Bioscience for these experiments.

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

Characterization

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.

We performed all analyses using in-house R scripts.

Time-Lapse Plate Reader Assay

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 1a: Dose response curves of GFP/OD for the different plasmids to various DETA/NO concentrations, with pNorVβ being the most expressive promoter, while pNorV of the 2016 ETH iGEM team being the least expressive promoter.

Figure 1b: Response of pNorV vs. pNorVβ to 2mM DETA/NO: This graph shows GFP expression normalized to the OD of pNorV and pNorVβ at 2mM 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 cytromety.

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 2a: Violin Plots and Boxplots for 2022 UZurich's pNorVβ Promoter and 2016 ETH's pNorV Promoter. 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.