Measurement

Introduction

Our project aims to engineer GI-tract bacteria to degrade gliadin, the immunogenic component of gluten, to act as a probiotic therapeutic for individuals with celiac disease. By utilizing constitutive promoters, signal peptides, and enzyme coding sequences, our system is designed to break down the gliadin peptides that cause adverse reactions in those with celiac disease. Accurate promoter selection and reliable measurements are crucial to validate the performance of our engineered system and ensure its efficacy.

To achieve this, we performed extensive measurements to characterize the promoter strength of a variety of broad-host-range antibiotic resistance genes such as GentR, TetR, CamR, SpecR, AmpR, and KanR. We compared these promoters to the well characterized Anderson series promoters (BBa_J23100, BBa_J23101, and BBa_J23114) that served as benchmarks for promoter activity, ensuring that our data is both accurate and reproducible. Comparison of our promoters' strengths to these controls helped us identify candidate promoters for further application in both gram-negative and gram-positive bacteria expression systems.

The iGEM Fluorescence Measurement Calibrants Kit¹ and our adapted calibration process were key in ensuring accuracy throughout our experiments. By using the kit's calibration tools for fluorescence and optical density readings, we were able to calibrate our microplate reader (Tecan Infinite 200). The calibration ensured consistency in our experiments and made our data accessible for replication by other teams. By adhering to standardized practices, we minimized variability and provided a reliable reference point for future studies, fostering collaboration in the field.

Contributions and Achievements in Measurement

In order to generate robust and high-quality data for our project's promoter characterization, our team conducted several key measurements:

1. Generated an OD660 to OD600 standard conversion curve for DH5α E. coli cells
2. Generated an OD660 to cell count standard conversion curve for the NanoCym 950nm monodisperse silica nanoparticles with the help of the interlab iGEM Calibration Protocol shown in the experiments page
3. Generated a Sulforhodamine 101 standard curve for microplate reader calibration using the iGEM Calibration Protocol for Sulforhodamine 101
4. Characterized several antibiotic promoter parts within a pIB184-derived shuttle vector using an RFP fluorescence assay

Calibration and Standardization

In scientific research, accurate, reproducible, and reliable results are crucial, and calibration plays a key role in achieving this consistency across experiments. By comparing our results to calibrated data, we ensure that our findings hold validity beyond our individual experiments.

We used the iGEM Fluorescence Measurement Calibrants Kit, which includes essential calibration tools for fluorescence and optical density readings¹ to our Tecan Infinite 200 Pro microplate reader. Sulforhodamine 101 was used to calibrate red fluorescence, and NanoCym 950nm monodisperse silica nanoparticles served as a reference for cell density. These standards allowed us to precisely calibrate our instruments, ensuring reliable and comparable data for future experiments.

OD660 to Cell Count Standard Calibration Curve

Challenges in Measuring OD600 when working with RFP Expressing Cells

A common method for quantifying the cell density of cultures is to measure the optical density (OD) of samples at a wavelength of 600 nm. While this setting works in most cases, problems can arise when measuring the cell density of RFP fluorescent cultures. The wavelength of 600 nm falls within the mScarlet's absorption spectrum². This is an issue, as absorption of light can increase cell density readings and cause lower fluorescence per cell readings³. To avoid obtaining skewed results of OD measurements using mScarlet in our project, we adjusted our protocol to measure OD at a wavelength of 660 nm that is beyond the typical mScarlet absorption range, ensuring more accurate fluorescence and cell density measurements.

Methods

OD660 to OD600 Conversion

We adapted the iGEM Calibration Protocol - Plate Reader Abs600 (OD) Calibration with Microsphere Particles and discussed with our primary investigators (PIs) to develop a protocol that allowed us to make measurements that could convert OD660 values to OD600 values ⁴. The detailed protocol and Excel sheets used for our calculations are presented in our experiments page under the Fluorescence Assays tab.

OD600 to Cell Count

We used the iGEM Calibration Protocol - Plate Reader Abs600 (OD) Calibration with Microsphere Particles to calibrate our Tecan Infinite 200 Pro microplate reader ⁴. We then used the Excel sheet linked in the protocol above to calculate the particle count per OD600, which is a suitable estimation for actual cell count (more explained in results), and create the standard calibration curves.

OD660 to Cell Count

Finally, using the data obtained from the conversion of OD660 to OD600 and OD600 to cell count, we were able make a standard conversion graph for OD660 to cell count. To do this, we first converted the OD660 values into OD600 values. Then we converted those OD600 values into cell count. Lastly, we directly graphed the OD660 values to cell count to make the final conversion curve between OD660 and cell count.

Results

OD660 to OD600

We first converted OD660 values to OD600 because our OD measurements are taken at 660 nm to avoid interference of OD600 readings from E. coli expressing RFP. After running our experiment, we plotted the OD660 values and OD600 values onto a graph to see if there was any relationship between the 2 values (Figure 1).

Figure 1. OD660 to OD600 Standard Conversion Curve. The equation for this linear relationship is y=0.9034x with y being the OPD660 value and x being the OD600 value.

We found a strong linear relationship between OD660 and OD600 shown by the straight slope with all the points perfectly aligned on the slope. Additionally, the high R² value of 0.9998 indicates a strong positive linear relationship between OD660 and OD600. This allows for an accurate and reliable conversion factor of 0.9034 between OD660 and OD660. Furthermore, the error bars in Figure 1 are relatively small other than the highest OD dilution. This means that the data points are consistent across different DH5a cultures and shows consistent and reliable data.

We later used this conversion curve in our experiments to standardize data measured at OD660, addressing RFP interference with OD600 readings. This allowed us to convert the data to OD600 and determine cell count to eventually analyze fluorescence per cell.

OD600 to Cell Count

After running the calibration assay on the Tecan Infinite 200 Pro microplate reader with the NanoCym 950nm monodisperse silica nanoparticles, we inputted our measurements into the Excel sheet which provided the particle standard curve (Figure 2).

Figure 2. Particle Standard Curve. A) Linear scale showing the relationship between particle count per 100 µL and OD600. B) Logarithmic scale visualizing the relationship between particle count per 100 µL and OD600, providing better clarity for smaller particle counts.

The standard curve, as seen in Figure 2A, demonstrates a linear relationship between particle count and OD600 values. This linear relationship allows us to accurately convert the OD600 readings to cell counts. Additionally, Figure 2B presents the same data plotted on a logarithmic scale, which helps to visualize lower particle concentrations. It highlights how the sensitivity of OD600 measurements decreases at lower particle counts around an OD600 value of 0.1. This indicates that our plate reader may not provide accurate readings for values at or below OD600 of 0.1.

Although particle count does value is not directly equivalent to cell count, an iGEM Interlab study found that “based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration”⁵. Thus, we used the particle count per OD600 value as our cell count per OD600. We found using the Excel sheet calculation from this protocol. that there are 4.44*10⁸ cells per OD600. This value allows us to create an equation that numerically converts OD600 to cell count: cell count = 4.44*10⁸ * OD600 value.

The conversion between OD600 and cell count is crucial, as it enables us to translate OD600 readings into cell counts, which is essential for analyzing fluorescence per cell in our promoter fluorescence assay. Additionally, calibration using NanoCym 950nm monodisperse silica particles helped ensure our data is comparable to other experiments.

OD660 to Cell Count

We used the two steps above to help standardize OD660 values to cell count for our Tecan Infinite 200 Pro microplate reader. By combining the two conversion steps, we were able to successfully make a conversion curve between OD660 and cell count that could standardize our fluorescence assay data needed for the characterization of our promoter parts. As can be seen from the standard conversion curve below, there is a clear linear relationship between OD660 and cell count (Figure 3).

Figure 3. OD660 to Cell Count Standard Conversion Curve. The equation is y=2*10⁹x with y representing the OD660 value and x is the cell count.

This conversion curve not only allowed us to standardize our fluorescence assay data for the characterization of promoter parts, but also provides a critical tool for future experiments involving red fluorescent proteins where OD600 readings may be compromised. By offering a reliable way to correlate OD660 with cell count, this method ensures more accurate measurements of bacterial cell density in fluorescence assays. Future researchers working with RFP-expressing constructs can apply this standardized approach to improve data consistency and reproducibility across diverse experimental setups.

Sulforhodamine 101 Standard Calibration Curve

In addition to calibrating optical density, it was crucial to standardize our fluorescence measurements to ensure consistency across experiments. Fluorescence readings can vary between instruments due to differences in sensitivity and settings, so calibration is essential. Using Sulforhodamine 101 calibration beads from the iGEM Measurement Kit, we created a standard curve that accounts for instrument variability, allowing us to align our fluorescence data with other experiments both within our team and across the broader iGEM community.

Methods

To calibrate our Tecan Infinite 200 Pro microplate reader for the analyzation of mScarlet fluorescence, we utilized the iGEM Calibration Protocol- Red Fluorescent Proteins in Plate Readersand adapted our protocol to measure OD at 660 nm⁶. It should be noted that this protocol references Sulforhodamine 101 as Texas Red, both of which are interchangeable between each other. We then imported our data into the provided Data Analysis Excel sheet to generate standard curves plotting µM concentrations of Sulforhodamine 101 against fluorescence intensity in arbitrary units.

Results

By using the Sulforhodamine 101 standard curve, we were able to successfully calibrate our Tecan Infinite 200 Pro microplate reader and assess for its threshold of accuracy. While majority of our data produced a linear relationship between µM concentrations and fluorescence intensity, we observed nonlinearity at fluorescence levels below 10 arbitrary units (Figure 4). With this, we were able to conclude a minimum fluorescence reading at which our microplate reader produces accurate and reliable results.

Figure 4. Standard Sulforhodamine 101 curve in both linear (A) and logarithmic (B) scales. The linear scale highlights the relationship between µM concentrations and fluorescence intensity in the higher fluorescence range, while the logarithmic scale provides better visualization of lower concentration values.

Furthermore, the Data Analysis Excel sheet helped us in calculating the molecules of equivalent Sulforhodamine 101 per arbitrary unit (MES/ a.u.) from its µM concentrations. With a value of 4.58 x 10¹⁰ MES/a.u., this gave us a standardization of fluorescence measured by our specific microplate reader. From this, we were then able to compile an equation to calculate the standardized fluorescence per cell when analyzing mScarlet fluorescence under the control our antibiotic resistance promoters (Figure 5):

Figure 5. General equation for calculating standardized fluorescence per cell.

Characterized Constitutive Antibiotic Resistance Promoter Library

Overview and Experimental Design

To characterize our antibiotic resistance promoters and identify the most promising candidates for broad-host range systems, we utilized fluorescence assays following the calibration and standardization of our microplate reader. We designed constructs containing the mScarlet reporter gene under the control of each of our 22 promoters, allowing us to measure fluorescence output as a proxy for promoter strength and activity.

Methods

Following the fluorescence assay protocol using iGEM calibrants outlined on our experiments page, we conducted a total of four trials using E. coli as the host organism. Each trial involved triplicate cultures containing constructs of antibiotic resistance (AbR) promoters, which were incubated for 24 hours. Following incubation, RFP fluorescence was measured using a gain setting of 75, with an excitation wavelength of 570 nm and an emission wavelength of 610 nm. These measurements provided a quantitative assessment of promoter strength by correlating the amount of fluorescence with the promoter's ability to drive gene expression.

Results

The results are shown below for a one time point mScarlet fluorescence assay. There were 4 trials conducted with triplicate cultures grown each trial. Then the total arbitrary units (a.u.) of fluorescence was converted into fluorescence per cell, shown in Figure 6.

To calculate the fluorescence per cell, we had to use our calibrations illustrated above. We first had to convert our OD660 values into cell count by using both OD660 to OD600 and OD600 to cell count conversions. Then, we used the Sulforhodamine 101 RFP fluorescence calibration. The calibration provided us the molecules of equivalent Sulforhodamine 101 (MES) value. Lastly, we used the equations shown in Figure 5 to calculate the fluorescence per cell.

We chose fluorescence per cell as the presentation unit because it provides a more reliable basis for comparison. Since measurement tools vary in sensitivity and settings for OD and fluorescence readings, using fluorescence per cell offers a standardized, calibrated metric. Additionally, this unit accounts for variations in growth rates and cell densities at the time of measurement, ensuring a more accurate representation of our data.

Figure 6. Results for antibiotic resistance promoter region fluorescence assay. The Anderson Promoter Series⁷ (J23100, J23101, J23114) served as controls with known and characterized strengths of 1, 0.7, and 0.1 respectively.

The Anderson Promoter Series promoters J23100 (strength of 1) and J23101 (strength of 0.7) had significantly higher fluorescence per cell compared to the rest of the promoters. The Anderson promoter J23114 with a strength of 0.1 had a fluorescence per cell much closer to the antibiotic resistance promoters. The full-length GentR and TetR promoters along with the KanR variant #2 promoter had very similar fluorescence per cell values as the Anderson promoter J23114 meaning that their relative strength is close to 0.1.

Among the antibiotic resistance promoters, the full-length CamR promoter showed the highest overall fluorescence per cell. Furthermore, there is a common theme that the full-length promoters tend to be stronger than truncated variants, likely because they retain critical regulatory motifs necessary for efficient RNA polymerase binding and transcription initiation⁸. The full-length GentR and TetR promoters had the next highest fluorescence per cell. However, it can be seen that the variants of the GentR promoter are barely fluorescent. This could be due to the absence of key transcriptional regulatory elements like the UP element or extended -10 motif, which stabilize the RNA polymerase and support more efficient transcription⁸.

Another highlight of the results shown in Figure 6 is that the Kan variant #2 promoter exhibited stronger fluorescence than both the full-length and Kan variant #1 constructs. The promoter calculator transcription rates between KanR variant #1 and KanR variant #2 correlate to their relative fluorescence strength to each other. Howeevr, as stated earlier, full-length promoters tend to have better fluorescence. The KanR variant #2 is stronger than the KanR full-length promoter could be due to the optimized -10 and -35 motifs, as well as the spacer length, which could improve transcription rates and reduce torsional stress on the RNA polymerase⁸. In contrast, both the full-length and variant SpecR promoters displayed little to no fluorescence per cell, with levels too low to even compare to our Anderson Promoter standards.

Although these fluorescence assays were performed in E. coli, the broad-host-range promoters are designed to function in various bacterial species. Future experiments will extend these tests to other bacterial strains like L. lactis. Additionally, we want to transform and test our promoters within other gram-positive organisms that colonize the human GI-tract to characterize these promoters in various bacterial species.

Measurement Award Considerations

Our project has been carefully designed and documented to meet the criteria for the iGEM Measurement Award, which focuses on ensuring that the parts and systems we develop are reproducible, reliable, and useful to the broader synthetic biology community. The following key aspects of our project reflect these guidelines:

1. Reproducibility: Our use of multiple trials with triplicates ensures that the function of each part is reproducible, with minimal variation across experiments.
2. Detailed Protocols: The methodologies we used to conduct the fluorescence assays, including the use of calibration standards, serial dilutions, and propagated uncertainty calculations, are clearly outlined. These protocols can be easily followed by other iGEM teams, ensuring that our measurements can be repeated and validated.
3. Usefulness to Other Projects: By characterizing a broad-host-range set of promoters, we provide valuable data that can be applied to a wide range of synthetic biology projects, particularly those looking to work with multiple bacterial species.
4. Controls and Calibration: We incorporated Anderson series promoters as controls to benchmark our experimental results. Additionally, we used the Sulforhodamine 101 red fluorescence calibrant to standardize our fluorescence measurements, ensuring accurate and reliable data across all trials.

Through these considerations, we have followed the best practices for measurement in synthetic biology, ensuring that our data is well-documented, repeatable, and useful to the community. Our project has made significant contributions to the understanding of promoter functionality, setting a strong foundation for future improvements and applications of these genetic parts.