Team:William and Mary/Measurement

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Quantitative Measurement of Orthogonality Assessment in the Literature RNA-Seq Data Meta-Analysis Mathematical Model Circuit Measurements RNA-Seq Data Using Additional Circuits References

Measurement

Measurement was a fundamental aspect of every aspect of our project from design through testing of the circuits.

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Quantitative Measurement of Orthogonality Assessment in the Literature

At the very beginning of our project, we were surprised that assessment of orthogonality was so uncommon in the literature. Therefore we conducted “measurement” of orthogonality assessment in both published papers in ACS Synthetic Biology and in iGEM projects. In a review of all ACS Synthetic Biology journal publications in 2020, we found approximately 20% mentioned orthogonality. Related terms 'burden' and 'toxicity' were mentioned in approximately 20% and 81% of papers, respectively. Beyond burden and toxicity, orthogonality was measured in approximately 9.55% of papers, and modeled in approximately 1.82%. In the general literature, we noticed parts were often assumed to be orthogonal without assessment. Out of 190 iGEM projects conducted in 2019, approximately 33% mentioned orthogonality or a synonym, and approximately 11% assessed orthogonality. In a survey released to 2021 iGEM teams, approximately 21.1% reported having no concern at all for orthogonality, crosstalk, or burden, and approximately 100% of teams responded “no” to whether they had measured orthogonality. These data confirmed the need for tools to assess orthogonality of circuits.

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RNA-Seq Data Meta-Analysis

In order to determine the most appropriate genes to assess orthogonality, we performed a meta-analysis and comparison of all extant RNA-Seq data that compared the impact of circuits on their hosts. We determined which genes were most differentially expressed and which were common to multiple circuits. These "measurements" are available on the Design Page.

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Mathematical Model

We developed a mathematical model in which output from our circuits is used as input into the model; we performed an extensive and systematic search for accurate quantitative parameter measurements for this model. These are described on the Model Page.

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Circuit Measurements

• We began by quantitatively confirming the functionality of each circuit via Sanger sequencing and by a functional experiment assessing fluorescence measured against a negative and positive control.
• We confirmed the functionality of test circuit pBbB8k-csg-amylase using a Congo Red spin-down assay and the results of this assay can be found on the Results Page.
• To obtain the data for input into our mathematical model, we performed an extensive series of measurements including measurement of: media alone, untransformed cells, sensor circuit alone, sensor circuit + uninduced test circuit, and sensor circuit + induced test circuit.
• WM21_013 was included as a positive control. LB and Untransformed cells were used as negative controls. In addition we used both induced and uninduced circuits to further control our experiment.
• In order to obtain dynamic data at a number of different time points, we conducted both OD and fluorescent measurements at T=-1 (before subculturing cells), T=0, T=1, T=6, T=12, T=24, and T=48.
• Using a plate reader, we consistently performed three replicates for every sample. In addition, although still too low of an N, we performed n=3 for each experiment to obtain some reproducibility information.

While independent calibrants were not available to us (we read Dr. Jake Beal’s papers), we performed multiple positive and negative controls and also conducted RNA-seq to complement other measurements.

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RNA-Seq Data Using Additional Circuits

To obtain additional measurements/data for our model, we performed RNA-Seq on three different plasmids systems: TS_pluxlac and pCDF_LuxR (Barbier et al., 2020), pBbB8k-csg-amylase (Birnbaum et al., 2021), and pDawn-AG43 (Jin et al., 2018). Each of these systems was tested with three replicates. The goal of these experiments was to determine differential gene expression of the host with and without an additional circuit. For the TS_pluxlac and pCDF_LuxR system, a culture co-transformed with these plasmids was grown overnight. The next morning the culture was induced with AHL to trigger the toggle switch action of plasmids. Five hours after induction, the culture was diluted into a flask containing fresh media with the same AHL. At both the five and ten hour time points, aliquots were removed from the sample, spun down in a centrifuge, and frozen in liquid nitrogen after removing the supernatant. In parallel, a flask of untransformed cells was also grown in the same way and aliquots were taken at the five and ten hour time intervals.

For the pBbB8k-csg-amylase plasmid (Birnbaum et al. 2021) -- an arabinose-inducible, curli fiber-producing plasmid -- a Congo Red spin down assay was performed based on the protocol from Harvard iGEM 2017 (Protocols) to confirm the functionality of the circuit. The results of this assay can be found on our Results page. Cultures transformed with this plasmid were grown overnight in LB containing arabinose at a concentration of 250 µM. 24 and 48 hours after induction, a Congo Red spin down assay was performed to confirm curli fiber production in the presence of arabinose. Aliquots of cells were taken 48 hours after induction. These aliquots were spun down at 13,300 RPM for 1 minute and frozen in liquid nitrogen after decanting the supernatant. Samples were also taken of untransformed host cells after 48 hours of growth.

For the pDawn-AG43 plasmid -- a biofilm production plasmid with a promoter induced by blue light -- transformed cells were grown to the beginning of their exponential growth phases in as much darkness as possible, then grown overnight from liquid subcultures (in 5 mL plates of minimal media) in blue light. After 16 hours, the growth media was rinsed from the planktonic cells, and a crystal violet assay was performed to confirm biofilm production on induced cells and a lack of biofilm on a no-blue light control. Unstained planktonic cells were removed from plates and made into aliquots. These aliquots were spun down and frozen in liquid nitrogen after removing the supernatant. Samples were also taken of host cells without the additional circuit at the same time points.

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References

Barbier, I., Perez-Carrasco, R., & Schaerli, Y. (2020). Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch. Molecular Systems Biology. 16. https://doi.org/10.15252/msb.20199361

Birnbaum, D., Manjula-Basavanna, A., Kan, A., Tardy, B., & Joshi, N. (2021). Hybrid Living Capsules Autonomously Produced by Engineered Bacteria. Advanced Science. 8(11), https://doi.org/10.1002/advs.202004699

Jin, X., & Riedel-Kruse, I. (2018). Biofilm Lithography enables high-resolution cell patterning via optogenetic adhesin expression. 115(15). doi: 10.1073/pnas.1720676115

Protocols. Harvard OPTI POLY. https://2017.igem.org/Team:Harvard/Protocols.