Team:Lambert GA/Contribution
BACKGROUND
The most essential nutrients to plant function are phosphorus and nitrogen, as well as trace concentrations of other elements [1]. Fertilizers are used to concentrate such nutrients and allow plants to slowly absorb them through their roots. In aquaponics systems, nitrogen is produced via nitrogenous fish waste and phosphorus is sourced from the addition of phosphate rocks. However, the method to determine phosphorus and nitrogen concentrations in the circulating water is inefficient and costly. To combat these obstacles that aquaponics farmers face, Lambert iGEM characterized an extracellular phosphate biosensor, part BBa_K2447000, in E. coli to provide a more accurate, timely, and cost-effective method of detecting and monitoring nutrients.
PART BBa_K2447000
Part BBa_K2447000, an extracellular phosphate sensor with GFP reporter, was created by NUS Singapore iGEM 2017 as an improvement to the part BBa_K116404, an existing external phosphate sensing reporter. While NYMU Taipei 2008 initially created part BBa_K116404 with a medium-strength RBS (BBa_B0032), NUS Singapore 2017 increased the sensitivity of the phosphate sensor by replacing BBa_B0032 with a stronger RBS, BBa_B0034.
Figure 1. Part design construct of BBa_K2447000.
As shown in Figure 1, BBa_K2447000 consists of a PhoB-activated promoter (BBa_K116401), strong RBS (BBa_B0034), GFP reporter (BBa_E0040), and double terminator (BBa_B0015).
The promoter BBa_K116401 is activated by the PhoB transcription factor from the Pho Regulon signaling pathway that exists naturally in E. coli; the pathway responds to extracellular inorganic phosphate levels and transcribes regulatory genes [2]. In part BBa_K2447000, natural Pho Regulon genes of the Pho signaling pathway are replaced by the GFP reporter BBa_E0040 so that activation of the promoter via binding of phosphorylated PhoB will result in expression of GFP.
In conditions of high extracellular phosphate, PhoB is inactive and unable to activate the transcription of the downstream GFP reporter. On the contrary, in conditions of low extracellular phosphate, PhoB is active, resulting in transcription of the downstream GFP reporter.
Figure 2. Diagram of part BBa_2447000 under different levels of extracellular phosphate.
According to literature, the Pho Regulon signaling pathway has a threshold of 4uM of extracellular phosphate [3]. In other words, extracellular phosphate concentrations greater than 4uM should result in no transcription of GFP while concentrations less than 4uM should result in transcription of GFP.
RESEARCH
PHO REGULON SIGNALING PATHWAY
Before characterizing part BBa_K2447000, Lambert iGEM researched the Pho Regulon in depth, learning about the signaling pathway’s reactions. The signaling pathway, shown below, is initiated once Pi (inorganic phosphate) molecules enter the cell by passing through PhoE porin proteins in the outer membrane. In the periplasmic space, Pi binds to the protein PstS, which carries Pi to the PstABC transporter complex located on the inner membrane. The PstABC complex consists of the PstA/C transmembrane channel and the permease PstB, which phosphorylates PstA/C to actively transport Pi across the inner membrane. Different levels of Pi within the cytoplasm will then bind to the accessory protein PhoU and consequently activate or deactivate transcription of Pho Regulon genes.
Figure 3. Diagram of the Pho Regulon signaling pathway.
Research has shown that higher levels of Pi in the cytoplasm deactivate the transcription of Pho Regulon genes [4]. When Pi is available in the cytoplasm, it binds to the accessory PhoU protein. The bound PhoU-Pi complex inhibits the PstB permease, preventing PstA/C from further transporting Pi into the cytoplasm. The same PhoU-Pi complex also inhibits the histidine kinase PhoR by repressing its autophosphorylation. Through this process, PhoR is unable to phosphorylate, or activate, the transcription factor PhoB. PhoB is inactive, and therefore unable to activate transcription of the Pho Regulon, so the genes of the Pho Regulon are not expressed. Over time, Pi dissociates from PhoU - therefore restarting the cycle.
On the other hand, lower levels of Pi limit the accessory PhoU protein from binding to Pi, and PhoU is therefore unable to inhibit the permease PstB. This allows Pi to enter the cytoplasm through the transmembrane channel PstA/C. Because of the initial lower levels of Pi, there is no PhoU-Pi complex to inhibit the histidine kinase PhoR. PhoR autophosphorylation occurs, and then PhoR phosphorylates the PhoB transcription factor. Once activated, PhoB binds to the promoter region of the Pho Regulon and transcription of genes within the regulon is initiated; these genes translate into the various proteins involved in the signaling pathway.
Essentially, lower levels of extracellular phosphate result in expression of the Pho Regulon genes, and higher levels lead to less expression of those genes.
MODEL
Next, Lambert iGEM developed a deterministic Ordinary Differential Equation model to simulate the biosensor’s activity over time and explore whether the 4uM threshold limited the biosensor.
Lambert iGEM’s model creates ordinary differential equations of all the Pho Regulon signaling pathway’s reactions in a single E. coli cell and evaluates them with an initial input of extracellular phosphate concentration to predict GFP expression.
For building the model, the team followed the process outlined in A Tutorial on Mathematical Modeling of Biological Signaling Pathways [5]. First, using MATLAB Simbiology software, Lambert iGEM diagrammed each reaction of the signaling pathway from the entering of Pi into the cell to final GFP expression.
Figure 4. Diagram of the reactions of the Pho Regulon signaling pathway in a single E. coli cell, created in MATLAB Simbiology software.
Twenty-two biochemical reactions were derived, initial values of species were inputted, ODE equations were generated, and rate constants were either inputted or estimated based on existing characterization data of BBa_K2447000. With Simbiology Model Analyzer, GFP expression was simulated in response to varying inputs of phosphate concentration.
Figure 5. Graph of the model’s simulated GFP expression to phosphate concentration.
The model confirms that phosphate concentrations above the 4uM threshold described by The Pho regulon and the pathogenesis of Escherichia coli [3] will still result in levels of GFP expression. Therefore, cloning part BBa_K2447000 for use as a phosphate biosensor would be sensitive enough to detect the range of phosphate concentrations commonly found in aquaponics systems: 0uM to 100uM.
The model also follows the same log relationship as the original characterization data from 2017 NUS Singapore, shown below:
Figure 6. Graph of the original characterization data from 2017 NUS Singapore.
Compared to NUS Singapore, Lambert iGEM’s model estimates smaller values of GFP expression because the model represents the GFP expression of one single E. coli cell, whereas NUS Singapore’s data is from a culture of cells.
With a completed model, Lambert iGEM began characterization of BBa_K2447000 in E. coli.
CHARACTERIZATION
EXPERIMENTAL DESIGN
Through research, Lambert iGEM discovered that typical phosphate levels in aquaponics systems range from 10ppm to 40ppm [1]. Using dimensional analysis, the team concluded that maintaining a phosphate concentration between 50uM and 100uM is ideal for plants and fish to coexist. To add detailed characterization of part BBa_K2447000 on phosphate concentrations specifically targeted for use in aquaponics systems, Lambert iGEM tested GFP expression of the phosphate sensor on extracellular phosphate levels from 0uM to 100uM in 20uM intervals.
EXPERIMENTAL PROCEDURE
After successfully cloning BioBrick K2447000, Lambert iGEM began characterization by growing biosensor cells in chloramphenicol LB for 24 hours and later diluting them to an OD600 value of 0.4. Then, the cells were pelleted and resuspended into MOPS media, which has minimal phosphate concentration relative to LB. To the 5 mL resuspension, the team added different phosphate concentrations between 0uM to 100uM and waited 3 hours for GFP to be expressed. Lambert iGEM used a plate reader from Styczynski Research Group at Georgia Institute of Technology to measure and analyze the GFP expression.
CHARACTERIZATION CURVE
Figure 7. Characterization curve showing the relationship between phosphate concentrations between 0 to 100uM and fluorescence/OD600 measured by a plate reader.
Figure 8. Prediction of relationship between GFP expression and phosphate concentrations ranging from 0 to 100uM made by deterministic ODE model.
Using data from the plate reader, Lambert iGEM created a characterization curve showing the relationship between phosphate concentrations and fluorescence/OD600. For phosphate concentrations ranging from 0uM to 80uM, the decreasing trend in fluorescence/OD600 closely resembled Lambert iGEM’s ODE model prediction, shown in Figure 8. The fluorescence value for the 100uM phosphate concentration did not match the prediction from the model because the phosphate media was diluted improperly, causing its measured GFP expression to be higher than expected. Due to time constraints in the lab, the team was not able to conduct further testing and decided to use the characterization data for only 0 to 80uM of phosphate. Next season, Lambert iGEM will continue characterization of BBa_K2447000 with an even greater number of phosphate concentrations.
REFERENCES
[1] Storey, N. (2017, December 13). The Most Important Things To Know About Phosphorus. Retrieved October 03, 2020, from https://university.upstartfarmers.com/blog/most-important-things-about-phosphorus.
[2] Santos-Beneit, F. (2015). The Pho regulon: a huge regulatory network in bacteria. Frontiers in Microbiology, 6. https://doi:10.3389/fmicb.2015.00402.
[3] Crépin, S., Chekabab, S., Bihan, G. L., Bertrand, N., Dozois, C. M., & Harel, J. (2011). The Pho regulon and the pathogenesis of Escherichia coli. Veterinary Microbiology, 153(1-2), 82-88. https://doi:10.1016/j.vetmic.2011.05.043.
[4] Uluşeker, C., Torres-Bacete, J., García, J. L., Hanczyc, M. M., Nogales, J., & Kahramanoğulları, O. (2019). Quantifying dynamic mechanisms of auto-regulation in Escherichia coli with synthetic promoter in response to varying external phosphate levels. Scientific Reports, 9(1). https://doi:10.1038/s41598-018-38223-w.
[5] Zi, Z. (2012). A Tutorial on Mathematical Modeling of Biological Signaling Pathways. Methods in Molecular Biology Computational Modeling of Signaling Networks, 41-51. https://doi:10.1007/978-1-61779-833-7_3.