Team:NYCU-Taipei/Model

model

Overview
In our modelling section, we want to construct three models for our natto it out project. There are two gene expression models for adhesive protein, FimH, and kill switch system, MazE and MazF. In addition, the optogenetic fusion protein docking model aims to confirm and asist the experimental design. There are three parts for discussing the project with computational modelling.

  • To OmpA-FimH protein expression model and the ahesive ability of FimH
  • To Optogenetic system protein-protein docking
  • To TetR, MazE, MazF gene expression and relation model
    • Modeling for Adhesive protein
    System Purpose and Summary
    The genetic system created by 2021 iGEM team NYCU Taipei was designed to adhere E. coli  Nissle 1917 in duodenum as an optogenetic system producing Nattokinase that breaks down blood clots and prevents deep vein thrombosis. In this part, we modeled the adhesive protein production as the function of time with MATLAB, and demonstrated the adhesion ability of our synbio product that supports our design.

    Figure 1. Adhesive fusion protein OmpA-FimH expression- and functional demonstration. (Created with BioRender)
    The model is broken up into two phases.

    Assuming T7 promoter is constitutive and lactose provided by duodenum environment is constantly sufficient for induction. The first phase begins when transcription is able to initiate and produce mRNA for OmpA-FimH fusion protein. This mRNA strand is then translated into the respective proteins and then inserts itself into the outer membrane of E.coli.

    The second phase describes the association and dissociation of the FimH adhesion domain that protrudes out of the cell surface to adhere to the epithelial cell in duodenum. A more detailed description would be carried out later on in this page.

    To build the model, we relied upon the following assumptions:

    For part one
  • The nutrition of growth is sufficient to maintain a steady nutrition uptake rate.
  • The cultivation environment is finite, and there is a stationary phase for the growth of E. coli .
  • These reactions take place in a single E. coli  cell of volume 7e-19 Liter.
  • The cell has excess RNA polymerase, ribosomes, and tRNA.
  • There is a maximum amount of plasmids for each plasmid count in the cell.
  • The transcription rate is 40 nuc/s [6].
  • The translation rate is 17 AA/s [6].
  • The mRNA degradation rate is 0.288 min-1, based on its degradation time of 5 minutes [7].
  • The protein degradation rate is half of the mRNA degradation rate at 0.144 min-1 [7]. This is because different proteins have different degradation rates, and they aren’t often found in literature. Since we know that proteins degrade far slower than mRNA, we’re using a conservative estimate that every protein whose degradation rate was not found in literature has a degradation rate of 10 minutes.


    • Modelling of constitutive T7 expression system

    Input: time (Nissle 1917 growth curve: bacterial quantity as a function of time N(t))
    Output: amount of OmpA_GS_FimH protein produced

    Calculation
    Transcription
    We used a transcription rate of 40 nuc/s to calculate the transcription rate of each mRNA strand. OmpA_FimH_mRNA: 1878 nucleotides -> 0.7825 mRNA / minute

    Translation
    We used a translation rate of 17 AA/s to calculate the translation rate of each protein. OmpA_FimH_protein: 626 AAs -> 0.6137 OmpA-FimH / minute

    Transcription rate through promoter strength
    According to Team Warsaw 2010’s measurement on Part:BBa_I719005, the absolute T7 promoter strength is approximately 41.8pg RNA/minute/ug substrate DNA, meaning that from 1 ug of the DNA containing pT7 regulated reporter DNA polymerase make around 41 pg of RNA in one minute. Double strand DNA encoding OmpA-FimH fusion protein is 1893 bps, equals approximately to 1893*607.4 (gram/mole) = 1149808 (gram/mole), and its single stranded mRNA transcribed with 574904 (gram/mole).

    According to the relation provided:
    We know in our case:
    And by conversion of weight to DNA amount:
    We got the rate of OmpA-FimH fusion protein mRNA transcription through pT7 as:
    Species
    Species Description
    DNA
    OmpA_FimH
    		 The DNA of OmpA-FimH fusion protein
    mRNA
    OmpA_FimH
    		 The mRNA of OmpA-FimH fusion protein
    OmpA-FimH OmpA-FimH adhesion domain fusion protein
    Process
    Reaction name Equation
    Transcription DNA ↔ DNA + mRNA
    Translation mRNA ↔ mRNA + OmpA_FimH
    mRNA degradation mRNA → null
    Protein degradation O_FimH → null
    Variables
    Variables Description Symbol Units
    X
    1
    		 mRNA
    OmpA_FimH
    		 mA nM
    X
    2
    		 OmpA_FimH protein A nM
    Parameters
    Parameter Description Value Units Source
    d
    mA
    		 mA degradation rate 0.288 1/min [3]
    d
    A
    		 A degradation rate 0.144 1/min [6]
    α T7 promoter hill constant NA NA Assuming
    k
    mA
    		 mA translation rate 4.37646e7 1/min [5]
    k
    A
    		 A degradation rate 0.6137 1/min [8]
    Equations
    Result
    Modelling of FimH adhesion ability to intestinal epithelial cells

    The second part demonstrates the least amount of FimH needed for theoretically functional anchor of an E. coli  cell to duodenum epithelium per unit area. FimH consists of two immunoglobulin-like domains: an N-terminal lectin domain that binds mannose ligand and a C-terminal pilin domain that anchors FimH into the fimbrial tip.[10] The two domains equilibrate between associated-state and separated-state accompanied by mannose binding.
    FimH lectin domain targets high mannose N-glycosylated glycoproteinA on duodenum epithelium. A 2000-fold higher affinity of the domain-separated state of FimH compared to its domain-associated state produced by tensile mechanical force is ligand-independent and consistent with a thermodynamic cycle.[11] The overall graphic reaction is described as the follows:
    Based on the energy landscape of biological bounds proposed by Evans et al., and according to Kramer’s reaction rate theory, if the multidimensional energy landscape of the unbinding pathway is projected onto the direction of force, the resulting one-dimensional landscape captures the essential properties of the system. [11]
    We thought this can express the unbinding rate as the following formula
    where A is the attempt frequency and ∆E representing energy barriers. The binding relation between mannose and FimH itself presents as a “two-state catch bond”, in which two relatively low energy states of FimH exist in the energy landscape with S state longer in the direction of force and having slower dissociation rate. Assume a first order reaction where A equals to 1, we can thus rewrite the formula as
    kS0 and kA0 are the rate coefficients for the bond dissociation via the slips and catch pathways in the absence of force.
    Since molar excess of accessible N-glycans over FimH on the cell surface favors monovalent FimH binding [12], we assume a FimH protein targets only one single mannose. The transitions between weak state (A-state with ligand bonded) and strong state (S-state with ligand bonded) of the catch bond of FimH protein thus became
    According to previous statement of FimH, the final equations describing the concentration of the A-state and S-state catch bond to mannose finally became
    References
    [1] Carolyn R. Honigford,Aktham Aburub,Hala M. Fadda, A Simulated Stomach Duodenum Model Predicting the Effect of Fluid Volume and Prandial Gastric Flow Patterns on Nifedipine Pharmacokinetics From Cosolvent-Based Capsules, Journal of Pharmaceutical Sciences, January 2019, DOI:https://doi.org/10.1016/j.xphs.2018.07.023

    [2] Y.Boada et al. , Host circuit interactions explain unexpected behavior of a gene circuit. Y.Boada et al. 2018.

    [3] Chih-Hung Wu, Hsiao-Ching Lee, Bor-Sen Chen, Robust synthetic gene network design via library-based search method, Bioinformatics, Volume 27, Issue 19, 1 October 2011, Pages 2700–2706

    [4] Politi, Nicolo' et al. “Half-life measurements of chemical inducers for recombinant gene expression.” Journal of biological engineering vol. 8,1 5. 1 Feb. 2014, doi:10.1186/1754-1611-8-5

    [5] Koš, M., & Tollervey, D. (2010). Yeast Pre-rRNA Processing and Modification Occur Cotranscriptionally. Molecular Cell, 37(6), 809–820. doi: 10.1016/j.molcel.2010.02.024

    [6] Young, R., & Bremer, H. (1976). Polypeptide-chain-elongation rate in Escherichia coli B/r as a function of growth rate. Biochemical Journal, 160(2), 185–194. doi: 7..1042/bj1600185

    [7] Politi, N., Pasotti, L., Zucca, S. et al. Half-life measurements of chemical inducers for recombinant gene expression. J Biol Eng 8, 5 (2014). https://doi.org/10.1186/1754-1611-8-5

    [8] Wilson CJ, Zhan H, Swint-Kruse L, Matthews KS. Ligand interactions with lactose repressor protein and the repressor-operator complex: the effects of ionization and oligomerization on binding. Biophys Chem. 2007 Mar;126(1-3):94-105. doi: 10.1016/j.bpc.2006.06.005. Epub 2006 Jun 18. PMID: 16860458.

    [9] Levandoski MM, Tsodikov OV, Frank DE, Melcher SE, Saecker RM, Record MT Jr. Cooperative and noncooperative effects in binding of the first and second plasmid Osym operators to a LacI tetramer: evidence for contributions of non-operator DNA binding by wrapping and looping. J Mol Biol. 1996 Aug 2;260(5):697-717. doi: 10.1006/jmbi.1996.0431. PMID: 8709149.

    [10] Isolde Le Trong, Pavel Aprikian, Brian A. Kidd, Manu Forero-Shelton, Veronika Tchesnokova, Ponni Rajagopal, Victoria Rodriguez, Gianluca Interlandi, Rachel Klevit, Viola Vogel, Ronald E. Stenkamp, Evgeni V. Sokurenko, Wendy E. Thomas. Structural Basis for Mechanical Force Regulation of the Adhesin FimH via Finger Trap-like Sheet Twisting. Cell. 2010.

    [11] Wendy E. Thomas, Viola Vogel, and Evgeni Sokurenko. Biophysics of Catch Bonds. Annual Review of Biophysics. 2008.

    [12] Maximilian M. Sauer, Roman P. Jakob, Thomas Luber, Fabia Canonica, Giulio Navarra, Beat Ernst, Carlo Unverzagt, Timm Maier, and Rudi Glockshuber. Binding of the bacterial adhesin FimH to its natural, multivalent high-mannose type glycan targets. JACS. 2018.

    [13] Yakovenko, Olga et al. FimH forms catch bonds that are enhanced by mechanical force due to allosteric regulation. The Journal of biological chemistry. 2008.


    Modeling for Optogenetics
    Introduction
    In our project of optogenetic control system, we need to construct several fusion proteins for the use of molecular tracing and FRET. In this case, knowing the structure of the protein we use and predicting the structure of fusion protein and protein-protein docking is essential for our design and design.

    Modeling BphP1 and Q-PAS1 Structure
    First of all, we need to build up the protein model of BphP1 and Q-PAS1 from sequence information in pKA-207I10. We use online servers for protein structure prediction: SWISS-Model [1] and trRosetta [2] to achieve this purpose. The results of calculation are visualized into two videos below. The structure model in PDB format. And the prediction 2D information is summarized in the PDF format and can be downloaded from the link at the bottom of the page.

    Video 1. BphP1 structure model generated from trRosetta
    Video 2. QPAS1 structure model generated from trRosetta
    Predicting Protein-Protein docking of BphP1 and Q-PAS1
    During photoconversion, BphP1 and Q-PAS1 will undergo heterodimerization. In this case, it is important to notice the structure hindrance, configuration transformation, and any other molecular interaction that might appear after the fusion of proteins and avoids aforementioned factors having negative effect on the protein-protein interaction of BphP1 and Q-PAS1. For this purpose, we used an online molecular docking prediction server: pyDockWEB [3] to construct the BphP1 and Q-PAS1 docking model.

    However, the diameter of the BphP1 structure model generated from trRosetta is too large for pyDockWEB to calculate. In this case, we decided to search for a template with good quality for BphP1 from the PDB database by SWISS-Model. After calculation, we found out that template:4gw9.1.A has 99.84% sequence identity. And the GMQE score is 0.74. The QMEANDisCo Global score is 0.81 ± 0.05. The QMEANDisCo Local Quality Estimate and QMEAN Z-Scores are shown at the three figures below.
    Fig 1. The information of BphP1 structure model from SWISS-Model.

    Overall, it seems to be good for us to use 4gw9.1.A as the receptor in protein-protein docking calculation. Thus, we take 4gw9.1.A and the QPAS1 model generated from trRoestta as receptor and ligand, respectively, to calculate molecular docking by pyDockWEB. The best docking model we got is showed at below:

    Video 3. Protein-protein docking model of 4gw9.1.A and QPAS1 from pKA207I10
    The Total score of this model from pyDockWEB is -40.979 kJ/mol. And other scores is showed at the figure below:
    Fig 2. Detailed scores information of the ranking 1 docking model of 4gw9.1.A and QPAS1 from pKA-207I10
    Modeling the fusion protein and protein-protein docking prediction
    In our project of optogenetic control system, we plan to construct QPAS1 and mCherry (the mCherry gene is also from pKA-207I10) fusion protein to cooperate with BphP1 as a FRET pair. Furthermore, in order to increase the specificity of the gene regulation, we plan to fusion LexA with Q-PAS1 to achieve this goal. So, we use trRosetta to predict the structure of these fusion proteins and use pyDockWEB and SwarmDock to predict the docking model of recombinant Q-PAS1 protein and BphP1.

    FRET Pair
    To test the protein-protein interaction of BphP1 and Q-PAS1, we decide to use BphP1 and mCherry as FRET pairs. In this case, we have to design a recombinant protein that fuses QPAS1 with mCherry. However, the fusion of mCherry on the N-terminal or C-terminal would have different influence on the PPI of BphP1 and QAPS1, and may have different distance to the BphP1 while mCherry and BphP1 should be close enough to allow the occurrence of FRET. Thus, we decide to use protein structure modeling and protein-protein docking to figure out which fusion strategy is better.

    We use trRosetta to generate two kinds of QPAS1_mCherry fusion protein mentioned above and visualized into the video below. The prediction 2D information is summarized in the PDF format and the protein structure models in PDB format can be downloaded from the link at the bottom of the page.

    Video 4. The structure model generated from trRosetta of QPAS1 fusion protein, which fused with mCherry on its C-terminal.
    Video 5. The structure model generated from trRosetta of QPAS1 fusion protein, which fused with mCherry on its N-terminal.
    For the convenience of elucidation, we named the QPAS1 fusion protein with mCherry on its C-terminal QPSA1_mCherry and the QPAS1 fusion protein with mCherry on its N-terminal mCherry _QPSA1.

    Then, we use pyDockWEB to calculate the docking model of 4gw9.1.A with QPAS1_mCherry and mCherry_QPAS1, respectively. The result is shown below:

    Video 6. Protein-protein docking model of 4gw9.1.A and QPAS1_mCherry.
    Video 7. Protein-protein docking model of 4gw9.1.A and mCherry_ QPAS1.
    The best docking model of 4gw9.1.A and QPAS1_mCherry from pyDockWEB got a total score of -38.687 kJ/mol. And the other scores are shown bellow:
    Fig 3. Detailed scores information of the ranking 1 docking model of 4gw9.1.A and QPAS1_mCherry.
    The best docking model of 4gw9.1.A and mCherry_QPAS1 from pyDockWEB got a total score of -27.496 kJ/mol. And the other scores are shown bellow:
    Fig 4. Detailed scores information of the ranking 1 docking model of 4gw9.1.A and mCherry_QPAS1.
    Fusion of LexA and QPAS1
    We also use trRosetta to predict the structure of the fusion protein of LexA and QPAS1. The result of calculation is visualized into the video below and can be downloaded here as PDB format. And the prediction 2D information is summarized in the PDF format and can be downloaded from the link at the bottom of the page.

    Video 8. The structure model of LexA_QPAS1 fusion protein generated from trRosetta.
    Implementation from the models
    Back to our goal of our model for optogenetic control system, we take the advantage of online protein structure prediction and protein-protein docking server to know more about our fusion protein, such as QPAS_mCherry and LexA_QPAS1.

    From our model of QPAS1 and mCherry fusion protein, we found that the strategy that fuse mCherry on the C-terminal of QPAS1 is better that the strategy that fuse mCherry on the N-terminal because the former has lower docking energy (-38.687 kJ/mol) than the latter (-27.496 kJ/mol). Also, from the visualized protein structure videos, the mCherry protein of the former is much closer than the latter, which theoretically results in higher FRET efficiency.

    From our model of BphP1, QPAS1, and LexA_QPAS1, we successfully constructed their structure by trRosetta with high estimated TM-score (0.759, 0.765, and 0.521, respectively).

    These data enable us to have a more detailed look in the protein structures and PPI while we are designing the project of the optogenetic system.

    Link for Open material of optogenetics model

    References
    [1] Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F.T., de Beer, T.A.P., Rempfer, C., Bordoli, L., Lepore, R., Schwede, T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296-W303 (2018).

    [2] Z Du, H Su, W Wang, L Ye, H Wei, Z Peng, I Anishchenko, D Baker, J Yang, The trRosetta server for fast and accurate protein structure prediction, Nature Protocols, in press (2021).

    [3] Jimenez-Garcia B., Pons C. and Fernandez-Recio J. "pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring". Bioinformatics (2013) 29(13):1698-1699.

    Modeling for Kill Switch
    System Purpose and Summary
    2021 iGEM team NYCU Taipei created a simple but multifunctional kill switch design. We selected MazE-MazF toxin-antitoxin genes to build our system. In this modeling, we took our kill switch design 1 as the modeling object.(see kill switch design(link)) We analyzed the possible interaction between MazE and MazF under the control of TetR and MazF with Matlab, and we pointed out the problem we and other iGEM teams may face while using the MazE-MazF toxin-antitoxin kill switch system. Also we analyzed the possibility to control the expression of MazE and MazF properly, which may lead the future iGEM team in a direction.
    Fig 1. The construction of the kill switch design 1.(Created with BioRender)
    According to the researches about MazE-MazF toxin-antitoxin system, MazF can detect and cleave the ACA sequence on the mRNA.(17) By this mean MazF can inactive the mRNA and block the Bacteria translation, leading to cell death.(16) Yet, another gene called MazE can combine with MazF to form a sandwich-like MazEF complex, and let the MazF inside the complex loses the function of killing cell.(18) On the other hand, MazE may be degraded by protein ClpAP(19).

    Before, many iGEM teams focused on the impact of ClpAP and MazEF complex on the interaction between MazE and MazF toxin-antitoxin genes, but they didn’t take it into consideration that MazF would cleave the mRNA ACA site. If the mRNA of the kill switch construct were cleaved, the balance between the expression of each protein may be totally different. Hence, this time we added the influence of MazF cleaving mRNA into our consideration.


    Modelling of the gene expression
    Assumption
    Assuming the kill switch design is working in the Nissle 1917 probiotics in duodenum environment, where the temperature is at 37 degree Celsius, and we assume that:
    1. In the duodenum, no endogenous L-arabinose exists.
    2. The gene can only be binded with one RNA Polymerase. So we can take the RNA Polymerase elongation rate as the Maximum transcription rate of the promoter.
    3. An E.coli  is with a radius about 1 micrometers, so we can calculate the volume of the cells.
    4. The initial concentration of MazE, MazF, and TetR is zero.

    Parameter
    parameter value description units reference
    k_pBad 3.72 RNA Polymerase elongation rate of pBad. kb/min [1]
    k_pTetR 2.79 RNA Polymerase elongation rate of pTetR kb/min [2]
    k_proConst 3.72 RNA Polymerase elongation rate of constitutive promoter J23106 kb/min [1]
    K_TetR 6 Dissociation constant of pTetR #m [3]
    c_TetR 4.5 Translation rate of TetR 1/min [4]
    c_MazE 73.2 Translation rate of MazE 1/min [5]
    c_MazF 0.54 Translation rate of MazF 1/min [5]
    l_pTetR 0.002 Leakage factor of pTetR none [3]
    l_pBad 0.01 Leakage factor of pBad none [6]
    l_ther 0.2 Leakage factor of RBS Thermometer none [7]
    n_TetR 3 Hills coefficient none [8]
    s_ther 1 or 0 Activation/Inactivation of RNA thermometer Binary assume
    s_pBad 1 or 0 Activation/Inactivation of promoter Bad Binary assume
    deg_F 0.5 Degradation rate by MazF with one ACA site 1/min assume
    deg_mRNA0 0.231 Endogenous degradation rate of mRNA 1/min [3]
    n_mTetR 12 number of ACA site on mRNA TetR none
    n_mMazE 2 number of ACA site on mRNA MazE none
    n_mMazF 9 number of ACA site on mRNA MazF none
    deg_TetR 0.1386 Degradation rate of TET 1/min [9]
    deg_MazE 0.0115 Degradation rate of MazE 1/min [10]
    deg_MazF 5.75*10^-4 Degradation rate of MazF 1/min [11]
    deg_MazE_F 0.1 Degradation rate of MazEF 1/min assume
    deg_ClpAP 0.1 Degradation rate of MazE by ClpAP 1/min [12]
    r_MazEF 0.01 MazE-MazF binding rate 1/min [13]
    r_MazE＿F 1 MazE-MazF unbinding rate 1/min [13]
    [L-ara0] 0 The endogenous concentration of L-arabinose in duodenum nM assume
    [TetR0] 0 The endogenous concentration of TetR in duodenum nM assume
    [MazF0] 0 The endogenous concentration of MazF in duodenum nM assume
    [ClpAP0] 10 Internal concentration of ClpAP nM [14]
    Calculation
    Transcription
    We converted the unit of transcription rate from kb/min to mRNA/min by dividing the nucleotides each promoter may transcript. According to our design, the mRNA TetR was transcripted under the influence of pBad, the mRNA MazE was transcripted under the influence of pTetR, and the mRNA MazE was s transcripted under the influence of constitutive promoter J23106. Hence, after we divided the RNA Polymerase elongation rate with the nucleotides of each construct, it turned out:
    mRNA_TetR: 26m/min
    mRNA_MazE: 8.38m/min
    mRNA_MazF: 3.72m/min

    Cell Volume
    We assumed an E. coli  is with a radius about 1 micrometers, so the volume of E.coli went:
    V=4/3*1^3*pi≈4.2*10^-15 L

    Concentration
    We converted the unit from the number to [nM] by dividing the cell volume. Assuming there were nth particles in the cell.
    n/(6*10^23)/(4.2*10^-15)=0.04n[nM]

    Method
    We generate ODE functions with three variables, and we can turn it into a picture with three dimensions. All we have to do is to input two scale ranges to the two of the three variables.

    ODE function
    1. the expression of mRNA TetR
    2. the expression of TetR
    3. the expression of mRNA MazE
    4. the expression of MazE
    5. the expression of mRNA MazF
    6. the expression of MazF
    7. the expression of MazEF
    Matlab result
    Fig 2. The concentration of mRNA TetR according to time and the amount of MazF change.
    Fig 3. The concentration of TetR according to time and the amount of MazF change.
    First, according to the function we’d solved, we could get (Fig. 2)The concentration of mRNA TetR according to time and the amount of MazF change and (Fig. 3)The concentration of TetR according to time and the amount of MazF change. Next we could use (Fig. 2) and (Fig. 3) to generate (Fig. 4)The concentration of TetR according to time and the amount of mRNA_TetR change.
    Fig 4. The concentration of TetR according to time and the amount of mRNA_TetR change.
    Fig 5. The concentration of mRNA MazE according to time and the amount of MazF change.
    Fig 6. The concentration of MazE according to time and the amount of MazF change.
    Fig 7. The concentration of MazE according to wider time and the amount of MazF change.
    Fig 8. The concentration of mRNA MazF according to time and the amount of MazF change.
    Following, we used the relationship between MazF, mRNA TetR, TetR, and mRNA MazF to conclude (Fig. 5)The concentration of mRNA MazE according to time and the amount of MazF change, and ,further, conclude (Fig. 6)The concentration of MazE according to time and the amount of MazF change. Yet, we found that in (Fig. 6) the value of each dimension didn’t come to a convergence state, so we enlarged the range of time and MazF concentration to 0-24000 and then it came up with (Fig. 7)The concentration of MazE according to wider time and the amount of MazF change.Then, we could get (Fig. 8)The concentration of mRNA MazF according to time and the amount of MazF change.
    Fig 9. The concentration of MazF according to MazE and mRNA MazF change.
    Fig 10. The concentration of MazE according to MazF and mRNA MazE change.
    Finally, according to the pictures we’d drawn above, we could have (Fig. 9)The concentration of MazF according to MazE and mRNA MazF change and (Fig. 10)The concentration of MazE according to MazF and mRNA MazE change.

    Results
    The mRNA expression under the pressure of MazF
    As we can see in (Fig. 2)(Fig. 5)(Fig. 9), the expression of mRNA TetR won’t exceed 0.05 nM, but the expression of mRNA MazE and mRNA MazF can reach 1.5 nM. This result corresponds to our expectation that without L-arabinose, the expression of mRNA TetR under the regulation of pBad will be inhibited. Also, the result indicates that once the concentration of MazF protein reaches 5nM, three kinds of mRNA are almost cleaved. What’s more, mRNA TetR declines sharply when the concentration of MazF rises from 0 to 5 nM. We speculate that because mRNA TetR has 12 ACA sites on the sequence, and mRNA MazE and mRNA MazF only have 2 ACA sites and 9 ACA sites respectively, so mRNA TetR is much easier to be cleaved. This result also can be interpreted that since mRNA MazE only has few ACA sites, MazE is less infected by the MazF and can serve as antitoxin to block MazF toxin.

    The MazE expression under the MazF and TetR regulation
    According to (Fig. 6)(Fig. 7), we can see that without the existence of MazF, the MazE won’t express. We speculate that it’s because the expression of TetR inhibits the expression of MazE. Since the MazE gene is under the control of TetR, once TetR is expressed too much, the Tet promoter regulating MazE will be inhibited, and MazE won’t be expressed. As we discussed above, mRNA TetR is much more vulnerable to MazF. Hence, even though MazF will cleave mRNA MazE, the extinction of MazF still helps the MazE to be expressed.
    Besides, (Fig. 6)(Fig. 7)shows that under our design, MazE will increases while MazF increases, which means MazE is capable to act as a antitoxin since even though the MazF cleave most mRNA, MazE can still be express and inactive the toxin of MazF. As we can see in (Fig. 10), the concentration of MazE will increase while mRNA MazE and MazF increase.

    The MazF expression under the MazE regulation
    According to (Fig. 9), the concentration of MazF drops dramatically under the extinction of MazE. MazE inactive MazF through combining itself and MazF and turning into MazEF, which can not cleave mRNA anymore and will be degraded soon. On the other hand, once the amount of MazE is not enough. MazF will lose control and lead to cell death.

    Improvement
    On the basis of the result, we find out that there are some problems we need to face. First, MazE will be inclined to TetR and MazF since mRNA TetR is vulnerable to MazF(there are 12 ACA sites on mRNA TetR). Under this circumstance, the regulation of MazE will be hard to control. Also it may be difficult for the wet lab to design the experience assay and debug the problem during construction . Hence, to avoid the situation, we think when using the MazE-MazF toxin-antitoxin system, we should avoid constructing the upper regulator like TetR. Only a construction to express MazE and a construction to express MazF is a better choice. To improve our kill switch system, we think maybe our kill switch design 2 with tandem promoters may have more potential. See our design page.

    References
    [1] Epshtein, V., & Nudler, E. (2003). Cooperation between RNA polymerase molecules in transcription elongation. Science (New York, N.Y.), 300(5620), 801–805. https://doi.org/10.1126/science.1083219

    [2] D. Braun, S. Basu and R. Weiss, "Parameter estimation for two synthetic gene networks: a case study," Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005, pp. v/769-v/772 Vol. 5, doi: 10.1109/ICASSP.2005.1416417.

    [3] https://2013.igem.org/Team:TU-Delft/KillSwitch

    [4] Team:Unesp Brazil/Model - 2018.igem.org

    [5] Erental, A., Sharon, I., & Engelberg-Kulka, H. (2012). Two Programmed Cell Death Systems in Escherichia coli: An Apoptotic-Like Death Is Inhibited by the mazEF-Mediated Death Pathway. PLoS Biology, 10(3), e1001281. doi:10.1371/journal.pbio.1001281

    [6] http://parts.igem.org/Part:BBa_K115002:Experience

    [7] http://parts.igem.org/Part:BBa_K115002:Experience

    [8] Kelly, C. L., Harris, A. W. K., Steel, H., Hancock, E. J., Heap, J. T., & Papachristodoulou, A. (2018). Synthetic negative feedback circuits using engineered small RNAs. Nucleic Acids Research. doi:10.1093/nar/gky828

    [9] Tuttle, L. M., Salis, H., Tomshine, J., & Kaznessis, Y. N. (2005). Model-Driven Designs of an Oscillating Gene Network. Biophysical Journal, 89(6), 3873–3883. doi:10.1529/biophysj.105.064204

    [10] Donegan, N. P., Thompson, E. T., Fu, Z., & Cheung, A. L. (2010). Proteolytic regulation of toxin-antitoxin systems by ClpPC in Staphylococcus aureus. Journal of bacteriology, 192(5), 1416–1422. https://doi.org/10.1128/JB.00233-09

    [11] Aizenman, E., Engelberg-Kulka, H., & Glaser, G. (1996). An Escherichia coli chromosomal "addiction module" regulated by guanosine [corrected] 3',5'-bispyrophosphate: a model for programmed bacterial cell death. Proceedings of the National Academy of Sciences of the United States of America, 93(12), 6059–6063. https://doi.org/10.1073/pnas.93.12.6059

    [12] Donegan, N. P., Thompson, E. T., Fu, Z., & Cheung, A. L. (2010). Proteolytic regulation of toxin-antitoxin systems by ClpPC in Staphylococcus aureus. Journal of bacteriology, 192(5), 1416–1422. https://doi.org/10.1128/JB.00233-09

    [13] Aizenman, E., Engelberg-Kulka, H., & Glaser, G. (1996). An Escherichia coli chromosomal “addiction module” regulated by guanosine [corrected] 3’,5’-bispyrophosphate: a model for programmed bacterial cell death. Proceedings of the National Academy of Sciences, 93(12), 6059–6063. doi:10.1073/pnas.93.12.6059

    [14] Erental, A., Sharon, I., & Engelberg-Kulka, H. (2012). Two Programmed Cell Death Systems in Escherichia coli: An Apoptotic-Like Death Is Inhibited by the mazEF-Mediated Death Pathway. PLoS Biology, 10(3), e1001281. doi:10.1371/journal.pbio.1001281

    [15] Nikolic, N., Bergmiller, T., Vandervelde, A., Albanese, T. G., Gelens, L., & Moll, I. (2018). Autoregulation of mazEF expression underlies growth heterogeneity in bacterial populations. Nucleic acids research, 46(6), 2918–2931. https://doi.org/10.1093/nar/gky079

    [16] Zhang Y, Zhang J, Hoeflich KP, Ikura M, Qing G, Inouye M. MazF cleaves cellular mRNAs specifically at ACA to block protein synthesis in Escherichia coli. Mol Cell. 2003 Oct;12(4):913-23. doi: 10.1016/s1097-2765(03)00402-7. PMID: 14580342.

    [17] Katsuhiko Kamada,Fumio Hanaoka,Stephen K. Burley, Crystal Structure of the MazE/MazF Complex Molecular Bases of Antidote-Toxin Recognition, Molecular Cell, 2003, DOI:https://doi.org/10.1016/S1097-2765(03)00097-2

    [18] Gottesman, S et al. “The ClpXP and ClpAP proteases degrade proteins with carboxy-terminal peptide tails added by the SsrA-tagging system.” Genes & development vol. 12,9 (1998): 1338-47. doi:10.1101/gad.12.9.1338

    [19] Nikolic, Nela et al. “Autoregulation of mazEF expression underlies growth heterogeneity in bacterial populations.” Nucleic acids research vol. 46,6 (2018): 2918-2931. doi:10.1093/nar/gky079

    CONTACT US
    FOLLOW US
    Authored and maintained by Team NYCU-Taipei 2021.