Part:BBa_K4235001

Polyhedrin Promoter

This promoter is part of the vector BBa_K4235002 and the miniatt-Tn7 transposon segment BBa_K4235010. It is also a part of Protein S expression circuit BBa_K4235007.

Usage and Biology

Normally, the polyhedrin promoter drives the expression of the polyhedrin protein in baculoviruses, which is critical to maintain the pathogenicity and protect the virions from harsh environments. The polyhedrin protein matrix breaks down in alkaline conditions, disrupting the crystal-like viral capsule and releasing viral particles in the environment.

Given the importance of the polyhedrin promoter, it is no surprise that its expression is driven by one of the most powerful promoters found in baculoviruses. This makes the polyhedrin promoter highly desirable for expression of heterologous genes. The Baculovirus expression systems frequently exploit the polyhedrin promoter for large scale production of recombinant proteins. While many details of the exact mechanism of this promoter are unknown, below are some of the functionally relevant parts of the promoter:

• A TAAG sequence is found in the polyhedrin promoter, which is essential for its identification by the baculovirus RNA polymerase. Mutations in the TAAG motif are known to abolish expression in reporter genes.

• CAGT sequence is conserved in nearly all baculovirus promoters and functions as a transcription initiation site. A CAGT sequence is present downstream of the transcriptional start site of the polyhedrin promoter.

• Burst sequence: An AT rich sequence present between the TAAG transcription start site and the ATG translation initiation site of the p10 and polyhedrin promoters. Assists in the burst transcription of viral genes in the very late phase of infection.

• PPBP (polyhedrin promoter binding protein): PPBP is a transcription factor which binds to the AATAAATAAGTATT sequence which contains the transcription initiation site (TAAG) in p10 and polyhedrin promoters.

• VLF-1: This transcription factor plays a regulatory role in very late phase gene expression by binding to the burst sequence found in the p10 and the polyhedrin promoters. Overexpressing VLF-1 and increasing the number of burst sequences results in an enhanced expression of the downstream gene.

Sequence and Features

Assembly Compatibility:

• 10
  COMPATIBLE WITH RFC[10]
• 12
  COMPATIBLE WITH RFC[12]
• 21
  COMPATIBLE WITH RFC[21]
• 23
  COMPATIBLE WITH RFC[23]
• 25
  COMPATIBLE WITH RFC[25]
• 1000
  COMPATIBLE WITH RFC[1000]

Mathematical modeling

Many biological projects require models that can accurately represent and predict multicomponent, temporally evolving, dynamic systems. Differential equation models are often used for this purpose. This method models the interaction of molecules in the form of rate equations. The system is represented by a system of ordinary differential equations (ODEs), which quantify the interaction between different molecules (i.e. DNA, mRNA or protein), by using the law of mass action. These equations include terms relating to the binding of transcription factors and RNA polymerase to DNA, interactions between transcription factors, mRNA translation rate, and mRNA and protein degradation rates (Ay and Arnosti 2011), for example. In order to accomplish this, knowledge about the system components and structure is required.

We used MATLAB simulations to model our genetic circuits. We used a system of ODEs derived for both constitutive and regulatory genetic circuits(PPBP-Polyhedrin promoter interactions) for our SF9 expression system. We were able to input our system of ODE’s into MATLAB to predict our steady state concentrations prior to starting wet lab in order to determine the process that would yield more favorable results. Those plots can be found below:

SF9 constitutive gene circuit model:

(1.) Protein vs time:

Figure 1:Plot showing Protein production over time

(2.) mRNA vs time:

Figure 2: Plot showing mRNA production over time

(3.) Effects of altering DNA concentration on protein production:

Figure 3: Plot showing effects of altering initial DNA concentrations on protein production

SF9 Regulatory gene circuit model:

The majority of recombinant protein expression is driven by the most powerful baculovirus promoter, polyhedrin, which is active in the late and very late stages of infection. Several studies have been done to study the mechanism of the polyhedrin promoter to better characterize its transcription activity. A host secreted transcription factor, polyhedrin promoter binding protein (PPBP) is known to bind a specific sequence on the polyhedrin promoter with extremely high affinity and specificity and plays a major role in the level of transcription through the polyhedrin promoter. It is established that the PPBP binds to the minor groove of DNA, interacting with the polyhedrin promoter sequence and forming a complex. Further studying the PPBP-DNA interactions and manipulating the concentration of PPBP in host SF9 cells can have a significant impact on the yield of recombinant proteins.

Here, we modeled the interaction between the PPBP and the polyhedrin promoter and its effect on the rate of production of mRNA and the resulting protein S. We were unable to find an established literature value for the concentration/number of molecules of the PPBP in SF9 cells, therefore, we estimated the concentration of PPBP based on some commonly found transcription factors in Drosophila Melanogaster. The plots of MATLAB simulations are listed below:

(1.) mRNA vs protein S over time:

Figure 4: 3D plot showing protein S as a function of mRNA and time

(2.) mRNA vs time:

Figure 5: PLot showing mRNA production over time

(3.) Protein vs time:

Figure 6: Plot showing protein S production over time

Information on parameter estimation, rate constants and analysis can be found on our model wiki page.
https://2022.igem.wiki/stony-brook/model

Summary: Modeling the sf9 regulatory gene circuit vs the sf9 constitutive gene circuit provides insight into the role of the Polyhedrin promoter binding protein and its effect on driving robust expression through the polyhedrin promoter. This is evident by the difference in the final steady-state protein values plotted by the regulatory vs the constitutive model. However, the exact dynamics of PPBP and polyhedrin promoter interactions are not well understood, it was difficult for us to find and approximate the literature values for initial steady state concentrations. Future models and experiments could seek to gain a better understanding of these values in order to make our simulation more accurate and improve our model.