Part:BBa_K1694003

Single-chain variable fragment (Anti-VEGF)

Introduction:

ScFv (Single-Chain Variable Fragment)

Fig.1 Single-chain variable fragment and A coding gene of scFv (anti-VEGF)

ScFv (single-chain variable fragment) is a fusion protein containing light (VL) and heavy (VH) variable domains connected by a short peptide linker (Fig. 1). The peptide linker (GGSSRSSSSGGGGSGGGG) is rich in glycine and serine which makes it flexible.

Features of scFv:

1. Specific：Though remove of the constant regions , scFv still maintain the specificity of the original immunoglobulin.

2. Efficient：ScFv is smaller than the entire antibody, so it place little stress for E. coli to express it

Vascular endothelial growth factor

1. VEGF (Vascular endothelial growth factor), a protein that can stimulates vasculogenesis and angiogenesis. Some cancers can overexpress VEGF, which will cause some vascular disease. Drug bevacizumab can inhibit VEGF and control or slow those diseases.

2. VEGF is a sub-family of growth factors, which comprises：VEGF-A, placenta growth factor (PGF), VEGF-B, VEGF-C and VEGF-D.

3. There are three types of VEGF receptors on the cell surface, and VEGFR-2 is one type of receptor which can mediate almost all of the known cellular responses to VEGF.

Bevacizumab

We selected the single chain variable fragments (scFv) of monoclonal antibodies Bevacizumab and named it Anti-VEGF. Bevacizumab is a monoclonal antibody which blocks angiogenesis by inhibiting vascular endothelial growth factor A (VEGF-A). VEGF-A stimulates angiogenesis in a variety of diseases, especially in cancer.

Mechanism:

When VEGF-A binds to VEGFR-2, it causes two VEGFR-2 to combine to form a dimer. This allows for signaling molecules to enter to the cell, bind to the receptor, and become activated. Then start the angiogenesis.
Bevacizumab can bind with VEGF released from tumor cell, block VEGFR to inhibit tumor angiogenesis, thereby cutting off the tumor's supplies and prevent tumor growth.

Fig. 2. (1.) Bevacizumab inhibit mechanism (2.) Dimerization mechanism

Reference:

Huston, J. S., Levinson, D., Mudgett-Hunter, M., Tai, M. S., Novotný, J., Margolies, M. N., … Crea, R. (1988). Protein engineering of antibody binding sites: recovery of specific activity in an anti-digoxin single-chain Fv analogue produced in Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America, 85(16), 5879–5883.
Los, M.; Roodhart, J. M. L.; Voest, E. E. (2007). "Target Practice: Lessons from Phase III Trials with Bevacizumab and Vatalanib in the Treatment of Advanced Colorectal Cancer". The Oncologist 12 (4): 443–50.
Dougher-Vermazen M, Hulmes JD, Böhlen P, Terman BI (November 1994). "Biological activity and phosphorylation sites of the bacterially expressed cytosolic domain of the KDR VEGF-receptor". Biochem. Biophys. Res. Commun. 205 (1): 728–38.

Experiment:

Fig.3. The PCR result of the scFv-VEGF. The DNA sequence length of scFv-VEGF are around 600~800 bp, so the PCR products should appear at 850～1050 bp.

After receiving the DNA sequences from the gene synthesis company, we recombined each scFv gene to PSB1C3 backbones and conducted a PCR experiment to check the size of each of the scFvs. The DNA sequence length of the scFvs are around 600~800 bp. In this PCR experiment, the scFv products size should be near at 850~1050 bp. The (Fig.3) showed the correct size of the scFv, and proved that we successful ligated the scFv sequence onto an ideal backbone.

Fig.4. scFv (anti-VEGF)

Application of the part

Fig.5Pcons+RBS+Lpp-OmpA-N+Anti-EGFR

Fig.6Pcons+RBS+RFP+Ter

Fig.7Pcons+RBS+GFP+Ter

Cell staining experiment:
After cloning the part of anti-VEGF, we were able to co-transform anti-VEGF with different fluorescence protein into our E. coli.
The next step was to prove that our co-transformed product have successfully displayed scFv of anti-VEGF and expressed fluorescence protein.
To prove this, we conducted the cell staining experiment by using the co-transformed E. coli to detect VEGF in the cancer cell line.


Fig.8 ~ Fig. 11 are our staining results：
Negative control:
There are red and green fluorescent anti-VEGF E. coli stick on the cell’s surfaces as the anti-VEGF probes on E. colis successfully detect and bind with VEGF.

Fig.8 As results,there is no green fluorescent E. coli stick on the cell’s surface as there is no specific scFv displayed around the E.coli.

Fig.9 As results,there is no red fluorescent E. coli stick on the cell’s surface as there is no specific scFv displayed around the E.coli.

Fig.10 There are green fluorescent anti-VEGF E. coli stick on the cell’s surfaces as the anti-VEGF probes on E. coli successfully detect and bind with VEGF.

Fig.11 There are red fluorescent anti-VEGF E. coli stick on the cell’s surfaces as the anti-VEGF probes on E. coli successfully detect and bind with VEGF.

cell staining experiment:

Fig.12Pcons+RBS+Lpp-OmpA-N+Anti-VEGF+RBS+RFP+Ter

Fig.13Pcons+RBS+Lpp-OmpA-N+Anti-VEGF+RBS+GFP+Ter

After creating the part of scFv and transforming them into our E. coli, we were going to prove that our detectors have successfully displayed scFv of anti-VEGF. To prove this, we have decided to undergo the cell staining experiment by using our E. coli to detect the VEGF in the SKOV-3 cancer cell lines. SKOV-3 is a kind of epithelial cell that expressed markers such as VEGF.

Below are our staining result：
Negative control:

Fig.14As results,there is no green fluorescent E. coli stick on the cell’s surface as there is no specific scFv displayed around the E. coli.

Fig.15As results,there is no red fluorescent E. coli stick on the cell’s surface as there is no specific scFv displayed around the E. coli.

Fig.16There are green fluorescent anti-VEGF E. coli stick on the cell’s surface as the anti-VEGF probes on E. coli successfully detect and bind with VEGF.

Fig.17There are red fluorescent anti-VEGF E. coli stick on the cell’s surface as the anti-VEGF probes on E. coli successfully detect and bind with VEGF.

Modeling

In the modeling part, we discover optimum protein production time by using the genetic algorithm in Matlab.
We want to characterize the actual kinetics of this Hill-function based model that accurately reflects protein production time.
When we have the simulated protein production rate, the graph of protein production versus time can be drawn. Thus, we get the optimum protein production time Compared with the simulated protein production rate of time, our experiment data quite fit the simulation.

Co-transform

Fig.18 From this graph, the orange curve is the simulated protein expression. The blue curve is our experimental data. By comparing the orange curve and the blue curve, the blue curve quite fit the simulation. The orange curve reaches peak after growing about 13 hours. Thus, we can know that the E. Cotector can have maximum efficiency at this point.

Fig.19 From this graph, the orange curve is the simulated protein expression. The blue curve is our experimental data. By comparing the orange curve and the blue curve, the blue curve quite fit the simulation. The orange curve reaches peak after growing about 9 hours.Thus, we can know that the E. Cotector can have maximum efficiency at this point.

Fig.20 From this graph, the orange curve is the simulated protein expression. The blue curve is our experimental data. By comparing the orange curve and the blue curve, the blue curve quite fit the simulation. The orange curve reaches peak after growing about 15 hours. Thus, we can know that the E. Cotector can have maximum efficiency at this point.

Fig.21 From this graph, the orange curve is the simulated protein expression. The blue curve is our experimental data. By comparing the orange curve and the blue curve, the blue curve quite fit the simulation. The orange curve reaches peak after growing about 12 hours. Thus, we can know that the E. Cotector can have maximum efficiency at this point.

Transform

Fig.22 From this graph, the orange curve is the simulated protein expression. The blue curve is our experimental data. By comparing the orange curve and the blue curve, the blue curve quite fit the simulation. The orange curve reaches peak after growing about 13 hours.Thus, we can know that the E. Cotector can have maximum efficiency at this point.

Fig.23 From this graph, the orange curve is the simulated protein expression. The blue curve is our experimental data. By comparing the orange curve and the blue curve, the blue curve quite fit the simulation. The orange curve reaches peak after growing about 12 hours. Thus, we can know that the E. Cotector can have maximum efficiency at this point.

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]