Revision as of 09:54, 19 September 2015

Single-chain variable fragment (Anti-EGFR)

Introduction:

ScFv (Single-Chain Variable Fragment)

Fig.1 Single-chain variable fragment

Fig. 2 A coding gene of scFv (anti-EGFR)

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 that the loading of production to E.coli is lower.

Epidermal growth factor receptor

1. EGFR is a transmembrane tyrosine kinase receptor that regulates the cell division and cell apoptosis.

2. EGFR is characterized by an extracellular ligand-binding domain, a transmembrane domain, and a cytoplasmic domain containing the tyrosine kinase region followed by a carboxyl-terminal tail with tyrosine autophosphorylation sites.

3. EGFR is overexpressed on the cell surfaces of various solid tumors.

4. Mutations in the gene encoding EGFR that lead to overexpression of this protein will lead to uncontrollable cells proliferate.

Cetuximab

We selected the single chain variable fragments (scFv) of monoclonal antibodies Cetuximab and named it Anti-EGFR. Cetuximab (International Nonproprietary Name) is an epidermal growth factor receptor (EGFR) inhibitor. It is a chimeric (mouse/human) monoclonal antibody and it specific binds to target antigen epidermal growth factor receptor (EGFR). With high affinity it can prevent ligand binding and activation of signal transduction.

Mechanism:

1. Cetuximab inhibition

When Cetuximab binds to the extracellular domain of the EGFR, it prevents the activation and subsequent dimerization of the receptor, inhibition in signal transduction and anti-proliferative effects. Moreover, this agent may inhibit EGFR-dependent primary tumor growth and metastasis.

2. EGFR activation

Firstly, the ligand binding at the extracellular domain of EGFR will lead to the occurance of active homo- or hetero-dimers. Dimerization induces the activation of the tyrosine kinase(TK) domain, leading to autophosphorylation of the receptors on multiple tyrosine residues. This phosphorylation triggers recruitment of a range of adaptor proteins, , followed by a series of intracellular signaling cascades that finally will affect the cell proliferation, apoptosis, invasion, metastasis, and angiogenesis.

Fig. 3. (1.) Cetuximab inhibit mechanism (2.) Dimerization mechanism

1.http://www.sciencedirect.com/science/article/pii/S0959804901002301
2.https://en.wikipedia.org/wiki/Cetuximab
3.http://www.ncbi.nlm.nih.gov/pubmed/22992668

Experiment:

Fig.4 The PCR result of the scFv-EGFR. The DNA sequence length of scFv-EGFR are around 700~800 bp, so the PCR products should appear at 900～1000 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. showed the correct size of the scFv (Anti-EGFR), and proved that we successful ligated the scFv sequence onto an ideal backbone.

Fig.5 The plate of scFv(Anti-EGFR)

Application of the part:

1. Co-transform

Fig.6 Pcons+RBS+Lpp-OmpA-N+Anti-EGFR

Fig.7 Pcons+RBS+RFP+Ter

Fig.8 Pcons+RBS+GFP+Ter

Cell staining experiment:
After cloning the part of anti-EGFR, we were able to co-transform anti-EGFR 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-EGFR and expressed fluorescence protein.
To prove this, we conducted the cell staining experiment by using the co-transformed E. coli to detect the EGFR in the cancer cell line.

Fig.9 ~ Fig. 12 are our staining results：
Negative control:
There are red and green fluorescent anti-EGFR E. coli stick on the cell’s surfaces as the anti-EGFR probes on E. colis successfully detect and bind with EGFR.

Fig.9 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.10 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.11 There are green fluorescent anti-EGFR E. colis stick on the cell’s surfaces as the anti-EGFR probes on E. colis successfully detect and bind with EGFR.

Fig.12 There are red fluorescent anti-EGFR E. colis stick on the cell’s surfaces as the anti-EGFR probes on E. colis successfully detect and bind with EGFR.

2. Single transform

cell staining experiment:

Fig.13 Pcons+RBS+Lpp-OmpA-N+Anti-EGFR+RBS+RFP+Ter

Fig.14 Pcons+RBS+Lpp-OmpA-N+Anti-EGFR+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-EGFR. To prove this, we have decided to undergo the cell staining experiment by using our E. coli to detect the EGFR in the SKOV-3 cancer cell lines. SKOV-3 is a kind of epithelial cell that expressed markers such as EGFR.

Fig.15~Fig.18 are our staining results：
Negative control:

Fig.15 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.16 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.17 There are green fluorescent anti-EGFR E. coli stick on the cell’s surface as the anti-EGFR probes on E. coli successfully detect and bind with EGFR.

Fig.18 There are green fluorescent anti-EGFR E. coli stick on the cell’s surface as the anti-EGFR probes on E. coli successfully detect and bind with EGFR.

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.

1. Co-transform

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 18 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 16 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 6 hours. Thus, we can know that the E. Cotector can have maximum efficiency at this point.

2. Single 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 17 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 17 hours. Thus, we can know that the E. Cotector can have maximum efficiency at this point.

Fig.24 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