Revision as of 03:43, 19 September 2015

Single-chain variable fragment (Anti-HER2)

We selected the single chain variable fragments (scFv) of monoclonal antibodies Trastuzumab

Introduction:

ScFv (Single-Chain Variable Fragment)

Fig.1 Single-chain variable fragment

Fig. 2 A coding gene of scFv (anti-HER2)

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.

HER2

Human epidermal growth factor 2, abbreviated as Her2 or EtbB2, belongs to the ErbB receptor tyrosine kinase fam ily, which is composed of four plasma membrane-bound receptor tyrosine kinases including the other three receptors, epidermal growth factor receptor(EGFR), erbB-3 (neuregulin-binding), and erbB-4

ErbB receptor family

Which are typical cell membrane receptor tyrosine kinases that are activated following ligand binding and receptor dimerization. Especially Her2 lacks a ligand, and its structure resembles a ligand-activated state and favors dimerization. The formation of dimers leads to activation of the intrinsic tyrosine kinase domain and subsequent phosphorylation on specific tyrosine residues, which serve as docking sites for a variety of molecules. Recruitment of these molecules leads to the activation of different downstream signaling cascades, including the MAPK proliferation pathway and/or the PI3K/Akt pro-survival pathway. Inappropriate signaling may occur as a result of receptor overexpression or dysregulation of receptor activation, which may lead to Increased/uncontrolled cell proliferation, decreased apoptosis (programmed cell death), enhanced cancer cell motility, and angiogenesis

The oncogene amplify or overexpress play an important role in development and progression of aggressive types of breast cancer. Her2 amplifications are seen in breast, ovarian, bladder, Non-small-cell lung carcinoma, as well as several other tumor types.HER2 proteins have been shown to form clusters in cell membranes that may play a role in tumorigenesis.

Mechanism:

1. Herceptin inhibition

Herceptin works by binding to the HER2 receptors on the surface of breast cancer cells, preventing them from transforming the cell growth signals and slow or cease the growth of the breast cancer. Moreover, Herceptin flags the HER2 receptors and strikes these receptors by the assistance of the immune system.

2. HER2 activation

HER2 is one of the members in the human epidermal growth factor receptor family. HER2 can heterodimerize with any other three receptors in the ErbB family to form the dimerization, which consequently results in the autophosphorylation and initials the signaling pathway. When the amplification or overexpression of the HER2 gene occurs, it may cause the development and progression of certain types of the breast cancer.

Fig. 3. (1.) Herceptin inhibition mechanism (2.) HER2 activation mechanism

1.http://www.biooncology.com/biological-pathways/her-signaling
2.http://www.cellsignal.com/contents/science-pathway-research-tyrosine-kinase/erbb-her-signaling-pathway/pathways-erbb
3.[ ]Mark M. Moasser , The oncogene HER2; Its signaling and transforming functions and its role in human cancer pathogenesis, Oncogene. Author manuscript; available in PMC 2011 January 14.

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.

Application of the part:

1.

Pcons+RBS+Lpp-OmpA-N+Anti-EGFR

Pcons+RBS+RFP+Ter

Pcons+RBS+GFP+Ter

Cell staining experiment:
After cloning the part of anti-HER2, we were able to co-transform anti-HER2 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-HER2 and expressed fluorescence protein.
To prove this, we conducted the cell staining experiment by using the co-transformed E. coli to detect the HER2 in the cancer cell line.

Below are our staining result：
Negative control:
There are red and green fluorescent anti-HER2 E. coli stick on the cell’s surfaces as the anti-HER2 probes on E. colis successfully detect and bind with HER2.

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.

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.

There are red fluorescent anti-HER2 E. colis stick on the cell’s surfaces as the anti-HER2 probes on E. colis successfully detect and bind with HER2.

There are green fluorescent anti-HER2 E. colis stick on the cell’s surfaces as the anti-HER2 probes on E. colis successfully detect and bind with HER2.

2.

cell staining experiment:

Pcons+RBS+Lpp-OmpA-N+Anti-HER2+RBS+RFP+Ter

Pcons+RBS+Lpp-OmpA-N+Anti-HER2+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-HER2. To prove this, we have decided to undergo the cell staining experiment by using our E. coli to detect the HER2 in the cell lines.

First of all, the main materials that we needed are red green blue fluorescent E. coli with scFv of anti-HER2, red and green fluorescent E. coli without scFv and the cancer cell line – SKOV-3 that expressed HER2,for staining used. SKOV-3 is a kind of epithelial cell that expressed markers such as HER2.
After injecting E. coli into the wells, we had to shake the plate in darkness for 45 minutes. After staining for 45 minutes, we will wash away the unbind E. coli with PBS solution for a few times before observing the staining result under fluorescent microscope.

Below are our staining result：
Negative control:
There are red and green fluorescent anti-HER2 E. coli stick on the cell’s surfaces as the anti-HER2 probes on E. colis successfully detect and bind with HER2.

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.

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.

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.

There are red 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 (Fig.1) (Fig.2) (Fig.3). Thus, we get the optimum protein production time Compared with the simulated protein production rate of time, our experiment data quite fit the simulation.

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.

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.

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.

Sequence and Features