An Introduction to Experiments Designing

The design of iPSC Safeguard pathway is simple and elegant. Basically, we can divide them into three major devices—the Suicide Gene, the MicroRNA-Target system and the Tet-off system, which has been described in detail in the Design module. For each device we had several candidates and before we finally assemble them into the whole pathway, we decided to test and characterize them carefully in the first place. The accurately quantitated parameters of each device then helped us set up a model and predicted which assembly scheme would work out best.

In order to test the devices, elements were cloned into our two major plasmids backbone: pcDNA3.0(from Invitrogen) and p199 (from。。。).The maps or design are shown in each parts of Results. We drove the Suicide Gene with CMV promoter to ensure a robust expression and prominent phenotype in cells. An eGFP was used to indicate the performance of Tet-off and MicroRNA-Target system, which could be quantitated through Image J and Western-Blot. All separately testing experiments were carried out via transient transfection in Bosc and HepG2, considering both the transfection efficiency and representation of liver cancer cell. Also, a survival experiment was done on liver cell to ensure that its miR122 level can successfully knockdown Suicide Gene with miR122 target-site.

After confirmation of each device, we proceeded to working on iPSC and assembly of the whole pathway. The whole pathway was integrated into two p199 plasmids, one for regulating and one for response .Lenti-virus packaging these two plasmids was produced and 3 cell lines of iPSC, HepG2, Hela were transfected. This successfully gave us the stable cell line with our iPSC Safeguard design. Then further test and characterization of the pathway’s performance of working as a whole were carried out. All the data can be seen in >>>>>.

Elements Testing

The Cruel Guard:Suicide Gene and Its Ancillary Facility

1.Comparison between Suicide Genes: who is the most tough killer?

We collected several Suicide Genes which functions in different pathways and patterns. In order to choose one that is most capable of inducing apoptosis in cancer cell, we first carried out a parallel experiment to compare their performance. The candidates were cloned into plasmid pcDNA3.0 in the MCS after CMV promoter just as shown in picture A, and then transfected into HEK293(PEI) and HepG2(Lipo2000). A GFP processed in the same way was used as a negative control to both indicate the transfection efficiency and normalize the death effect bring about by transfection process.Pictures were taken 48 hr after the transfection process.

To achieve a better resolution of the apoptosis phenomenon, we tried two techniques with the 48hr cells. The first one is DAPI staining (done with RIP1 and RIP3), which …... The second one is PI staining and flow cytometry counting(FCM counting), a technique that can give us the exact number of cell of different state. These two techniques were complementary to each other in a certain degree, for…

Figure1. Performance of different Suicide Genes candidate in mediating apoptosis.(A).plasmids construction. (B,C) Wells transfected with RIP1\RIP3 both showed significant phenotype of apoptosis compared with GFP control, 48 hr after transfection. After DAPI staining, dead cell showed deep blue color and the phenotype was better visualized. (D)Apoptosis phenotype of Apoptin(VP3). The protein was expressed in fusion with a GFP. Compared with 0 hr , apoptosis rose 48 hr after transfection, and GFP vision indicated the transfection efficiency. (E)DAPI staining of RIP1, zoomed in vision. We can see that the dead cell is…(F).PI staining and FCM data.

The figure shows that Both RIP1 and RIP3 had a outstanding performance in mediating apoptosis. Although Apoptin also killed cell in the results, its performance wasn’t stable since we cannot reproduce the results with another construction( data not shown.) However, according to many paper, this is a powerful protein with a specificity in killing cancer cell, which persuade us to keep working with it in the latter experiments. Moreover, we also test Bax/Bax S184A with the same method. However, no significant phenotype could be observed (data not shown) and thus we abandoned them in our Suicide Gene list.

The PI staining and FCM data showed that the apoptosis rate of RIP1 and RIP3 were about …. However, there could be some problems about the results such as a low expression level of GFP control, which made the data not so convincing. And we had planned a repeat for the experiment.

2.Suicide Gene working with its ancillary facility

Although we have successfully proved that each device of our pathway worked well, we wanted to further make use of the convenience of transient transfection due to the difficulty and time-consuming quality of Lenti-Virus transfection. So we designed combinational experiments, centered on our protagonist, the Suicide Gene, to further confirmed the devices’ performance in working coordinately. Although we didn’t have enough time to finished every one of them before Regional Jamboree, we have put them into our schedule of October if they’re still needed at that time(in situation where we still don’t have enough convincing data from Lenti-Virus work.)

A.Controlling the switch: a combination of Suicide Gene and Tet-off system.

Tough as the Suicide Gene is, what we expect from it was to protect instead of killing the innocent. And the 2 kinds of “innocent” cells in our designing were iPSC and Heptocyte. For iPSC, we have assigned it with a controlling panel: the Tet-Off system. Thus we designed an experiment to test the ability of Tet-off system to drive or turning off the expression of Suicide Gene. The designing and expected results were both shown below and Tet-Off construction driving GFP expressiong was taken as the positive control. Figure 2 B.Rescue scheme: a combination of Suicide Gene and MicroRNA-Target system.

For Heptocyte the protection comes from functioning of MicroRNA-Target system since the miR122 level was very high in Heptocyte(see Design). Due to technical bottleneck in transfection of Heptocyte, we decided to first simulated the environment and thus designed a mini-orthogonal experiment crossing variables of Target and miR122. The designing and expected results were shown below.

The results, together with the RT-qPCR data of miR122 level in Heptocyte(see。。。。), would prove that the device’s knockdown efficiency is sufficient to protect Heptocyte. Also with the mathematical model derived from Simulation experiment with GFP(see MicroRNA-Target part and Modelling), we can predict the rescue efficiency under different concentration of miR122 and target-site number.

With a idealized transfection efficiency, theoretically the three systems can be assembled and tested by simply transfect the cells with 3 plasmids(fig. ..). Plasmids A and B can turn on the Tet-off system and drive expression of GOI(Suicide Gene or GFP), while plasmid C can simulate the environment of Hepatocyte and execute the rescue scheme. Although practically it could be difficult due to efficiency and duration time limitation, we still decided to try it as a complement and see how much we can do with the transient transfection strategy. So we designed the following orthogonal experiment which, though seemingly complicated, could perfectly give a cross-reference to each variable and confirm all elements of our pathway.

We carried out the plan in HEK293 cell with PEI. However, due to technical problems no obvious results can be observed. We may try to optimize the technical factors to promote efficiency and repeat the experiment if necessary.

With our collinear-regression model of MicroRNA-Target system, the knockdown efficiency predicted with a two-copy complete target-site and the miR122 concentration of liver cell would be >>>%, and the experiment shows that this gives a good rescue effect.

Simulated Testing of MicroRNA-Target Devices: Security Gate for Liver Cell.

1.Performance comparison between target-sites: CULT and Complete[1].

An orthogonal experiment was performed to make a comparison between the knock-down efficiency of different target-site under different concentration of MiR122. Two plasmids were co-transfected using PEI into Bosc cell line. Plasmids A expressed GFP whose mRNA carries different target-site of miR122 in its 3’ UTR. Plasmids B express MiR122. Target-site with most notable knock-down efficiency will be chosen for further characterization and construction.

The miR122 expression plasmid used in this experiment also express a GFP, which is a bug originally neglected in our design. However, we find out that although this does interfere our results to some degree, we can still gain insight into different performance between target-sites. Also, this accident may be kind of an unexpected clue to a new discovery, which may be one of our future work>>>>

After finding out this bug, we do quick-change to introduce a stop-codon into the CDS of GFP, which proves very efficient.

The result shows that with the same copy number, complete target-site works better than CULT target-site, which fits the results of the 2010 Heiderburg iGEM team. What’s more, two copies target-site works better than single site, which encourage us to further build target-sites of more copy number and quantitatively test their performance.

2.Quantitative characterization and Target-site modelling

In order to build a precisely controlled device, we decided to further characterized the complete-target-sites of 1\2\4 copy number and chose the most suitable one for pathway construction. To begin with we design another orthogonal experiment for the two variables, target-site-number N and MiR122 concentration M, with a more precise gradients for MiR122. But soon we found that would be too labour- costing. A better way is to replace it with 2 dosage-gradient experiment and a mathematic model.

Plasmids A was the same as used in experiment 1, while plasmids B no longer expressed GFP due to the silencing mutation in CDS. In order to quantitate target-site performance under different concentration of MiR122, a dosage-gradient of plasmids B was set up and so was a parallel experiment groups to leave enough samples of each concentration for both Western Blot and RT-qPCR.

收样时间：48hr
Target-site: Complete 2-copies

The results show that MiR122 does have an dosage effect upon target-site. The concentration that have the best knock-down performance is about >>>>>, and RT-qPCR of different kinds of cell line shows that only liver cell is able to turn off this second switch of iPS- Safeguard and avoid the fate of apoptosis.

B.Performance of complete target-site of different copy number

To further characterized the several candidate target-site for our iPS-Safeguard, we design another gradient experiment and this time let target-site copy number be the variable. The MiR122 concentration is fixed at one that gives a best performance and since we’ve already known the relationship between plasmids concentration and MiR122 expression level, only a Western-Blot is needed.

Mir122 concentration:0.75ug=

The results show that 4 copy target-site has the most brilliant knock-down efficiency, which is about〉〉〉〉, which conforms to the data given by 2010 Heiderburg using dual-fluorescent system. However, since we are on a tight schedule in this competition, we can’t wait until this results and have already adopt the 2 copy target-site for further construction. Has we needed a better knock-down efficiency later, we can replace the target-site conveniently with our restriction enzyme.

C. the linear regression model for miR122-target system.

The results of experiment A and B then provide enough data for Model construction. The （拟合了线性关系，用matlab 做出了这样的一幅图？总之 就是一个建模过程的简单描述）. The model shows that(关于模型说明的问题的一个精简解释，说明了什么趋势，预测了什么事情) Figure4. Linear Regression Model for MicroRNA-Target Gate.

Dosage test of Tet-off system: Controlling Panel For iPSC.

1.Trial test of two different Tet-off system: the switching performance between on and off states.

Experiment Design:
Two plasmids were co-transfected into Bosc cell line using PEI, plasmids A expressed tTA protein and one carries the TRE-tight element and while response to tTA protein, expressed eGFP (BBa>..). The well with only plasmids B characterized the basal leaky expression of TRE- tight element, which can be viewed as the first off-state. Well with both plasmids represents the on-state of the system, and the third well with dox represents the second off-state. Dox concentration was chosen according to protocols in order to give a best performance of the system and avoid hazard on cell growth.

Conclusion:
The data shows that the two systems have different on/off expression levels, but both shows good response to Dox, which is low off- expression-not so high on-expression, and relatively high off-expression-robust on-expression. The variability in performance can be properly utilized according to the apoptosis condition conducted by Suicide Gene.