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<h6><center><b>Figure 7. </b>This is the SDS-page gel that was run after purifying the esterase protein from bacteria. The band is at 43 kDa, the expected size of the esterase protein.</center></h6>
<h6><center><b>Figure 7. </b>This is the SDS-page gel that was run after purifying the esterase protein from bacteria. The band is at 43 kDa, the expected size of the esterase protein.</center></h6>

Revision as of 07:54, 17 October 2014

Stanford–Brown–Spelman iGEM 2014 — Amberless Hell Cell

Figure 1. This image describes the degradation process, with the esterase enzyme de-acetylating cellulose acetate, and the cellulase enzyme breaking down the cellulose into glucose monomers.

In order to trigger the onset of degradation our UAV at specific conditions, we investigated pressure-sensitive promoters for their abilities to stimulate degradation upon impact and time-sensitive promoters that will control the amount of time the UAV should fly before it degrades. After a series of experimentations, we chose to use quorum sensing as a time delay mechanism to control the length of time our UAV can fly before degradation enzymes are produced.
Approach & Methods
We extracted the esterase and cellulase gene sequences from Neisseria Sicca and transformed them in E.coli. Since the genes were synthesized with a T7 promoter, we induced the expression of the proteins by adding IPTG to the cell cultures. The Esterase was successfully purified and its presence was confirmed by observing a band at 43kDa when a protein gel was run. The activity of the Esterase enzyme was tested by soaking in blue stain reagent and testing for intensity of the stain. As for cellulase, we are currently working to express the cellulase in E.coli so as we can purify it and test its activity.
In addition to working with the degradation enzymes, we created a mechanism where quorum sensing can cause a needed time delay for degradation so that our UAV does not degrade as soon as it starts to fly. Quorum sensing is a cell to cell communication mechanism, and here we use it to control when and how fast degradation should occur.

Figure 2. Our ideal quorum sensing system works in a loop: quorum sensing will activate a promoter that will trigger the expression of the degradation enzymes, which will activate positive feedback on the loop.
Our goal for this project was not only to isolate biodegradation enzymes but also to control the release of these enzymes, so that our UAV would not degrade uncontrollably. In order to control the initiation of biodegradation, we first considered using a pressure sensor. This would allow the UAV to begin degrading after the impact of a crash. The 2008 Tokyo Tech iGem team found that the ptet promoter was pressure sensitive and increased its activity 3-fold after undergoing 30 MPa of pressure. We got their construct from the distribution kit and tested its functionality by monitoring GFP expression. We found that this promoter is always turned on even at very low pressures (The cells were bright green even with no pressure at all). When we introduced a repressor for the Ptet promoter, we found out that the repressor is too strong, and even at high pressures, the Ptet promoter will still be repressed. The photo and graphs below show our testing of the Ptet promoter in the presence and absence of the Tet repressor.

Figure 3. A graph showing the relationship between pressure applied and expression of GFP under control of the ptet promoter.
The graph above shows that there was not a clear correlation between the amount of pressure applied on the ptet promoter to the amount of GFP expression: For instance, application of 14000rpm pressure on cells with ptet promoter for 30 seconds led to production of less fluorescence than application of 5000rpm pressure to the same batch of cells. When Tet Repressor was used, its effect was too strong and we could not find a repressor that could inhibit ptet just enough to control the biodegradation process.

On top of testing the ptet promoter, we analyzed the impact a crash would have on our UAV by using a force plate ,and we found it unlikely that the impact of the crash would ever reach as high a pressure as 30 MPa, based on projections from the data we collected. (see graph below).

Figure 4. A graph demonstrating the relationship between impact pressure and height of crash.
In the graph above, we measured the pressure upon impact of an object similar in shape and weight to our UAV. We took measurements by dropping or throwing the object from various heights and angles. Based on our data, it is projected that even from a drop height of 10m, the pressure upon impact would not even be 1 MPa, making it unlikely that any crash would ever cause 30 MPa of pressure.

We next tried to initiate degradation using a light sensor, which would activate degradation in the darkness, allowing our UAV to have a flight time of one day. We planned to do this using the construct from the UT Austin and UCSF 2004 Coliroid project. However, the strain of E. coli (EnvZ) that we needed to work with to use this construct was resistant to all 4 of the main antibiotics we had in our lab, making the bacteria difficult and expensive to work with.

Finally, we decided to use quorum sensing as a means of creating a time delay for initiation of degradation. Two previous constructs, BBa_I13202 and BBa_T9002, when combined, create a quorum sensing cascade used to initiate expression of GFP. We ligated these two parts together to create our novel part, BBa_K1499500, which we used for our assays. We found that, in lac deficient cells, the quorum sensing construct can be initiated by induction with IPTG. After this induction, GFP expression increases with time, implying that the construct is doing its job. Our data for this assay is shown below.
Quorum Sensing Data

Figure 5. Flow-cytometer data & histogram plots from fluorescence testing for quorum sensing construct.
Flow-cytometry is a laser-based technology that has a wide-range of uses, including multiparametric cell counting and cell sorting. In this study, we utilized the cell counting ability of a flow cytometer in order to determine how many of our cells were expressing GFP after being induced with IPTG (a lac analog). The three graphs and tables above represent the flow-cytometer data obtained from three samples of lac-deficient E. coli cells. One of the samples was a negative control of LB-cultured E. coli cells (Negative Control), and the other two samples (IPTG-Positive and IPTG-Negative) had the GFP-quorum sensing construct. The IPTG-Positive sample had a ~6X increase in GFP expression over the background IPTG-Negative cells, showing our construct worked correctly.

The “X-Median” seen on the three tables above shows the fluorescence intensity of all of the cells that were counted that were not considered dead (Median). Each sample measured 50,000 cells (Seen under All Events - Count), and then counted all of the cells that were alive (Median - Count), where each sample had between 47,000 and 49,000 live cells. From the data, and as illustrated in the graphs above, we can gather that the fluorescence intensity (X-Median) of the LB only cells was approximately 63 arbitrary fluorescence units (AFU), the IPTG-negative cells was ~17,000 AFU, and the intensity of the IPTG-positive cells was ~107,000 AFU, meaning that the IPTG-positive quorum sensing cells increased fluorescence intensity over 6X the background intensity (IPTG-negative cells). These results indicate that our quorum sensing construct works and greatly increases GFP expression when activated through IPTG-induction. The IPTG-negative cells also expressed some non-negligible degree of GFP, which suggests that the construct could still be improved further. However, the successful proof of concept of our quorum sensing construct is incredibly promising and allows us to proceed toward the next step.

Since we know that the quorum sensing construct is functional and inducible with IPTG. We can now work towards replacing the GFP gene with the genes for our degradation enzymes, allowing us to control degradation by applying IPTG at different time points.

In conjunction with working on controlling the initiation of degradation, we simultaneously worked with our two degradation enzymes, esterase and cellulase. After successfully transforming the esterase gene into E. coli, and confirming via colony PCR, we grew up a large culture of transformed cells and used this to extract and purify the esterase protein.

Figure 6. This is an image of a gel electrophoresis done after running colony PCR with E. coli colonies transformed with our esterase gene (1.5 kB).

Figure 7. This is the SDS-page gel that was run after purifying the esterase protein from bacteria. The band is at 43 kDa, the expected size of the esterase protein.
Once we had isolated our protein we were able to do functional assays with the esterase enzyme. By using a cellulose-binding dye that selectively binds to cellulose and not cellulose acetate, we were able to test whether or not the esterase enzyme was effective in de-acetylating commercial grade cellulose acetate. We soaked the cellulose acetate in the esterase protein at its optimal temperature of 30ºC and tested with the stain at multiple points. The results of our assay (shown below) demonstrate that over time the protein was working to degrade the cellulose acetate, as the blue stain intensity increased over time. In the future, we can better characterize our esterase protein by doing assays with more replicates, longer time periods, and varying amounts of protein.

Figure 8. Pieces of cellulose acetate were soaked in esterase enzyme for varying amounts of time before their level of degradation was tested using the blue cellulose-binding dye.
We are currently working on functional assays of the cellulase gene, and have submitted it as a BioBrick (BBa_K1499501).
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