This year's team conducted multiple experiments to improve the characterization of biobrick part BBa_F2620. This part can be viewed here on the iGEM registry.
We designed 3 experiments to provide better characterization of this part: F2620 Induced by Synthetic AHLs, F2620 Induced by Sender Supernatants, and F2620 induced by Sender cells on Agar.
*What's New*
The first experiment, F2620 Induced by Synthetic AHLs, included F2620 receiver cells being induced by synthetic AHL chemicals under different concentrations. The maximum GFP expression was analyzed to understand the effect AHL concentration had on the receiver expressing GFP.
Last year, the 2016 team conducted experiments on the F2620 receiver with synthetic AHLs with only two concentrations. This year F2620 was improved with new protocols and additional analysis. We expanded the synthetic AHLs tested to induce F2620 with Sin (3-oxo-C14-HSL) and Rhl (C4-HSL), along with additional concentrations to broaden the characterization, resulting in a transfer function for each tested AHL. This additional data greatly improve the characterization of F2620.
*What’s new*
The following experiment, F2620 Induced by Sender Supernatants, used F2620 receiver cells being induced by a variety of combinations of senders in supernatant form. The maximum GFP expression was analyzed to understand the effect different percentages of sender supernatant had on the receiver expressing GFP. We redesigned and optimized supernatant induction protocols to produce more reliable and consistent data than last year. First, we tested more concentrations of filtered sender supernatant and were able to create novel transfer functions. In addition to redoing last year’s inductions with the F2620 using single senders, this year we added combinations of two sender supernatant and therefore greatly improved characterization of the receivers and senders.
*What’s new*
The final experiment included F2620 receiver cells that were spread on agar plates and induced by spread sender cells. Images are then taken at different time intervals of the agar plate to analyze the induction rates and diffusion of F2620 induction.
We greatly improved the protocol and controls for this year’s agar plate inductions, resulting in more consistent and reliable data. We also collected data over time to generate a model of induction rate, which informed us on further experiments. We demonstrated a new 3D analysis technique with greater accuracy for agar plate induction distance. These additional results and analyses greatly improved the characterization of F2620
*What's New*
Last year, the 2016 team conducted experiments on the F2620 receiver with synthetic AHLs with only two concentrations. This year F2620 was improved with new protocols and additional analysis. We expanded the synthetic AHLs tested to induce F2620 with Sin and Rhl, along with additional concentrations to broaden the characterization, resulting in a transfer function for each tested AHL. This additional data greatly improve the characterization of F2620.
The graphs below depict the GFP production from the F2620 receiver to be higher at a higher concentration of the synthetic AHL of Las. The second graph, depicts the same nature however at a higher concentration of the synthetic AHL of Lux. It is interesting to note that the Las sender, at a lower concentration, promotes a higher GFP expression than the Lux. Our group hypothesized that it would be opposite considering the LuxI sender comes from the same system as F2620. These results show differently. This improved the characterization of F2620, with more range of concentrations. Using the Trans Function, these results can be seen in a new light
In this graph with the Rhl AHL, it can also be seen that there is a somewhat steady state of GFP expression from LuxR. Although there is an imperceptible increasing slope of points across increasing concentration, GFP expression remains fairly consistent.
Another interesting result was the with the sender RpaI. As depicted in the graph below, it shows an almost steady state of GFP production, independent of the concentration of the AHL signal. This might be a useful finding if researchers wanted to use a smaller amount of signal due to limiting resources of RpaI sender. However, it did not show an orthogonal pathway since it did in fact promote GFP expression.
The resulting graph showed a varied fluctuation between GFP expression and increasing AHL concentration. A noticeable observation however is the overall trend seems to be that 1E-14M through 1E-9M concentration has a steady state of GFP expression then increases with increasing concentration.
A fairly interesting result came about in the induction of LuxR with the Tra AHL. There was a steady increase in GFP expression as the AHL concentration increased. This data will be beneficial to researchers looking for a system with a maximum or minimum GFP induction.
*What’s new*
The following experiment, F2620 Induced by Sender Supernatants, used F2620 receiver cells being induced by a variety of combinations of senders in supernatant form. The maximum GFP expression was analyzed to understand the effect different percentages of sender supernatant had on the receiver expressing GFP. We redesigned and optimized supernatant induction protocols to produce more reliable and consistent data than last year. First, we tested more concentrations of filtered sender supernatant and were able to create novel transfer functions. In addition to redoing last year’s inductions with the F2620 using single senders, this year we added combinations of two sender supernatant and therefore greatly improved characterization of the receivers and senders.
The receiver being used for the below results is the Lux receiver. The second set of senders that was tested is shown below, these are all the combinations and percentages of the AHLs for the test including the controls. Each data point was tested in triplicate. The colors will coordinate with the graphs for each set of tests. The graphs for each set of data will include the overall average GFP signal, the average OD 600 and the normalization of the GFP over the OD 600. The number of data points used made adding individual error bars ineffective as the data was not able to be read. Error was calculated on the controls and added as separate bar graphs below the full data set. There was also Hill curve (trans equations) made that include error/ standard deviation if more information is needed for any notable results.
In this set of tests with the Lux receiver the results showed that the LasI expressed the highest, EsaI 2nd highest and RpaI 3rd highest. The graphs concluded that the higher the Las and EsaI combination, the higher the overall GFP expression. No combinations pushed the GFP expression higher than any 50% sender alone.
The second set of senders that was tested is shown below, these are all the combinations and percentages of the AHLs for the test including the controls. Each data point was tested in triplicate. The colors will coordinate with the graphs for each set of tests. The graphs for each set of data will include the overall average GFP signal, the average OD 600 and the normalization of the GFP over the OD 600. The number of data points used made adding individual error bars ineffective as the data was not able to be read. Error was calculated on the controls and added as separate bar graphs below the full data set. There was also Hill curve (trans equations) made that include error/ standard deviation if more information is needed for any notable results.
This test showed some notable results. As seen clearly in the last graph, the AubI showed a higher expression when mixed with 10% of a second sender (even when that sender was a negative control sender). The 40% AubI mixed with 10% negative sender and the 40% AubI mixed with 10% RhlI both expressed higher than the 50% AubI by itself. This result was confirmed in another test where 40% Aub mixed with 10% EsaI and 40% AubI mixed with 10% CerI both expressed higher than the 50% AubI alone.
The third set of senders that was tested is shown below, these are all the combinations and percentages of the AHLs for the test including the controls. Each data point was tested in triplicate. The colors will coordinate with the graphs for each set of tests. The graphs for each set of data will include the overall average GFP signal, the average OD 600 and the normalization of the GFP over the OD 600. The number of data points used made adding individual error bars ineffective as the data was not able to be read. Error was calculated on the controls and added as separate bar graphs below the full data set. There was also Hill curve (trans equations) made that include error/ standard deviation if more information is needed for any notable results.
In this set of tests with the Lux receiver the results showed that the RpaI expressed the highest, LasI 2nd highest and EsaI 3rd highest. The graphs concluded that the higher the RpaI and LasI combination, the higher the overall GFP expression. No combinations pushed the GFP expression higher than any 50% sender alone.
This test showed some notable results. As seen clearly in the last graph, the AubI showed a higher expression when mixed with 10% of a second sender (even when that sender was a negative control sender). The 40% AubI mixed with 10% negative sender and the 40% AubI mixed with 10% EsaI both expressed higher than the 50% AubI by itself. This result was confirmed in the previous test #2 with the LuxR (40% Aub mixed with 10% EsaI and 40% AubI mixed with 10% CerI both expressed higher than the 50% AubI alone).
The fifth set of senders that was tested is shown below, these are all the combinations and percentages of the AHLs for the test including the controls. Each data point was tested in triplicate. The colors will coordinate with the graphs for each set of tests. The graphs for each set of data will include the overall average GFP signal, the average OD 600 and the normalization of the GFP over the OD 600. The number of data points used made adding individual error bars ineffective as the data was not able to be read. Error was calculated on the controls and added as separate bar graphs below the full data set. There was also Hill curve (trans equations) made that include error/ standard deviation if more information is needed for any notable results.
In this test there were some results that were not able to be replicated, meaning that there may have been some contamination or other unaccounted for error. The 40% LuxI + 10% negative sender expressed higher than the 50% LuxI alone, this result was not able to be replicated. Again the 40% LuxI + 10% RpaI expressed higher than the 50% LuxI alone but the result was unable to be replicated and lastly the 40% LuxI + 10% BraI expressed higher than the 50% LuxI and again, the result was unable to be replicated.
The sixth set of senders that was tested is shown below, these are all the combinations and percentages of the AHLs for the test including the controls. Each data point was tested in triplicate. The colors will coordinate with the graphs for each set of tests. The graphs for each set of data will include the overall average GFP signal, the average OD 600 and the normalization of the GFP over the OD 600. The number of data points used made adding individual error bars ineffective as the data was not able to be read. Error was calculated on the controls and added as separate bar graphs below the full data set. There was also Hill curve (trans equations) made that include error/ standard deviation if more information is needed for any notable results.
In this set of tests with the Lux receiver the results showed that the LuxI expressed the highest, RhlI 2nd highest and LasI 3rd highest. The graphs concluded that the higher the LuxI combination, the higher the overall GFP expression. No combinations pushed the GFP expression higher than any 50% sender alone.
*What’s new*
The final experiment included F2620 receiver cells that were spread on agar plates and induced by spread sender cells. Images are then taken at different time intervals of the agar plate to analyze the induction rates and diffusion of F2620 induction.
We greatly improved the protocol and controls for this year’s agar plate inductions, resulting in more consistent and reliable data. We also collected data over time to generate a model of induction rate, which informed us on further experiments. We demonstrated a new 3D analysis technique with greater accuracy for agar plate induction distance. These additional results and analyses greatly improved the characterization of F2620