Team:Aalto-Helsinki/Proof Of Concept

Aalto-Helsinki 2020

PROOF OF CONCEPT

GOALS OF OUR PROOF OF CONCEPT EXPERIMENTS


The end-users of SINISENS are wastewater treatment plants, where it could be used for optimization of the pharmaceutical removal process. In order to demonstrate feasibility and potential of SINISENS being used in practice, we performed several experiments. The goal of these tests was answering three key questions:


  • Can our biosensor survive in wastewater?
  • Can GFP be correlated to the amount of macrolides?
  • Is the biosensor sensitive enough to measure the low concentrations of macrolides present in wastewater?

Here, we only briefly describe the experiments. More details about performed tests, as well as full protocols, can be found on our experiments page.

CAN OUR BIOSENSOR SURVIVE IN WASTEWATER?


A common mistake we have noticed in previous iGEM projects is the fact that the chosen organism could not survive in the setting it was supposed to function in. To ensure this does not happen in our case, we decided to conduct cell viability experiments, more detailed description of which can be found here. First, we performed a plate-reader experiment, where cells expressing pET28a-sfGFP plasmid were grown in various media: Luria-Bertani (LB), mixes of LB and wastewater, wastewater and milliQ. The fluorescence was measured in 15-minute intervals. using optical density (OD). The results indicate that E. coli can survive in wastewater, as fluorescence increases in all samples other than milliQ (Fig. 1).


Figure 1. Fluorescence produced by cells in the plate-reader experiment determining whether E. coli can survive in wastewater. Fluorescence values are normalized with OD. WW - wastewater, MQ - milliQ. The graph includes data from three repeats (each comprising 3 technical repeats).

To confirm the results from the plate-reader experiment, we also conducted another viability test using a flow cytometer, as optical density does not give specific information how many cells are alive and dead. Cells expressing pET28a-sfGFP were grown in different media (mix of LB and wastewater, LB, wastewater and milliQ) and induced 1.5 h before measurements. Right before the measurement a viability dye was added to distinguish the dead cells. There were viable cells in all the samples. These results confirm the conclusion of the plate-reader experiment: E. coli were able to survive in wastewater. (Fig. 2).


Figure 2. Proportion of living cells in the selected growth media. WW - wastewater, MQ - milliQ. The graph includes data from two repeats (each dot represents one repeat).

CAN GFP BE CORRELATED TO THE AMOUNT OF MACROLIDES?


Another important aspect of the biosensor is a confirmation of its ability to quantify macrolides efficiently. We need to know if our system is reliable and if it works at all and what is the best time for a measurement after exposing cells to wastewater. We also wanted to set the detection limits and specificity. We conducted a plate reader experiment, where cells transformed with the biosensor were grown in media concentrations of erythromycin (0, 0.1, 0.5, 1, 10, 50, 100 µg/ml). The amount of erythromycin present in the media seems to correlate with the amount of fluorescence produced. (Fig 3). However, the differences are so slight the biosensor could not be used as a tool for macrolide quantification in its present design. For these reasons we decided to further fine-tune it.


Figure 3. OD normalized fluorescence values produced by cells grown in different erythromycin (ERY) concentrations (0, 0.1, 0.5, 1, 10, 50, 100 µg/ml). The graph includes data from three repeats (each comprising 3 technical repeats).

MEETING THE NEEDS OF WASTEWATER TREATMENT PLANTS


The macrolide concentrations in effluent water in wastewater treatment plants are between 50 - 500 ng/L [1]. Current genetic circuit does not meet these detection limits. To further tune our design, we considered several changes.

First, we wanted to see if we can get a stronger output by using a different fluorescent protein. We replaced egfp with sfgfp, which in our test produced higher fluorescent output and performed a plate-reader experiment (Fig. 4). The assumption was that the difference between the intensities read in various erythromycin concentrations will be more pronounced. However, this did not seem to improve the resolution of the biosensor (see our results page).


Figure 4. Comparison of fluorescent output produced by cells expressing two output cassettes (assemblies containing a reporter protein driven by an inducible pMphR promoter, see our experiments page), one containing sfGFP, second EGFP, grown in absence of the repressor. Fluorescence values were OD normalized. The graph includes data from one biological repeat (comprising 3 technical repeats).

Second, we tried optimizing the strength of the promoter, as our model, suggested lowering the strength could result in higher fluorescence values (Fig. 5). For more details see our modelling page.


Figure 5. Scan of EGFP production in a constant erythromycin concentration. Different lines correspond to different promoter strengths. The lowest line represents the strength of the constitutive promoter used in our initial design, the highest line represents the output with no repressor present.

Based on these results we assembled an inducible optical device, where we replaced a constitutive promoter driving MphR with an inducible pBAD promoter and performed an analogical plate-reader experiment as with the initial optical device (containing a constitutive promoter). This slightly improved the separation of output signal between different erythromycin concentrations, but the change was not significant enough to reach the necessary sensitivity (Fig. 6). Noteworthy, due to time restraints we did not have time to optimize the experiments and we can only include the preliminary data. Although promising, the protocol can be further improved and the tests should be replicated.


Figure 6. OD normalized fluorescence values produced by TOP10 E. coli cells transformed with an inducible optical device grown in different erythromycin (ERY) concentrations (0, 0.1, 1 and 100 µg/ml) and 0.1% of arabinose. The graph includes data from two repeats (comprising 3 technical repeats).

Another approach we have taken to improve sensitivity is replacing natural MphR with new MphR sequences modelled in Rosetta. These modifications were likely to improve binding affinity of MphR to macrolides. We experimentally tested 5 of the sequences that showed best scores for erythromycin, clarithromycin or both. One of them (third best score for erythromycin, see our modelling page) seemed to improve biosensor’s sensitivity and produce better separation between fluorescence measured in samples containing different macrolide concentrations (Fig. 7). We have only performed two experiments and these results should be replicated, but the initial data seems promising.


Figure 7. Fluorescence produced by cells transformed with the optical device comprising a MphR mutant in different erythromycin (ERY) concentrations [µg/ml]. Fluorescence values were OD normalized. The data includes an average of two experiments (each comprising 3 technical repeats).

Lastly, we assessed the performance of the constitutive biosensor in a modified E. coli strain: GKCW104 [2]. This strain has deleted tolC genes, which are proteins forming an outer membrane channel in several multidrug resistance efflux pumps. Moreover, it has open FhuA proteins, which leads to hyperporination of the cell. Introduction these changes to a membrane requires induction with arabinose. We have performed a plate-reader experiment, where the cells with the constitutive optical device were grown in various erythromycin concentrations and 0.1% of arabinose. We observed improved sensitivity compared to TOP10, since fluorescence produced in samples containing different macrolide concentrations showed greater separation (Fig. 8). However, it is not clear yet whether such a biosensor would be able to detect the lowest concentrations of macrolides present in wastewater. Additionally, another viability test with GKCW104 would be required to confirm these cells are suitable for growth in wastewater. In addition to that, the differences between fluorescence measured in different concentrations is visible after less time than in TOP 10 cells.


Figure 8. OD normalized fluorescence values produced by GKCW104 cells transformed with an constitutive optical device grown in different erythromycin (ERY) concentrations (0, 0.1, 1 and 100 µg/ml) and 0.1% of arabinose. The graph includes data from two repeats (each comprising 3 technical repeats).

POSSIBILITIES FOR FURTHER IMPROVEMENT


Although our results are quite promising, the biosensor still requires a lot of improvement. It is possible that adding an enhancer sequence, using a low copy number plasmid instead of a high copy number one or chromosomal integration may bring it a few steps closer towards the desired product. We are also exploring ways to concentrate macrolides in a sample with use of microfluidics, but the initial tests have been inconclusive and the technique requires further optimization. More about future experiments can be found on our results page.

REFERENCES


1. Schafhauser, B. H., Kristofco, L. A., de Oliveira, Cíntia Mara Ribas, & Brooks, B. W. (2018). Global review and analysis of erythromycin in the environment: Occurrence, bioaccumulation and antibiotic resistance hazards. Environmental Pollution, 238, 440-451. doi:10.1016/j.envpol.2018.03.052
2. Krishnamoorthy, G., Wolloscheck, D., Weeks, J., Croft, C., Rybenkov, V., & Zgurskaya, H. (2016). Breaking the permeability barrier of Escherichia coli by controlled hyperporination of the outer membrane. Antimicrobial Agents And Chemotherapy, 60(12), 7372-7381. doi: 10.1128/aac.01882-16











Special thanks to HSY for all their support











Kemistintie 1, Espoo, Finland

team@aaltohelsinki.com




Aalto-Helsinki 2020

PROOF OF CONCEPT

GOALS OF OUR PROOF OF CONCEPT EXPERIMENTS


The end-users of SINISENS are wastewater treatment plants, where it could be used for optimization of the pharmaceutical removal process. In order to demonstrate feasibility and potential of SINISENS being used in practice, we performed several experiments. The goal of these tests was answering three key questions:


  • Can our biosensor survive in wastewater?
  • Can GFP be correlated to the amount of macrolides?
  • Is the biosensor sensitive enough to measure the low concentrations of macrolides present in wastewater?

Here, we only briefly describe the experiments. More details about performed tests, as well as full protocols, can be found on our experiments page.

CAN OUR BIOSENSOR SURVIVE IN WASTEWATER?


A common mistake we have noticed in previous iGEM projects is the fact that the chosen organism could not survive in the setting it was supposed to function in. To ensure this does not happen in our case, we decided to conduct cell viability experiments, more detailed description of which can be found here. First, we performed a plate-reader experiment, where cells expressing pET28a-sfGFP plasmid were grown in various media: Luria-Bertani (LB), mixes of LB and wastewater, wastewater and milliQ. The fluorescence was measured in 15-minute intervals. using optical density (OD). The results indicate that E. coli can survive in wastewater, as fluorescence increases in all samples other than milliQ (Fig. 1).


Figure 1. Fluorescence produced by cells in the plate-reader experiment determining whether E. coli can survive in wastewater. Fluorescence values are normalized with OD. WW - wastewater, MQ - milliQ. The graph includes data from three repeats (each comprising 3 technical repeats).

To confirm the results from the plate-reader experiment, we also conducted another viability test using a flow cytometer, as optical density does not give specific information how many cells are alive and dead. Cells expressing pET28a-sfGFP were grown in different media (mix of LB and wastewater, LB, wastewater and milliQ) and induced 1.5 h before measurements. Right before the measurement a viability dye was added to distinguish the dead cells. There were viable cells in all the samples. These results confirm the conclusion of the plate-reader experiment: E. coli were able to survive in wastewater. (Fig. 2).


Figure 2. Proportion of living cells in the selected growth media. WW - wastewater, MQ - milliQ. The graph includes data from two repeats (each dot represents one repeat).

CAN GFP BE CORRELATED TO THE AMOUNT OF MACROLIDES?


Another important aspect of the biosensor is a confirmation of its ability to quantify macrolides efficiently. We need to know if our system is reliable and if it works at all and what is the best time for a measurement after exposing cells to wastewater. We also wanted to set the detection limits and specificity. We conducted a plate reader experiment, where cells transformed with the biosensor were grown in media concentrations of erythromycin (0, 0.1, 0.5, 1, 10, 50, 100 µg/ml). The amount of erythromycin present in the media seems to correlate with the amount of fluorescence produced. (Fig 3). However, the differences are so slight the biosensor could not be used as a tool for macrolide quantification in its present design. For these reasons we decided to further fine-tune it.


Figure 3. OD normalized fluorescence values produced by cells grown in different erythromycin (ERY) concentrations (0, 0.1, 0.5, 1, 10, 50, 100 µg/ml). The graph includes data from three repeats (each comprising 3 technical repeats).

MEETING THE NEEDS OF WASTEWATER TREATMENT PLANTS


The macrolide concentrations in effluent water in wastewater treatment plants are between 50 - 500 ng/L [1]. Current genetic circuit does not meet these detection limits. To further tune our design, we considered several changes.

First, we wanted to see if we can get a stronger output by using a different fluorescent protein. We replaced egfp with sfgfp, which in our test produced higher fluorescent output and performed a plate-reader experiment (Fig. 4). The assumption was that the difference between the intensities read in various erythromycin concentrations will be more pronounced. However, this did not seem to improve the resolution of the biosensor (see our results page).


Figure 4. Comparison of fluorescent output produced by cells expressing two output cassettes (assemblies containing a reporter protein driven by an inducible pMphR promoter, see our experiments page), one containing sfGFP, second EGFP, grown in absence of the repressor. Fluorescence values were OD normalized. The graph includes data from one biological repeat (comprising 3 technical repeats).

Second, we tried optimizing the strength of the promoter, as our model, suggested lowering the strength could result in higher fluorescence values (Fig. 5). For more details see our modelling page.


Figure 5. Scan of EGFP production in a constant erythromycin concentration. Different lines correspond to different promoter strengths. The lowest line represents the strength of the constitutive promoter used in our initial design, the highest line represents the output with no repressor present.

Based on these results we assembled an inducible optical device, where we replaced a constitutive promoter driving MphR with an inducible pBAD promoter and performed an analogical plate-reader experiment as with the initial optical device (containing a constitutive promoter). This slightly improved the separation of output signal between different erythromycin concentrations, but the change was not significant enough to reach the necessary sensitivity (Fig. 6). Noteworthy, due to time restraints we did not have time to optimize the experiments and we can only include the preliminary data. Although promising, the protocol can be further improved and the tests should be replicated.


Figure 6. OD normalized fluorescence values produced by TOP10 E. coli cells transformed with an inducible optical device grown in different erythromycin (ERY) concentrations (0, 0.1, 1 and 100 µg/ml) and 0.1% of arabinose. The graph includes data from two repeats (comprising 3 technical repeats).

Another approach we have taken to improve sensitivity is replacing natural MphR with new MphR sequences modelled in Rosetta. These modifications were likely to improve binding affinity of MphR to macrolides. We experimentally tested 5 of the sequences that showed best scores for erythromycin, clarithromycin or both. One of them (third best score for erythromycin, see our modelling page) seemed to improve biosensor’s sensitivity and produce better separation between fluorescence measured in samples containing different macrolide concentrations (Fig. 7). We have only performed two experiments and these results should be replicated, but the initial data seems promising.


Figure 7. Fluorescence produced by cells transformed with the optical device comprising a MphR mutant in different erythromycin (ERY) concentrations [µg/ml]. Fluorescence values were OD normalized. The data includes an average of two experiments (each comprising 3 technical repeats).

Lastly, we assessed the performance of the constitutive biosensor in a modified E. coli strain: GKCW104 [2]. This strain has deleted tolC genes, which are proteins forming an outer membrane channel in several multidrug resistance efflux pumps. Moreover, it has open FhuA proteins, which leads to hyperporination of the cell. Introduction these changes to a membrane requires induction with arabinose. We have performed a plate-reader experiment, where the cells with the constitutive optical device were grown in various erythromycin concentrations and 0.1% of arabinose. We observed improved sensitivity compared to TOP10, since fluorescence produced in samples containing different macrolide concentrations showed greater separation (Fig. 8). However, it is not clear yet whether such a biosensor would be able to detect the lowest concentrations of macrolides present in wastewater. Additionally, another viability test with GKCW104 would be required to confirm these cells are suitable for growth in wastewater. In addition to that, the differences between fluorescence measured in different concentrations is visible after less time than in TOP 10 cells.


Figure 8. OD normalized fluorescence values produced by GKCW104 cells transformed with an constitutive optical device grown in different erythromycin (ERY) concentrations (0, 0.1, 1 and 100 µg/ml) and 0.1% of arabinose. The graph includes data from two repeats (each comprising 3 technical repeats).

POSSIBILITIES FOR FURTHER IMPROVEMENT


Although our results are quite promising, the biosensor still requires a lot of improvement. It is possible that adding an enhancer sequence, using a low copy number plasmid instead of a high copy number one or chromosomal integration may bring it a few steps closer towards the desired product. We are also exploring ways to concentrate macrolides in a sample with use of microfluidics, but the initial tests have been inconclusive and the technique requires further optimization. More about future experiments can be found on our results page.

REFERENCES


1. Schafhauser, B. H., Kristofco, L. A., de Oliveira, Cíntia Mara Ribas, & Brooks, B. W. (2018). Global review and analysis of erythromycin in the environment: Occurrence, bioaccumulation and antibiotic resistance hazards. Environmental Pollution, 238, 440-451. doi:10.1016/j.envpol.2018.03.052
2. Krishnamoorthy, G., Wolloscheck, D., Weeks, J., Croft, C., Rybenkov, V., & Zgurskaya, H. (2016). Breaking the permeability barrier of Escherichia coli by controlled hyperporination of the outer membrane. Antimicrobial Agents And Chemotherapy, 60(12), 7372-7381. doi: 10.1128/aac.01882-16











Special thanks to HSY for all their support











Kemistintie 1, Espoo, Finland

team@aaltohelsinki.com