Aalto-Helsinki 2020



Our main goal has been to construct a biosensor to detect and quantify macrolide antibiotics meant for use in wastewater treatment plants. Engineering success has been demonstrated in the overall project by following the engineering design cycle. As an overview, we gathered information about environmental issues and wastewater treatment plant needs. We thoroughly discussed which kind of device could be beneficial. We confirmed the functioning of five basic parts and the full biosensor. Additionally, after observing unsatisfactory performance, we improved and adapted our design (see Optical Device Troubleshooting below).

Project Design - General Overview


We gathered information about the main environmental issues facing Europe. Our attention was caught by issues related to water pollution. We wanted the device developed during our project to be of use in real life, so it was crucial to maintain communication with wastewater treatment plants and make sure all their needs are met.


We were considering several ways to use synthetic biology to tackle problems currently being faced by wastewater treatment plants. Some of them included designing bacteria capable of degrading or recycling microplastics, as well as microbial systems detecting certain pollutants, such as pharmaceuticals or perfluoroalkyl chemicals, more efficiently.


In order to evaluate these ideas, we asked experts from several fields, including, among others, wastewater treatment plant employees, microplastics experts and environmental institutions. The key questions we had for the interviewees concerned feasibility and applicability of our device: we wanted to be able to develop a promising tool within the 3 months that we had access to the lab, and we wanted it to be useful for wastewater treatment plants.


Based on our research and experts' advice, we decided to design a biosensor to detect and quantify macrolide antibiotics.This seemed to be the most appropriate choice since (i) these compounds have been in the ‘watch list’ of pharmaceuticals for EU-wide monitoring in aquatic environments for several years [1], (ii) wastewater treatment plants confirmed that a device like ours would be useful to monitor the pharmaceutical removal process on-site as current detection methods are quite expensive [2] and (iii) clarithromycin is one of the marker compounds used for monitoring the removal efficiency in Switzerland, which is currently leading the research in this area [3].


We set three main goals for our project: (i) assure the cells used for the biosensor survived in wastewater, (ii) construct the optical device for macrolide detection and quantification, (iii) increase the sensitivity of the biosensor and concentrate macrolides in the cells or in the sample if necessary, since macrolides are present in wastewater in very low amounts [4].

Cell Viability

To achieve our first goal, we performed cell viability tests, where cells were grown with various media, including wastewater. More detailed description can be found here. These experiments were initially conducted using a plate-reader and later additionally confirmed with a flow cytometer. The results confirmed that cells can survive in wastewater.

Optical Device Construction


Macrolide Detection - Necessary Sensitivity

While constructing the biosensor, our main concern was the low concentration of macrolides in wastewater [4]. Therefore, we decided to model the expected range of the biosensor. The results indicated that the biosensor may need to be 10-100 times more sensitive to detect the low concentrations of macrolides found in wastewaters (Fig. 1). To solve this issue, we decided to use Rosetta to predict mutations in the binding site of MphR that would increase the binding affinity of MphR to macrolide antibiotics. More about mathematical and protein modelling can be found here. In addition, to further increase the detection of macrolide antibiotics, we decided on studying ways of concentrating them inside our biosensor cells.

Figure 1. Plot of the Hill function showing at which macrolide concentration MphR binds to these antibiotics. Blue line shows the result for the wildtype MphR and red line for a previously modified MphR [5].

Selecting the right Antibiotic Resistance Gene

Since our biosensor aims to detect macrolide antibiotics, it is fundamental the cells survive in their presence. However, many products of antibiotic resistance genes act by pumping the compound out from the cell or by modifying its chemical structure. This would greatly affect the detection and reliability of the results. That is why we decided ermC is a good candidate, since it encodes a protein that methylates selected residues of 23S rRNA, thus making the cells immune to the effect of macrolides and licosamines [9].


Our genetic circuit (Fig. 2) was constructed, so that when there are no macrolides present in the sample, MphR, a repressor protein [6], binds to pMphR (naturally a promoter of macrolide resistance genes) [7] preventing the production of enhanced green fluorescent protein [EGFP]. In case there are macrolides, these compounds bind to MphR, preventing the protein from DNA binding. This allows egfp to be expressed. Although for the proof-of-concept studies we are using a fluorescent output, the final product used in wastewater treatment plants may be an electrochemical biosensor, since in many cases there are thought to be more sensitive [8] and the output is easier to be interpreted (see our implementation page). The constitutive promoter we selected is a medium strength promoter (BBa_J23106) to avoid production of too many repressor proteins and egfp being silenced regardless of macrolide concentrations. All proteins expressed in our genetic circuit contain the same ribosomal binding site, with medium strength 0,764 (BBa_B0029) to decrease the effects of metabolic burden.

In our lab experiments we also included ermC, which is regulated by the same promoter as mphR. This gene ensures the biosensor survives even in high concentrations of macrolides [9]. It was placed under a constitutive promoter (BBa_J23106), so we can test the biosensor in a wide range of macrolide concentrations. In our genetic circuit it would be expressed together with mphr (Fig. 2). However, in our final biosensor this may not be necessary, since the concentrations of macrolide antibiotics in wastewater are rather low [4]. This would make the biosensor safer to use, since there would be no risk of releasing an antibiotic resistance gene into the environment.

Figure 2. Schematic overview of our genetic circuit. MphR and ermC are expressed together under a weak constitutive promoter. When MphR is unbound by macrolides, it remains bound to pMphR, preventing egfp transcription. In presence of macrolides MphR disassociates from pMphR and egfp can be expressed.


In order to build a biosensor, we decided to use Modular Cloning protocol [10], since it is modular, it allows for addition of more parts than RFC10 at the same time and is compatible with iGEM standards. Our final optical device has been divided into two level 1 assemblies: called a repressor cassette and an output cassette, so it is easier to spot which part has malfunctioned in case we face any issues (Fig. 3). We have constructed our repressor cassette using Modular Cloning protocol (see our experiments page). We sequenced the assembly to make sure it is correct before assembling the full optical device.

Figure 3. Construction of level 1 repressor cassette (level 1 MphR) and level 1 output cassette (level 1 EGFP).


ermC functionality

We have grown cells transformed with a repressor cassette (Fig. 4) in 1, 10, 50 and 100 µg/ml of erythromycin, clarithromycin and, as a control, spectinomycin. The results showed that ermC is expressed and provides resistance to macrolide antibiotics. The part can be found in iGEM’s registry. More about the test can be read on our experiments page.

Figure 4. Survival of cells transformed with a repressor cassette expressing ermC. OD - optical density. SPE - spectinomycin. CLA - clarithromycin. ERY - erythromycin.

Full Optical Device Functionality

We conducted a plate-reader experiment, where we grew cells transformed with the optical device in various erythromycin concentrations for 20 h. Non-transformed Escherichia coli cells grown in LB with 100 µl/ml erythromycin were a positive control. A negative control comprised non-transformed cells grown in LB with no antibiotic. More details about the experiment, as well as the protocol can be found on our experiments page. Although there seems to be a correlation between erythromycin correlation and the fluorescent output, the differences are too insignificant for the biosensor to be practical (Fig. 5). It was apparent our current design requires an improvement. See the Optical Design Troubleshooting section below. However, the test confirmed that pMphR and MphR are functional.

Figure 5. Normalized fluorescence values of cells containing our optical device grown in various erythromycin concentrations, as well as cells transformed with an output cassette expressing egfp without a repressor.



We have sequenced the optical device to see whether, despite seeing a correct length band, there has been a mistake that results with little egfp being expressed. The results show no differences between what we assembled and what we expected.

Our initial hypothesis was that the codon optimization of natural egfp sequences may have affected the secondary structure of the mRNA and decrease or block the translation of the protein. We examined the probability of this happening with an online tool ( [11] and decided this issue is unlikely to be a source of the issue. Another problem we considered was a suboptimal strength of the promoter: MphR may be overexpressed, thus always being bound to pMphR with erythromycin having little effect, causing such low EGFP production.


Is the output signal sufficient?

To investigate what is the issue, we performed several tests. First, we constructed a new level 1 output cassette, but with egfp replaced by sfgfp. Then we performed a plate reader experiment, where we measured fluorescence produced in absence of the repressor and compared the intensity of their fluorescent signal. For more details see our experiments page. sfGFP turned out to produce much stronger fluorescence (Fig. 6). We decided to use sfGFP instead as it may decrease the time needed for the measurement.

Figure 6. Normalized fluorescence of output cells transformed with output cassettes consisting of either sfgfp or egfp. The graph includes data from one biological repeat (comprising 3 technical repeats).

To see the effect of MphR on egfp expression, we performed a plate-reader experiment, where we measured the fluorescence of level 1 output cassette, which does not express MphR gene so there is no repressor present. We also measured fluorescence of the optical device in various erythromycin concentrations for comparison. For more details see our experiments page. We did observe that MphR represses the egfp, but the difference between the output of cells expressing and not expressing the repressor is not significant (more on our results page. Worth mentioning, we were already expecting such results since in our mathematical model it can be seen that the increase in macrolide concentrations result only in slight changes of the fluorescence (see our modelling page). Nonetheless, we wanted to test our system in the laboratory to confirm the model and the experiment were in accordance.

Can a weaker promoter improve the output signal?

To assess whether mphR overexpression can be responsible for low sensitivity, we run a MATLAB scan with five different promoter strengths with constant intervals between 0 and 0.02 polymerase/second. As expected, the output signal was higher when less repressor was being produced (Fig. 7). For that reason we decided to construct a new optical device, where we replaced a constitutive promoter with an inducible one.

Figure 7. 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 the initial design, the highest line represents the output with no repressor present.


We had four potential ways to improve the range and sensitivity of our biosensor:

  1. Constructing a new optical device, where the constitutive promoter was replaced by an inducible pBAD promoter (BBa_I0500) [12]. This would allow us to test various induction strengths and select the most optimal one. The results showed improved sensitivity, but it was still not sufficient for the needs of wastewater treatment plants (Fig. 8).

  2. Figure 8. A. New genetic circuit for the inducible optical device. B. OD normalized fluorescence values produced by TOP10 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 one repeat (comprising 3 technical repeats).

  3. Constructing alternative inducible optical devices with MphR mutants with changes to the ligand binding sites. We expect these changes to improve the binding affinity of MphR to erythromycin (to know more, see our modelling page). One of the ordered mutants (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. 9).

  4. Figure 9. 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).

  5. Concentrating macrolides inside the biosensor cells by using a modified strain with removed element of several multidrug resistance pumps and altered FhuA pore with a constantly open conformation, which leads to their hyperporination [13]. The strain showed improved sensitivity compared to TOP10, since fluorescence produced in samples containing different macrolide concentrations showed greater and quicker separation (Fig. 10).

  6. Figure 10. 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).

  7. Concentrating macrolides in the sample using isoelectric focusing on a gel [14] as a proof of concept for concentrating using paper-based microfluidics. Even though the initial tests were inconclusive (see our results page), this technique may work with a more specialized device or gel optimized for small analytes. More information on our concentration attempts can be found on our design page.


Although we found many potential ways of improving the sensitivity of our optical device, there is still a lot of research to be done before it can be used for the purpose of optimizing pharmaceutical removal in wastewater treatment plants. The next steps would include:

  1. Improving reporter gene expression by using a stronger ribosomal binding site, changing the distance between ribosomal binding site and the initiation codon or using an enhancer sequence [15].
  2. Testing improved design with MphR mutant in the modified strain (after confirming it can survive in wastewater).
  3. Constructing the optical device in a low copy number plasmid or integrating the optical device into a bacterial chromosome. This may prevent plasmid loss and improve sensitivity of the biosensor [16].
  4. Constructing a biosensor with a more sensitive signal than an optical output, such as electrochemical biosensor based on the Mtr pathway. More can be read on our contributions page in section Mtr pathway in Escherichia coli.
  5. Further research methods for concentrating macrolides in the samples.


1. Agency for Healthcare Research and Quality. (2018). 2017 national healthcare quality and disparities report (Report No. 18-0033-EF). U.S. Department of Health and Human Services. https://
2. 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
3. Verordnung des UVEK zur Überprüfung des Reinigungseffekts von Massnahmen zur Elimination von organischen Spurenstoffen bei Abwasserreinigungsanlagen, 814.201.231 § 2 (2016).
4. Kanoh, S., & Rubin, B. K. (2010). Mechanisms of action and clinical application of macrolides as immunomodulatory medications. Clinical Microbiology Reviews, 23(3), 590-615. doi:10.1128/cmr.00078-09
5. Kasey, C. M., Zerrad, M., Li, Y., Cropp, T. A., & Williams, G. J. (2018). Development of Transcription Factor-Based Designer Macrolide Biosensors for Metabolic Engineering and Synthetic Biology. ACS Synthetic Biology, 7(1), 227–239.
6. Zheng, J., Sagar, V., Smolinsky, A., Bourke, C., LaRonde-LeBlanc, N., & Cropp, T. A. (2009). Structure and function of the macrolide biosensor protein, MphR(A), with and without erythromycin. Journal of Molecular Biology, 387(5), 1250-1260. doi:10.1016/j.jmb.2009.02.058
7. Gardner, L., Zou, Y., Mara, A., Cropp, T., & Deiters, A. (2011). Photochemical control of bacterial signal processing using a light-activated erythromycin. Molecular Biosystems, 7(9), 2554. doi: 10.1039/c1mb05166k
8. Yi, H., Li, M., Huo, X., Zeng, G., Lai, C., & Huang, D. et al. (2019). Recent development of advanced biotechnology for wastewater treatment. Critical Reviews In Biotechnology, 40(1), 99-118. doi: 10.1080/07388551.2019.1682964
9. UniProt: a worldwide hub of protein knowledge. (2018). Nucleic Acids Research, 47(D1), D506-D515. doi: 10.1093/nar/gky1049
10. Haddock, Traci & Densmore, Douglas & Appleton, Evan & Carr, Swati & Iverson, Sonya & Freitas, Monique & Jin, S. & Awtry, Jake & Desai, Devina & Lozanoski, Thomas & Shah, Pooja & Agarwal, Yash & Lewis, Kathleen & Pacheco, Alan. (2015). BBF RFC 94: Type IIS Assembly for Bacterial Transcriptional Units: A Standardized Assembly Method for Building Bacterial Transcriptional Units Using the Type IIS Restriction Enzymes BsaI and BbsI.
11. Reuter, J. S., & Watson, R. M. (2017). Welcome to the Predict a Secondary Structure Web Server. Predict a Secondary Structure Web Server.
12. Lee, N., Gielow, W., & Wallace, R. (1981). Mechanism of araC autoregulation and the domains of two overlapping promoters, Pc and PBAD, in the L-arabinose regulatory region of Escherichia coli. Proceedings Of The National Academy Of Sciences, 78(2), 752-756. doi: 10.1073/pnas.78.2.752
13. 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, AAC.01882-16. doi: 10.1128/aac.01882-16
14. Zhao, C., Ge, Z., & Yang, C. (2017). Microfluidic Techniques for Analytes Concentration. Micromachines, 8(1), 28.
15. Kondo, T., & Yumura, S. (2019). Translation enhancement by a Dictyostelium gene sequence in Escherichia coli. Applied Microbiology And Biotechnology, 103(8), 3501-3510. doi: 10.1007/s00253-019-09746-7
16. Hicks, M., Bachmann, T., & Wang, B. (2019). Synthetic Biology Enables Programmable Cell‐Based Biosensors. Chemphyschem, 21(2), 132-144. doi: 10.1002/cphc.201900739

Special thanks to HSY for all their support

Kemistintie 1, Espoo, Finland