Our project has greatly benefited from the work of previous iGEM teams, whose contributions have shaped the foundations upon which we built. In this section, we aim to continue this collaborative spirit by highlighting the key reusable contributions generated by our team for future iGEM participants.
PFOA-Dependent Response
As part of our project, we focused on the detection and quantification of perfluorooctanoic acid (PFOA). Specifically, we investigated a detection system based on E. coli cells transformed with a construct containing a promoter that is upregulated in the presence of PFOA, coupled to an mRFP1 reporter gene (composite part : BBa_25O14TR6).
Using this genetic part in a low-intermediate copy plasmid (p15A ORI), we successfully demonstrated a PFOA-dependent normalized fluorescent response in E. coli MG1655 after 24 hours of incubation. The measured fluorescence (RFP/OD) correlates with PFOA concentration and can be accurately modeled using a second-degree polynomial regression. Based on our calibration curve obtained with known PFOA concentrations, this model allows for the prediction of unknown PFOA concentrations from fluorescence data.
Figure 1 : Graphical representation of PFOA concentration (µM) as a function of the measured RFP fluorescence/OD ratio, with the fitted polynomial regression curve and the 95% confidence interval shown in red.
This mechanism could be used by future teams working on PFAS to facilitate PFOA detection, or it could be further optimized to improve performance.
It is important to note that for practical use of this biosensing method or kit, an appropriate calibration must be performed for each measurement session. This involves preparing a standard curve with known concentrations of PFOA and measuring the corresponding reporter signal. Only by referencing this calibration can the fluorescence output be accurately translated into quantitative concentrations of PFOA.
Identification of Additional PFOA-Responsive Promoters
In addition to our tested construct, we identified several other promoters that drive genes significantly upregulated in the presence of 100 µM PFOA after 6 hours of exposure. These promoters exhibited low transcriptional background (few reads in the absence of PFOA) and strong expression in PFOA-treated conditions, making them promising candidates for future detection systems. Although we did not have time to experimentally validate these promoters within our project, they represent valuable starting points for future iGEM teams wishing to build upon our approach and establish new quantitative relationships between PFOA concentration and reporter expression.
Based on the transcriptomic study by Wintenberg et al., 2025, we identified the following two promoters of particular interest:
- BBa_257SBK5S, corresponding to the appY gene promoter (DLP12 prophage; DNA-binding transcriptional activator AppY), which is significantly upregulated in the presence of PFOA. Average read counts increased from 318 (control condition) to 1016 (PFOA 100 µM), corresponding to a log₂ fold change of +1.5.
- BBa_25IF1M23, corresponding to the malE gene promoter (maltose ABC transporter periplasmic binding protein), which showed an even stronger upregulation, with average reads increasing from 219 (control) to 1179 (PFOA 100 µM), corresponding to a log₂ fold change of +2.28.
These promoters, identified through transcriptomic screening, could thus serve as valuable parts for future teams interested in engineering improved biosensors for PFOA detection.
Enhancing biosensor specificity
When designing a biosensor, specificity is a critical factor. In a classical design composed of a sensitive promoter and a reporter gene, as described above, the risk of false positives is significant. There is a substantial chance that a signal other than the target molecule could activate the promoter driving the biosensor, leading to unintended reporter expression.
To address this, we aimed to generate a signal only if two promoters were active simultaneously, thereby greatly reducing the likelihood of false positives and increasing the specificity. The choice of promoters was therefore critical: it was necessary to select promoters from genes involved in different signaling or metabolic pathways to avoid any co-regulation that could also produce false positives in this system.
Figure 2 : Diagram illustrating the robustness of the response which appears if and only if both promoters are activated at the same time.
To test this design, we were only able to work with inducible promoters pLac and pTet (Figure 3), which we subjected to different conditions. E. coli DH5α cells were transformed with the construct integrated to a low copy plasmid (pSEVA261) or left untransformed as a control. These bacteria were then exposed to induction conditions with zero, one, or both promoters activated.
Figure 3: Schematic view of the design to assess the robustness of the system.
We observed that luminescent output was only present under double induction, while single induction with IPTG alone did not trigger any detectable luminescence (as predicted in the design Figure 2). This demonstrates that the system can effectively reduce false positives by requiring simultaneous activation of both promoters (Figure 4).
This clearly demonstrates that this mechanism significantly improves signal specificity. Moreover, this approach is transferable to any luminescent biosensor design that relies on promoter-based regulation. We hope that this strategy will be useful to future iGEM teams, helping them enhance the specificity of their biosensor designs.
Figure 4: Normalized luminescence signal in E. coli DH5α transformed with either the collection part or the empty plasmid, under different induction conditions: single induction with 20 µM IPTG, double induction with 20 µM IPTG and 10 ng/mL aTc, or without induction.
* : p < 0,05 between the condition “Double induction 20µM IPTG + 10 ng/mL aTc” and “non induction”, ** : p < 0,01 for the same conditions. # : p < 0,05 between the condition “Double induction 20µM IPTG + 10 ng/mL aTc” and “Induction 20µM IPTG”, ## : p < 0,01 for the same conditions.
Every details concerning the collection part is documented in the collection part registry : registry.igem.org/collections/6eb97f16-f618-4ebf-816c-82ccfb64b856.
Labrys portucalensis F11 transformation
As part of our project, we initially aimed to work with the non-model bacterium Labrys portucalensis, known for its remarkable resistance to PFAS. However, to the best of our knowledge, no transformation protocol had been established for this species. Therefore, we needed to develop one in order to enable genetic manipulation of this strain. We were fortunate to benefit from the invaluable help of Véronique, who successfully achieved this feat.
A conjugation-based transformation protocol was developed to introduce plasmids from E. coli donor strains into Labrys portucalensis. Donor E. coli strains carrying various pSEVA plasmids (pSEVA621, pSEVA631, pSEVA651, pSEVA661) and the helper strain EGE214 were grown overnight in TSB medium supplemented with gentamicin (10 µg/mL) or kanamycin (50 µg/mL). Cultures of L. portucalensis were grown slowly for several days at 28 °C to reach an OD > 1. Equal optical densities of donor and recipient cultures were mixed, pelleted, resuspended in a small TSB volume, and spotted onto sterile 0.45 µm filters placed on TSA plates. After overnight incubation at 30 °C, filters were transferred to TSA plates containing ampicillin (100 µg/mL) and gentamicin (30 µg/mL) for selection of transconjugants. Colonies appearing after 5–7 days were reisolated on selective medium. Plasmid extraction from successful conjugations (notably with EGE214 + EGE460) confirmed the presence of recombinant plasmids by restriction digestion, demonstrating effective plasmid transfer into L. portucalensis through conjugation.
This represents a significant breakthrough that can be reused by all future teams working with this strain, whose remarkable characteristics continue to impress the scientific community studying PFAS.
One Health Matrix
What is the Ethical Matrix?
The Ethical Matrix (Figure 5) is a tool that maps all actors potentially affected by a synthetic biology project across three fundamental ethical principles:
- Autonomy: The capacity of stakeholders to make informed decisions and maintain control over factors affecting their lives
- Well-being: The physical, mental, and social health impacts on each stakeholder group
- Justice: Fair distribution of benefits and risks, addressing environmental justice and equity concerns
By evaluating each stakeholder group through these three lenses, teams can identify ethical tensions, prioritize concerns, and design projects that are not only scientifically sound but also socially responsible.
Why iGEM Teams Need This Tool
Synthetic biology projects inherently involve multiple stakeholders—from local communities and regulatory bodies to industrial partners and environmental ecosystems. Many teams focus primarily on technical feasibility, or overlook certain groups, like non-human actors. The Ethical Matrix addresses this gap.
How to Use the Ethical Matrix
Step 1: Identify All Stakeholders
Begin by listing every group affected by your project. Think broadly—include:
- Direct users (e.g. municipalities, industries, laboratories)
- Affected communities (e.g.residents near contamination sites, vulnerable populations)
- Regulatory bodies (e.g. environmental agencies, safety committees)
- Economic actors (e.g. competing technologies, suppliers, investors)
- Environmental actors (e.g.ecosystems, wildlife, plants)
- Scientific community (e.g.researchers, future iGEM teams)
- Ethical watchdogs (e.g. NGOs, advocacy groups, journalists)
Step 2: Analyze Each Stakeholder Against Three Principles
For each stakeholder, systematically evaluate:
Autonomy (+/−/+−)- Can they make informed decisions about the technology?
- Do they have agency in how it affects them?
- Are they consulted in development and deployment?
- Does the project improve or harm their health/safety/quality of life?
- Are there unintended consequences?
- What are the long-term impacts?
- Are benefits and risks fairly distributed?
- Does the project address or worsen existing inequalities?
- Who bears the costs? Who reaps the benefits?
Use (+) for positive alignment, (−) for negative, and (+−) for mixed or ambiguous impacts.
Step 3: Identify Conflicts and Prioritize
Look for patterns:
- Stakeholders consistently rated (−) across all principles require immediate attention
- Conflicts between stakeholder interests indicate areas needing mediation or redesign
- Groups with (+−) ratings need clearer communication or modified approaches
Step 4: Use Insights to Guide Project Decisions
Our experience with the Ethical Matrix led to concrete decisions:
- We chose not to partner with PFAS producers because doing so would compromise our commitment to environmental justice and community well-being
- We prioritized water treatment operators as partners since they aligned with our goals and could implement the technology responsibly
- We developed the One Health framework after realizing the interconnected nature of human, animal, and environmental stakeholder groups
- We created equitable pricing models after identifying justice concerns around municipal access to our technology
Figure 5 : Ethical matrix example.
References
- Wintenberg, M. E., Vasilyeva, O. B., & Schaffter, S. W. (2025). Comparative Transcriptomic Analysis of Perfluoroalkyl Substances-Induced Responses of Exponential and Stationary PhaseEscherichia coli. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2025.02.18.638913
