Implementations

Our journey to tackle antimicrobial resistance began when this issue was brought to our attention and gained the support of a great majority of our team. Initially, we focused on developing a solution for the medical industry to combat Carbapenem-Resistant Klebsiella pneumoniae (CRKP) using the CRISPR-Cas9 mechanism. After thorough research, we determined that due to time constraints, biosafety regulations, and a lack of experience, we revised our plan to the BLIP conjugation system. Building on the same goal of addressing antimicrobial resistance, we envision expanding our project into the medical field through innovative and safe applications of BLIP. We came up with a few approaches:

Although we are tempted to test out our theory and put it to practical use to make tangible contributions against antimicrobial resistance. However, we are aware of the biosafety rules and the limited ability and experience of our iGEM team. We do not have the chance to extend our project to real medical implementations. If we have the opportunity to demonstrate that our project goes beyond theory, we still hope to propose a practical implementation that extends beyond the lab, which we have shifted our aim to environmental dry lab simulations, to develop simulations of real-world applications that address the root causes of AMR by targeting environmental reservoirs of antibiotic-resistant bacteria.

By focusing on intercepting resistant bacteria before they enter human populations and cause severe, untreatable infections, we aim to disrupt the cycle of resistance transmission at its source. This preventative approach could reduce the spread of resistance genes in both natural and clinical environments.

To extend our project’s impact beyond the lab, we brainstormed strategies to tackle antimicrobial resistance at its environmental roots. One promising approach is through online environmental implementation simulations that model AMR dynamics and intervention strategies in natural and engineered ecosystems. [4]

Antimicrobial resistance in the environment originates from sources such as residual antibiotics in wastewater, improper disposal of medications, and agricultural runoff harboring resistant bacteria. From there, they spread through food, water, and direct contact. These sources contribute to the spread of resistance through waterways, food chains, and direct contact, eventually impacting human health. Our focus centers on wastewater treatment and soil remediation as critical control points where antibiotic-resistant bacteria and resistance genes propagate towards humans. [5]

Conjugation in Wastewater Flow Simulation

Our core idea is to introduce donor bacteria carrying the BLIP gene into wastewater systems or treatment plants to mitigate AMR by inhibiting beta-lactamase resistance before these waters are reused in agriculture, animal husbandry, or released into the environment.

However, conjugation in flowing water presents challenges: effective gene transfer requires sustained contact between donor and recipient cells, which is limited by flow dynamics and compartmentalization of wastewater systems.

To capture this, we could model the wastewater collection system as compartments (pipes, aeration tanks, and channels), each with distinct flow speeds and mixing behaviors, simulating advection and dispersion processes. [6] This subdivision allows us to explore key factors, including:

- Injection Point: Evaluating the efficacy of BLIP donor introduction at various system locations (start, middle, end) to understand where conjugation and transconjugant formation peaks.

- Residence Time Requirements: Investigating how long donor and recipient cells must remain in proximity under varying flow speeds and mixing intensities to establish significant conjugation events.

- Dose Optimization: Creating heatmaps of transconjugant percentages along pipe lengths or tank depths for optimized BLIP donor dosing strategies.

Figure 1. The Different Types and Sizes of Sewer Lines in a Typical Wastewater Collection System [6][7]

Through this simulation framework, we aim to identify optimal operational parameters that maximize the inhibitory effect on beta-lactamase within wastewater environments, providing a scalable blueprint for environmental intervention against AMR.

Antimicrobial resistance genes and β-lactamase-producing bacteria are prevalent in soil environments due to the use of agricultural antibiotics, wastewater irrigation, and runoff. If our BLIP-conjugation system can be effectively applied in soil, it has the potential to reduce the spread of resistance genes before they reach human populations. [8]

Our approach involves introducing BLIP-carrying donor bacteria into the soil, where conjugation requires physical contact between donor and recipient cells. Therefore, our model will investigate how frequently such contacts occur under different soil conditions to optimize gene transfer in both moist and dry soils. [9]

In moist soil, continuous water films line the pore spaces, allowing bacterial cells to swim or slide, thereby increasing the opportunities for collisions and the formation of microclusters or biofilms. However, the flowing nature of this aqueous environment can also separate cells quickly after contact, reducing the contact time necessary for effective conjugation. Overall, moist soil offers higher chances of contact but lower stability. [10]

Conversely, in dry soil, water films break into isolated droplets, trapping bacteria in small pockets that restrict movement and reduce encounter frequency. However, when cells do make contact, they tend to remain in proximity longer, which favors successful conjugative transfer of the BLIP gene. Thus, dry soil provides fewer contact opportunities but greater contact stability.

We also target antimicrobial hotspots such as the rhizosphere, where nutrient availability promotes dense bacterial growth, increasing the likelihood of conjugation. Soil aggregates and biofilms further concentrate bacterial communities, leading to frequent cell-to-cell contact and enhanced gene transfer efficiency.

By understanding and modeling conjugation dynamics in these diverse soil microenvironments, we aim to identify effective strategies to deploy BLIP conjugation for reducing antimicrobial resistance spread in natural settings.

In this implementation, we proposed to manually insert the BLIP gene into the BBa_J435320 plasmid, which carries ampicillin resistance (β-lactamase). This design aims to explore whether the co-expression of BLIP and β-lactamase within the same genetic construct can modulate bacterial survival under antibiotic stress. The experimental validation would involve transforming this construct into E. coli DH5α and culturing it on ampicillin plates. If the BLIP expression successfully inhibits β-lactamase, the bacteria would lose resistance and fail to survive—demonstrating BLIP’s effective inhibition when expressed intracellularly.

While the initial prototype employs a constitutive promoter to continuously express BLIP, resulting in immediate cell death, this system provides a foundation for developing a conditional survival circuit. In such a design, BLIP expression would be placed under the control of an inducible promoter, such as pBAD (arabinose-inducible) or pTet (tetracycline-inducible).[11][12] This would allow dynamic regulation of BLIP activity—where expression could be toggled “on” or “off” depending on environmental signals or user-defined inducers.

By linking BLIP activation to an environmental cue (e.g., presence of pollutant, nutrient limitation, or population density via quorum sensing), the system could serve as a programmable self-regulation mechanism. Under normal conditions, the promoter remains inactive, allowing bacterial growth even in ampicillin-containing environments. Upon induction, BLIP expression would suppress β-lactamase function, rendering the bacteria sensitive to ampicillin and leading to controlled cell death.

This concept transforms BLIP from a static inhibitory gene into a functional biocontrol module—an engineered kill-switch capable of responding to specific stimuli. The circuit could thus be integrated into broader biosafety frameworks, enabling conditional containment and environmentally responsive genetic control within synthetic biology applications.

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2. Yuan, J.; Huang, W.; Chow, D.-C.; Palzkill, T. Fine Mapping of the Sequence Requirements for Binding of β-Lactamase Inhibitory Protein (BLIP) to TEM-1 β-Lactamase Using a Genetic Screen for BLIP Function. Journal of Molecular Biology 2009, 389 (2), 401–412. https://doi.org/10.1016/j.jmb.2009.04.028.
3. Lin, D. M.; Koskella, B.; Lin, H. C. Phage Therapy: An Alternative to Antibiotics in the Age of Multi-Drug Resistance. World Journal of Gastrointestinal Pharmacology and Therapeutics 2017, 8 (3), 162–173. https://doi.org/10.4292/wjgpt.v8.i3.162.
4. Larsson, D. G. J.; Flach, C.-F. Antibiotic Resistance in the Environment. Nature Reviews Microbiology 2021, 20 (5), 1–13. https://doi.org/10.1038/s41579-021-00649-x.
5. Ahmad, I.; Malak, H. A.; Abulreesh, H. H. Environmental Antimicrobial Resistance and Its Drivers: A Potential Threat to Public Health. Journal of Global Antimicrobial Resistance 2021, 27 (2213-7165). https://doi.org/10.1016/j.jgar.2021.08.001.
6. WaterOperator.org Blog | Wastewater Collection System Components. wateroperator.org. https://wateroperator.org/blog/wastewater-collection-system-components.
7. 5032683. Lec 1 (wastewater collection system). Slideshare. https://www.slideshare.net/slideshow/lec-1-wastewater-collection-system/69289097
8. Wang, F.; Fu, Y.-H.; Sheng, H.-J.; Topp, E.; Jiang, X.; Zhu, Y.-G.; Tiedje, J. M. Antibiotic Resistance in the Soil Ecosystem: A One Health Perspective. Current Opinion in Environmental Science & Health 2021, 20, 100230. https://doi.org/10.1016/j.coesh.2021.100230.
9. Osbiston, K.; Oxbrough, A.; Fernández-Martínez, L. T. Antibiotic Resistance Levels in Soils from Urban and Rural Land Uses in Great Britain. Access Microbiology 2020, 3 (1). https://doi.org/10.1099/acmi.0.000181.
10. Bongiovanni, D.; Masson, S.; Chialva, M.; Fiorilli, V.; Votta, C.; Lanfranco, L.; Stefanini, I. Impact of Urbanization on Antimicrobial Resistance in Soil Microbial Communities. Scientific Reports 2025, 15 (1). https://doi.org/10.1038/s41598-024-84945-5.
11. pBAD Expression System | Thermo Fisher Scientific - US. Thermofisher.com. https://www.thermofisher.com/tw/zt/home/life-science/protein-biology/protein-expression/bacterial-protein-expression/pbad-expression-system.html (accessed 2025-10-07). 12. Part:BBa K2800027 - parts.igem.org. Igem.org. https://parts.igem.org/Part:BBa_K2800027 (accessed 2025-10-07).