Design

Overview

In this study, we started with developing a biosensor system utilizing E. coli’s endogenous CueR as a sensor protein to detect copper ions (Cu²⁺) through a streamlined approach that minimizes genetic modifications. Our system employs a single transcriptional unit (TxU) with a CueR-activated promoter (pCopA) driving the expression of the fluorescent protein mVenus. We evaluated three fluorescent proteins—mVenus, sfGFP, and AmpliCP—focusing on mVenus for its rapid maturation and high brightness, ensuring timely and accurate detection. Then we introduce another plasmid expressiong more CueR in this system to understand how CueR expression could affect the responds to Cu2+. By constructing a promoter library with 96 unique promoters, we apply high-throughput screening to fine-tune CueR expression for optimal performance across varying environmental conditions. To analyze the relationship between promoter sequences and fluorescence responses, we utilized machine learning techniques on our experimental dataset of 88 samples, allowing us to identify the most effective promoter candidates.

Moreover, we validated the specificity and orthogonality of our detection system by testing the response to various metal ions, confirming its selective sensitivity to copper. Furthermore, we assessed the impact of Cu2+ concentrations on E. coli growth, noting that concentrations exceeding 3 mM inhibit growth, which is critical for operational safety in environmental applications. Our findings demonstrate the potential of our CueR-based biosensor for effective and reliable copper ion detection, with applications in bioremediation and environmental monitoring.

Building upon the work of previous teams, our design incorporates more engineering considerations and employs novel techniques to elucidate the relationship between the sensor protein and its response in the detection system. Our findings demonstrate the potential of our CueR-based biosensor for effective and reliable copper ion detection, with subsequent projects poised to benefit from our insights and data.

Reporter plasmid

Previous teams often overlooked the influence of E. coli's endogenous CueR on similar systems. In our approach, we tested a system that incorporates only a single response plasmid, demonstrating a strategy that minimizes modifications by utilizing the bacterium’s own receptor. This allows for a more streamlined and efficient detection mechanism, leveraging E. coli's natural components to enhance the system's functionality and reliability in sensing copper concentrations.

We initially utilized E. coli's endogenous CueR as the sensor protein to operate our system. This approach allowed us to introduce only a single transcriptional unit (TxU), which includes a CueR-activated promoter, pCopA, driving the expression of a downstream fluorescent protein.

In a detection system, we aim to obtain results as quickly as possible to meet the demand for rapid response. However, this goal is constrained by various factors, including transcription duration, the synthesis speed of response proteins, and signal processing. Previous teams using similar systems, such as Team Oxford 2016, employed RsRed for fluorescence signaling, which resulted in a detection time of around 14 hours to observe fluorescence signals, along with a relatively low signal intensity.

Fluorescent proteins with faster maturation rates can generate fluorescence bursts similar to transcriptional bursts, resulting in higher detectable fluorescence intensities above the detection limit. To account for the potential impact of fluorescence on the system, we tested three different fluorescent proteins: mVenus, sfGFP, and AmpliCP. Our choice of mVenus was based on a balance between its fast maturation rate (<20 min) and high brightness, as supported by a reference. [1]. These characteristics make mVenus particularly suitable for generating a rapid and strong fluorescent signal, ensuring that our system can provide timely and accurate measurements of copper concentration. By comparing the performance of all three proteins, we aimed to identify the one that would minimize any delays in response while maintaining optimal signal intensity.

In the downstream of 02 plasmid, we utilized pMB1 origin, which can produce copies of 15 to 30. It is the origin of replication, which replicate independently of the genetically modified DNA. Furthermore, the downstream CmR cassette allows bacteria carrying the plasmid with this cassette to express antibodies of chloramphenicol.

CueR plasmid

Although CueR is an endogenous copper sensor protein in E. coli, we are uncertain whether the self-produced levels of CueR are sufficient, as we currently do not have solid data proving its insufficiency. Interestingly, our single-plasmid data already show clear and robust response curves. However, we aim to explore whether altering CueR concentration could further modify the overall system response. Therefore, we introduced a secondary system to control CueR expression by constructing our 01 plasmid, which consists of a stable, constitutive promoter J23119 driving the expression of the CueR gene. This system allows us to investigate the effects of CueR overexpression on system sensitivity.

The downstream of our second plasmid contains the p15A origin that derived from p15A plasmid and provides an origin of replication.15A origin provides stable replication of the plasmid, which can be beneficial for maintaining plasmids with potentially toxic genes or large inserts.

A kana cassette refers to a genetic sequence that confers resistance to the antibiotic Kanamycin (k+). The downstream Kana cassette in 01 plasmid allows bacteria to express the antibodies of Kanamycin. When Kanamycin is added, the modified bacteria can survived, while the unsuccessully modified E. coli would die.

We performed a two-fold serial dilution starting from 2 mM Cu²⁺ to test both the single-plasmid and dual-plasmid systems. The experiment was carried out in a 96-well plate with a 150 μL volume per well. The cultures were incubated overnight in a plate reader, and fluorescence was measured to assess system response.

Promotor Library screening

Specifically, we designed a promoter library containing 96 unique promoters, each 51 bp in length, sourced from the E.coli genome. These promoters span a wide range of strengths, allowing us to fine-tune CueR expression in the system and optimize its performance under varying environmental conditions. This diversity in promoter strength provides flexibility in adjusting the system's sensitivity, ensuring reliable copper detection across a broad range of concentrations. We applied Golden Gate Assembly(GGA) to inset the promoter upstream CueR and picked up 96 single clones. These 96 clones were cultured overnight in 1 mL LB medium, then diluted 100-fold. Each clone was subsequently exposed to conditions with final Cu²⁺ concentrations of 1 mM and 0 mM. The mVenus fluorescence was measured using 510 nm excitation and 550 nm emission wavelengths. We then employed machine learning (ML) techniques to analyze the relationship between sequences and responses data using our experimental dataset of 88 samples.

Specificity validation of our system

To sum up, we have two systems in two different plasmids. The first system involved the use of promoters in the promoter library. The result is the CueR protein. The second system starts with the CueR protein, which detects and binds with copper ions, which activates the promoter and results in the expression of a fluorescent protein, mVenus.

To ensure the robustness of our detection system, it is crucial to validate both its specificity and orthogonality. Specificity in a detection system refers to the ability to respond exclusively to the target analyte—in this case, copper ions—while ignoring other ions or environmental factors. High specificity is essential to avoid false positives or misleading results from unrelated substances. Orthogonality is another key characteristic, ensuring that the system's response to one stimulus (such as copper ions) does not interfere with the detection of other inputs or outputs in a broader biological or environmental context.

We designed additional experiments to validate these properties, beginning with an assessment of copper ion specificity. Copper ion specificity is critical for systems that rely on selective binding or response to copper, such as biosensors using proteins like CueR. We selected several metal ions for comparison: Ca²⁺, Na⁺, K⁺, Cu²⁺, Mg²⁺, and Zn²⁺. These ions were chosen because they are commonly found in biological systems and culture environments, and their presence could potentially interfere with the system's response if it lacks proper specificity. For instance, calcium (Ca²⁺) and magnesium (Mg²⁺) are prevalent in cellular processes, while sodium (Na⁺) and potassium (K⁺) are essential for maintaining ion gradients in living organisms. Zinc (Zn²⁺), like copper, is a transition metal and serves as a crucial cofactor in many enzymes, making it an important control to differentiate the system's response to similar ions.

Understanding the effect of Cu2+ concentration on E.coli cell growth

Based on our HP work, real-world copper ion detection concentrations can vary from 2 mM to 10 µM; thus, we need to ensure that our system functions effectively within this range. To further investigate the effect of Cu²⁺ concentration on E. coli growth, we designed an experiment using both E. coli strains containing the CueR-pCoA system and empty plasmid controls. Cultures were grown to the exponential phase and then exposed to various concentrations of Cu²⁺, focusing on levels up to 12.5 mM, which were previously identified as inhibitory to bacterial growth. Additionally, as copper ions are toxic heavy metals, understanding their impact on bacterial growth is crucial for developing reliable biosensors that can operate safely and effectively in environmental monitoring scenarios.

Flexible Application Scenarios

After having an array of systems for detecting different concentrations of copper, we can apply each to different scenarios.

• High Copper Concentration scenarios

  Industrial discharges result in high copper concentrations. Factories and industrial sites that process copper may release wastewater containing high copper concentrations. This is harmful to the environment, leading to soil and water contamination, which negatively impacts plant and animal life[4].

  In addition, ore processing, such as crushing, flotation, and smelting ores, can raise the concentration of copper ions.

  During the recycling process of old batteries, especially lithium and nickel-cadmium batteries, higher concentrations of copper ions may be released.

• Medium Copper Concentration scenarios

  Copper ions on roads and building surfaces may be washed off during urban surface runoff. Resulting in the accumulation of copper ions in the drainage system as rainwater washes away.

  The use of copper-containing pipes, faucets, and certain cleaning agents in the home may result in a concentration of copper ions in wastewater.

• Low Copper Concentration scenario
  • In nature, freshwater lakes and rivers contain lower concentrations of copper. Seawater contains various minerals, and the concentration of copper ions is usually very low.

The development of a cell-free transcription test strip

To achieve everyday applicability, we envision this system's final form as a cell-free, in vitro transcription system on a test strip. Drawing inspiration from previous iGEM teams, we designed a simplified version and conducted preliminary tests. This version includes cell lysate containing the target pathway, supplemented with additional ATP, dNTPs, essential Mg²⁺, and amino acids.

We first focused on testing the conditions under which bacterial cells can be fully lysed without leaving behind any biosafety risks. After determining these optimal lysis conditions, we validated whether our system could function effectively within this cell-free lysate.

This approach is designed to make the detection process more accessible, safe, and efficient for real-world applications, as it eliminates the need for live bacterial cultures while preserving the functionality of the biosensing system.

References

[1]Paolo Guerra, Luc-Alban Vuillemenot, et al. Systematic In Vivo Characterization of Fluorescent Protein Maturation in Budding Yeast ACS Synthetic Biology 2022 11 (3), 1129-1141 DOI: 10.1021/acssynbio.1c00387

[2] Thomason MKBischler T, Eisenbart SKFörstner KU, Zhang A, Herbig A, Nieselt K, Sharma CM, Storz G.2015.Global Transcriptional Start Site Mapping Using Differential RNA Sequencing Reveals Novel Antisense RNAs in Escherichia coli. J Bacteriol197:.https://doi.org/10.1128/jb.02096-14

[3] Bhandari N, Khare S, Walambe R, Kotecha K. 2021. Comparison of machine learning and deep learning techniques in promoter prediction across diverse species. PeerJ Computer Science 7:e365 https://doi.org/10.7717/peerj-cs.365

[4] Engler, Carola, et al. “A Golden Gate Modular Cloning Toolbox for Plants.” ACS Synthetic Biology, vol. 3, no. 11, 20 Feb. 2014, pp. 839–843, https://doi.org/10.1021/sb4001504. Nayl, A.A., et al. “The Nanomaterials and Recent Progress in Biosensing Systems: A Review.” Trends in Environmental Analytical Chemistry, vol. 26, June 2020, p. e00087, https://doi.org/10.1016/j.teac.2020.e00087. Accessed 13 Jan. 2022.

[5] Stoyanov, Jivko V., et al. “CueR (YbbI) of Escherichia Coli Is a MerR Family Regulator Controlling Expression of the Copper Exporter CopA.” Molecular Microbiology, vol. 39, no. 2, Jan. 2001, pp. 502–512, https://doi.org/10.1046/j.1365-2958.2001.02264.x. Accessed 18 Aug. 2022.5

[6] “Team:Oxford/Parts - 2016.Igem.org.” Igem.org, 2016, 2016.igem.org/Team:Oxford/Parts. Accessed 25 Aug. 2024.

[7] Valenta, R.K., et al. “Re-Thinking Complex Orebodies: Consequences for the Future World Supply of Copper.” Journal of Cleaner Production, vol. 220, May 2019, pp. 816–826, https://doi.org/10.1016/j.jclepro.2019.02.146.