1. Rational Design Strategy Based on Niche Matching

Breaking away from the conventional approach of stacking universal components in common model strains (e.g., E. coli) for traditional biosensors, we pioneered the "niche matching" design principle. Functional components were sourced from native microorganisms (the rice endophyte CML2) in the target environment (paddy fields). These components, evolved to respond to arsenic in real polluted environments (in terms of affinity, specificity, and dynamic range), are better adapted to complex field conditions—greatly enhancing the potential and reliability of translating the sensor from laboratory research to practical application.

2. "AND-Gate-Like" Circuit Design for Fundamental Leakage Resolution

To address the industry-wide challenge of unavoidable basal leakage in repressive circuits, we moved beyond traditional strategies like promoter weakening or RBS optimization. Instead, we integrated higher-order synthetic biology logic by incorporating a split-GFP self-assembly system into the sensing circuit, constructing an "AND-gate-like" circuit. This design requires two events to occur simultaneously for strong fluorescent signal production, significantly reducing background noise at the system level (rather than the component level). This represents a paradigm shift from "suppressing leakage" to "neutralizing leakage," essentially improving the signal-to-noise ratio.

3. Multi-Round Closed-Loop DBTL Engineering Optimization Process

Rather than treating the construction process as a simple linear operation, we strictly implemented an engineering iterative workflow of "Design-Build-Test-Learn (DBTL)." When the initially constructed sensor exhibited high basal levels, we systematically investigated factors ranging from protein expression levels to protein-DNA binding efficiency through hypothesis-driven research, with support from computational tools like AlphaFold. This data-driven, closed-loop engineering mindset ensured rapid problem localization and precise optimization, demonstrating strong engineering expertise in synthetic biology.

1. Novel Ecologically Relevant Sensing Elements—CML2-Derived ArsR Protein and Its Cognate Promoter

We isolated and characterized a new arsenic-sensing element: the ArsR protein from the rice endophyte CML2 and its cognate promoter/operator sequence. Compared to homologous proteins from model strains like E. coli, this element offers two key potential advantages:

• Environmental Adaptability: Its sensing properties, shaped in the complex, real-world environment of the rice rhizosphere, may enable superior recognition of arsenic species in agricultural fields.
• Chassis Compatibility: Tailored for its native host CML2, this element provides an indispensable, highly compatible core component for in-situ construction of more complex, stable "detection-remediation" systems in the CML2 chassis.

2. Split-GFP Fusion Protein Design for Enhanced Stability

To address challenges in the split-GFP system, we designed and constructed a GFP1-10-linker-GFP11 fusion protein connected by a short linker. While retaining the "AND-gate-like" logical function, this design enhances intracellular stability by increasing the polypeptide's molecular weight and optimizing its structure. It provides an innovative engineering solution—referenceable for the broader community—to the common problem of small peptide fragment degradation.

1. Resolving Core Grassroots Pain Points, Developing "Low-Cost, Implementable" Linked "Detection-Remediation" Technology

Targeting farmers' dual predicament—"lack of rapid detection tools and eco-friendly remediation methods"—the project developed a solution tailored to grassroots needs through multiple rounds of technical optimization and field validation:

1.1 Implementation of High-Precision Detection Technology

Using E. coli DH5α as the chassis, we integrated core elements (ArsR-Pars system) from the rice endophyte Cupriavidus metallidurans CML2 to develop a "single-cell arsenic pollution biosensor." Farmers can use this sensor for rapid pollution screening without relying on professional institutions—avoiding detection costs of hundreds of yuan per test and a two-week waiting period.

1.2 Groundwork for Eco-Friendly Remediation Technology

Leveraging CML2's natural properties of "heavy metal enrichment and rice growth promotion," we first constructed the system using E. coli DH5α as the chassis. After successful engineering modification of CML2 in the future, elements can be reintroduced to upgrade to an "integrated detection-remediation bacterial agent." This agent will enable engineered bacteria to both provide arsenic concentration early warning via fluorescence and actively adsorb/immobilize arsenic ions through metabolic pathways—reducing arsenic uptake by rice at the source and safeguarding long-term soil productivity.

1.3 Scientifically Adapted Technical Route

By interviewing 5 domain experts (including Zhang Haimou and He Nisha), we avoided the risk of "working in isolation":

• Abandoned the initial plan to "modify CML2 as the sensor chassis" (due to incomplete engineering modification of CML2 and unstable heterologous gene expression), prioritizing E. coli DH5α to quickly verify technical feasibility.
• Through "promoter screening experiments" (using medium-strength promoters), increased the fluorescence intensity for low-concentration arsenic (0.05 mg/kg) detection by 30%, ensuring no mild pollution is overlooked.

2. Linking Stakeholders to Build a "Sustainable Governance System"

2.1 Field Demonstration and Farmer Empowerment

In the early stage, a 50-mu demonstration field was established in Lingmi Village, Jiangxia District, Wuhan, Hubei Province. Farmers were provided with free sensors and CML2 bacterial agents, alongside on-site operation guidance. Over 50 farmers were trained to master "rapid arsenic pollution detection + ecological remediation" skills, transforming them from "passive bearers of environmental risks" to "active managers of soil health."

2.2 Radiating Social Value

Through technical practice and popular science linkages, team members raised awareness of arsenic pollution hazards (e.g., "long-term exposure induces skin cancer and impairs children's cognitive development").

2.3 Planning for Long-Term Mechanisms

In the future, we plan to collaborate with local environmental protection agencies and agricultural bureaus to establish an "arsenic pollution monitoring network": Farmers will upload detection data via sensors, and the team will aggregate and analyze the data to provide "personalized remediation recommendations" (e.g., "1 application of bacterial agent for mild pollution, 2 applications for moderate pollution"). This will form a sustainable closed loop of "detection → feedback → remediation → optimization."

1. Creating a "Community Toxin Detection" Themed Teaching Module, Reconstructing Synthetic Biology Teaching Logic

For high school synthetic biology workshops, we designed immersive "from laboratory to community" teaching—distinct from traditional abstract experiments:

1.1 Innovation in Teaching Content

Abandoning the traditional protocol of "inducing the BBa_R0040-Pplac-GFP circuit with IPTG," we used the project's core elements (the ArsR-Pars system of CML2) as teaching carriers. Students engaged in practical work focused on "detecting environmental toxins for the community," using self-constructed engineered bacteria to test arsenic pollution in local tap water, river water, and mineral water samples.

1.2 Realization of Educational Value

Three key cognitive breakthroughs were achieved:

• From Abstract to Concrete: Strongly linking classroom theories (e.g., "repressive circuits," "gene expression") to "drinking water safety." When students observed "green fluorescence in test tubes with river water samples," they could intuitively understand the complete mechanism of "ArsR protein recognizing arsenic → releasing repression of the Pars promoter → GFP expression and luminescence"—rather than memorizing principles by rote.
• Inspiring a Sense of Mission: 96% of participating students reported, "I realized synthetic biology is not a laboratory game—it can directly protect my family's health." Some students proactively proposed, "I want to promote this technology to farmland in my hometown," fostering early penetration of the concept of "technology serving society."
• Conveying Ecological Component Selection Logic: By comparing the application effects of "laboratory components (e.g., BBa_R0040) and niche components (ArsR-Pars)," students learned that "selecting ecologically relevant components is key to solving practical problems." For example, the stability of the ArsR-Pars system in paddy field environments far exceeds that of heterologous components commonly used in laboratories.

2. Building a Diversified "Online + Offline" Popular Science Matrix to Expand Arsenic Pollution Awareness

To address the current situation of "insufficient public awareness of arsenic pollution and limited knowledge of biosensors," we designed hierarchical popular science activities to enhance cognition from "unaware" to "informed," and from "understanding" to "action":

2.1 Online Popular Science Outreach

We operated the "HUBU SKY" official WeChat account, publishing content on "arsenic pollution hazards" and "biosensor principles." It has accumulated over 150 followers and 47,000+ total reads, with traffic covering channels such as "Search," "Moments," and "Recommendations." The content reached diverse groups including university students, community residents, and rural populations. For instance, the article Is the Soil Under Your Feet "Poisoned"? helped over 2,000 readers learn about the "risk of excessive arsenic in rice" for the first time.

2.2 Offline Interactive Outreach

• "Green Fun Stamp Collection" Stall Activity: Two stalls were set up—the "Arsenic Pollution Popular Science Area" (exhibition boards + manuals) and the "Technology Interaction Area" (a puzzle game using fluorescent cards to simulate hierarchical detection principles). By offering "environmental souvenirs in exchange for completed tasks," we lowered cognitive barriers, reaching over 300 community residents and collecting more than 20 optimization suggestions (e.g., "smaller sensor size," "simplified detection steps").
• Shennongjia Volunteer Teaching for Prevention: In Taoping Village, Shennongjia (a national nature reserve), we conducted "Arsenic Pollution and Little Earth Guardians" themed volunteer teaching. Through animated short films and "drawing arsenic pollution hazards" activities, over 50 children learned "how arsenic pollution affects ecosystems." Children were encouraged to pass on protective knowledge (e.g., "well water testing," "reducing pesticide use") to their families, creating a "child-family" popular science transmission chain and achieving the ecological protection goal of "preventing problems before they occur."

3. Forming a "Research-Education-Feedback" Closed Loop to Ensure Popular Science Accuracy and Sustainability

3.1 Precise Demand Localization

152 online and offline questionnaires were distributed (132 valid responses), identifying key public knowledge gaps: Over 60% of respondents were unaware of "arsenic pollution sources (mining, arsenic-containing pesticides)," and only 23% could recognize early arsenic poisoning symptoms such as "skin pigmentation." We adjusted popular science priorities accordingly, focusing on "arsenic exposure pathways" and "simple household protective methods."

3.2 Feedback-Driven Educational Optimization

After campus lectures, in response to students' feedback of "wanting more hands-on practice," we added a "local water source testing" module. For the official account, in response to readers' comments requesting "more real-world biosensor application cases," we published a feature article How Do Farmers in Lingmi Village Use Sensors to Test Paddy Fields? with over 5,000 reads—further strengthening the concrete understanding of the technology.

Our 2025 iGEM HUBU-Wuhan team made a contribution to the annotation of Part: BBa_J33201 (E. coli Chromosomal ars Promoter with arsR Repressor Gene) by identifying a relevant academic literature, interpreting it, and using its data to supplement the part’s functional description.