Engineering Process

Introduction Module 1: Construction and Optimization of Sensing Circuits Driven by Constitutive Promoters Module 2: Natural Promoter Engineering and Component Innovation Module 3: Self-Assembly System and System-Level Optimization Conclusion

Full-Process Engineering Development of Arsenic Biosensor Based on DBTL Closed-Loop

Introduction

This study adopts "Design-Build-Test-Learn (DBTL)" as the core engineering logic, attempting to gradually address key issues of arsenic biosensors (such as high basal leakage and low component interaction efficiency) through 3 modules and 7 iterative cycles. Meanwhile, innovative strategies including "niche matching" and "AND-gate-like logic circuit" are integrated to provide an engineering paradigm for the development of synthetic biology environmental sensing systems.

Module 1: Construction and Optimization of Sensing Circuits Driven by Constitutive Promoters (3 Cycles: C1-C3)

In this module, we focus on "construction of basic arsenic-responsive circuits - problem localization - interference elimination". Through three rounds of DBTL iterations, we identify the core cause of high basal expression, laying a foundation for subsequent optimization.

Cycle 1: First-Round DBTL Development of Basic Plasmids V0-V1-V2

Design: Initial Design Focused on "Component Function Verification"

Core objective: Construct the basic framework of "ArsR repressor protein - sfGFP reporter gene", and investigate the source of basal expression through differentiated promoter combinations.

Design logic: Introduce regulatory components in phases --

V0 (no specific promoter, only ArsR-sfGFP): Verify the "basal level without regulation"

V1 (PlacV promoter drives ArsR + ParsOC2 promoter regulates sfGFP): Establish the basic "repression-response" circuit

V2 (ParsOC2 promoter drives ArsR + ParsOC2 regulates sfGFP): Test the compatibility of "homologous promoter - repressor protein" and initially explore the impact of component matching on circuit performance

Build: Plasmid Assembly via Standardized Molecular Cloning

Operation procedure: Successively construct recombinant plasmids pET46a-ArsR-sfGFP-V0, pET-46a-PlacV-ArsR-ParsOC2-sfGFP-V1, and pET46a-ParsOC2-ArsR-sfGFP-V2. Digest the pET46a vector with restriction endonucleases, directionally insert the ArsR, sfGFP, and corresponding promoter fragments, ligate them, and transform into E. coli competent cells. After antibiotic resistance screening, colony PCR, and sequencing verification, confirm the successful construction of the three plasmids.

Test: Quantitative Detection of Circuit Response Performance by Microplate Reader

Detection protocol: According to common parameters in literature, set up multiple gradient NaAsO₂ treatment groups (0 ppb (0 μM) ... 300 ppb (300 μM)). Use a microplate reader to determine the sfGFP fluorescence intensity (Fluo) and bacterial OD600 value respectively, and calculate the "Fluo/OD600" ratio to correct the interference of bacterial density.

Key results: As shown in Figure a, under the condition of 300 ppb arsenic concentration, the fluorescence intensity of V1 increased by 1.7-fold compared with the control group at 0 ppb. However, the basal expression was relatively high, and the fluorescence response change was small after 6 hours of gradient induction. In the absence of arsenic, V2 showed high GFP basal expression; after arsenic treatment, the fluorescence induction error was large, the fold did not meet expectations, and the "signal-to-noise ratio" of the circuit was poor.

Learn: Localize the Core Issue of "Promoter Regulation Imbalance"

Conclusion deduction: Combined with literature reports, confirm that the "expression level of ArsR driven by constitutive promoters" is a key variable -- either excessively high or low expression of ArsR will weaken the repression effect on the ParsOC2 promoter, leading to downstream sfGFP leakage.

It is necessary to optimize the constitutive promoter to improve the binding efficiency between ArsR and ParsOC2, and reduce the basal level from the perspective of "component expression level".

Cycle 2: Second-Round DBTL Iteration of V3-V4 Driven by Constitutive Promoters

Design: Targeted Design Focused on "Promoter Optimization"

Core objective: Address the "promoter regulation imbalance" identified in the Learn phase of C1, and replace the promoter type to improve the expression compatibility of ArsR.

Design logic: Retain the core framework of "ArsR-ParsOC2-sfGFP" and only replace the promoter driving ArsR --

V3 (continue to use the PlacV promoter, optimize the ArsR coding sequence to improve protein stability)

V4 (adopt strong constitutive promoters PJ100, PJ101, PJ102, PJ104, and PJ111 to drive ArsR, enhancing the expression level of repressor protein)

By comparing V3 and V4, exclude the independent effects of "promoter strength" and "protein sequence"

Build: Precise Plasmid Assembly and Verification

Operation procedure: Construct recombinant plasmids pET46a-PlacV-ArsR-ParsOC2-sfGFP-V3 and pET-46a-PJ100-ArsR-ParsOC2-sfGFP-V4. Adopt the same molecular cloning process as C1, and confirm the successful construction of the two optimized plasmids through enzyme digestion, ligation, transformation, and sequencing verification.

Test: Continuous Performance Detection and Data Comparison

Detection protocol: Conduct multi-gradient NaAsO₂ treatment consistent with C1, determine the Fluo/OD600 value using a microplate reader, and focus on comparing the basal differences between V3, V4 and V1, V2 in C1.

Key results: As a sensing component of the whole-cell arsenic sensor, PJ100 enables arsenic detection with high sensitivity (×2.5), low background, and high signal-to-noise ratio.

Learn: Explore the New Issue of "Protein-Promoter Binding Efficiency"

Conclusion deduction: Through the prediction of the binding effect between promoter and repressor protein, combined with the consistent data from C1 and C2, infer that "high basal level" does not originate from the expression level of ArsR, but from the low binding efficiency between ArsR and ParsOC2.

It is necessary to verify whether "ArsR-ParsOC2 binding efficiency" is a key factor, and at the same time exclude the interference of induction conditions (concentration, time).

Cycle 3: DBTL Verification of Multi-Gradient Induction Experiments

Design: Control Design for Eliminating "Induction Condition Interference"

Core Objective: Verify whether "ArsR-ParsOC2 binding efficiency" is the primary cause of high basal levels, while ruling out environmental interferences such as inducer concentration and induction time.

Design Logic: Using DH5α strains transfected with V3 and V4 plasmids as research subjects, set up "multi-gradient induction conditions" -- NaAsO₂ concentration (0~300 μM), induction time (4h/8h/12h/24h), and initial induction OD value (0.4/0.6/0.8). Investigate the impact of non-core variables through comprehensive controls.

Build: Establishment of Multi-Gradient Experimental System

Operation Procedure: Prepare seed cultures of V3 and V4 strains, inoculate them at different initial OD values, add gradient concentrations of NaAsO₂ respectively, and sample at different time points to construct a "concentration-time-OD" three-dimensional control system.

Test: Systematic Investigation of Interfering Factors

Detection Protocol: Perform microplate reader detection consistent with C1 and C2, record Fluo/OD600 values under different induction conditions, and plot the "induction condition-fluorescence basal level" correlation curve.

Key Results: Regardless of adjustments to induction concentration or time, the arsenic-free basal levels of V3 and V4 did not decrease significantly, ruling out the interference of "unreasonable induction conditions".

Learn: Locking "Component Interaction Efficiency" and Introducing Computational Assistance

Conclusion Deduction: Combined with protein-DNA binding prediction results from AlphaFold3, confirm that "low binding efficiency between ArsR and ParsOC2" is the core cause -- amino acid conflicts at the binding interface weaken the repression effect, leading to downstream gene leakage.

It is necessary to move beyond the limitation of "promoter replacement" and shift to "promoter sequence screening", and construct a promoter library to explore variants with high binding efficiency.

Module 2: Natural Promoter Engineering and Component Innovation (2 Cycles: C4-C5)

Based on the Learn conclusions of Module 1, this module introduces the innovative "niche matching" strategy and develops novel promoter components with stronger compatibility through DBTL cycles of "natural promoter testing -- library construction".

Cycle 4: DBTL Verification of CML2 Natural Promoter

Design: Innovative Design Practicing "Niche Matching"

Core Objective: Break the inertia of "universal components for model strains" and directly isolate the natural promoter ParsCML2 from the native host (rice endophyte CML2) in the target habitat (paddy field). ParsCML2 co-evolves with homologous ArsR, resulting in lower binding free energy, and can maintain high repression and low leakage in the paddy microenvironment (pH 6.2--6.8, microaerobic, rich in organic matter). Retain the "main switch" architecture where PJ100 drives arsR, and only replace the downstream ParsOC2 with ParsCML2 to obtain the plasmid pET-46a-PJ100-ArsR-ParsCML2-sfGFP-V4, enabling the "homologous promoter-repressor protein" pairing test.

Build: Cloning and Assembly of Novel Components

Operation Procedure: Amplify the ParsCML2 promoter fragment from the CML2 strain by PCR, insert it into the pET-46a-PJ100-ArsR-sfGFP vector to replace the original ParsOC2 sequence; confirm the successful construction of the recombinant plasmid pET-46a-PJ100-ArsR-ParsCML2-sfGFP-V4 through sequencing verification.

Test: Performance Evaluation of Natural Promoter

Detection Protocol: Conduct multi-gradient NaAsO₂ treatment consistent with Module 1, determine the Fluo/OD600 value using a microplate reader, and compare the basal level control effects between ParsCML2 and ParsOC2.

Key Results: Compared with the ParsOC2 system, ParsCML2 exhibits stronger binding ability with ArsR derived from the rice endophyte CML2, thereby significantly reducing the basal leakage expression of GFP to 8%. Under induction conditions in the range of 50--300 μM, the fluorescence intensity of ParsCML2 is 4785--6000 a.u., which is 2.3--3.1 times that of ParsOC2, achieving higher signal-to-noise ratio and detection sensitivity.

Learn: Clarifying the Necessity of "Library Screening"

Conclusion Deduction: Although the CML2 natural promoter has compatibility advantages, a single variant cannot fully solve the binding efficiency issue; it is necessary to obtain better promoter variants through "random mutation + high-throughput screening".

Construct a ParsCML2 promoter mutant library, and screen components with high repression efficiency through DBTL cycles.

Cycle 5: DBTL Construction and Planning of Promoter Library (In Progress)

Design: Library Design Targeting "High-Throughput Screening"

Core Objective: Construct a ParsCML2 promoter mutant library to provide a resource pool for screening promoter variants with "high binding efficiency and low leakage".

Design Logic: Adopt error-prone PCR technology to introduce random mutations into ParsCML2 (including base substitutions, insertions, or deletions), and construct a mutant library covering the core regions of the promoter (-35 region, -10 region); the library size is set to more than 1000 clones to ensure sufficient sequence diversity.

Module 3: Self-Assembly System and System-Level Optimization (2 Cycles: C6-C7)

Based on the Learn conclusions of the previous two modules, this module integrates the innovative strategy of "AND-gate-like circuit" and attempts to solve the basal leakage problem from the "system level" through two rounds of DBTL iteration, while developing new split-GFP fusion components.

Cycle 6: First-Round DBTL of the "AND-Gate-Like" Self-Assembly System

Design: System Design Innovating "AND-Gate-Like Logic"

Core Objective: To address the "intrinsic leakage" of repressive circuits, introduce a split-GFP self-assembly system and construct an "AND-gate-like" circuit, shifting from "passive repression" to "actively neutralizing leakage".

Design Logic: Define "leakage efficiency = leakage brightness / total brightness" and propose a "dual-signal-dependent" mechanism -- strong fluorescence is only produced when "arsenic relieves ArsR repression (Signal 1)" and "GFP fragment self-assembly (Signal 2)" occur simultaneously; in the absence of arsenic, even if ArsR repression is incomplete (leakage), a single GFP fragment has no fluorescent activity, thereby blocking the basal level.

Specific Design: Construct split-GFP (GFP1-10, GFP11) downstream of the ParsOC2 promoter respectively, and initiate induction when the system reaches the logarithmic growth phase to ensure synchronous expression of the fragments.

Build: System Establishment of the Self-Assembly Circuit

Operation Procedure: Insert GFP1-10 and GFP11 downstream of ParsOC2 in the pET46a-PlacV-ArsR-ParsOC2 vector respectively via molecular cloning to construct the "ArsR-ParsOC2-GFP fragment" self-assembly circuit; confirm the successful construction of the circuit through sequencing verification.

Test: Functional Verification of the Self-Assembly System

Detection Protocol: After culturing the system to OD600 = 0.6, add gradient concentrations of NaAsO₂ for induction. After 3 hours, determine the fluorescence intensity using a microplate reader and plot the "arsenic concentration-fluorescence" curve.

Key Results: The improved split-GFP variant system (Variant) has a leakage efficiency of less than 1%, with almost no spontaneous basal fluorescence. When detecting low-concentration arsenic, it can more accurately distinguish between "basal level" and "arsenic-induced signal", improving the detection specificity. The disadvantage is that as the arsenic concentration increases, the fluorescence intensity is weak, and the fluorescence response characteristics need to be optimized.

Learn: Investigating Dual Possibilities of "Fragment Expression/Assembly"

Conclusion Deduction:

GFP fragments are normally expressed but self-assembly is poor (complementary interface blocked)
fragments are not effectively expressed (or degraded after expression)

It is necessary to verify the function of the fragments through "in vitro expression and purification" to rule out expression/assembly issues.

Cycle 7: DBTL for GFP Fragment Stability Optimization (In Progress)

Design: Plasmid Structure Optimization Targeting "Anti-Degradation"

Core Objective: To address the "fragment degradation" identified in the Learn phase of C7, improve the stability of GFP fragments through "tag addition + linker connection" while retaining the self-assembly function.

Design Logic: Adopt two major optimization strategies --

Add a SUMO tag to the N-terminus of GFP11 (capping protection to reduce degradation)

Connect GFP1-10 and GFP11 via a flexible linker (Gly4Ser3) to construct a "GFP1-10-linker-GFP11" fusion structure (increase molecular weight to > 30 kDa to avoid column penetration; meanwhile, the linker does not affect fragment complementation)

Summary: Project Innovation and Value Driven by the DBTL Closed Loop

Through modular design, we conducted the engineering development of arsenic biosensors from "basic construction" to "system optimization" in three separate modules and a total of seven DBTL (Design-Build-Test-Learn) cycles, while demonstrating three core values:

Methodological Innovation

Explore environment-adaptable components through "niche matching"
Solve intrinsic leakage with "AND-gate-like circuits"
Achieve precise iteration via "multiple DBTL closed loops"
Form a complete methodology from design concepts to optimization strategies

Component Contribution

Develop two novel components -- CML2-derived ArsR-promoter (high environmental adaptability) and GFP1-10-linker-GFP11 fusion protein (high stability), enriching the synthetic biology toolbox

Engineering Paradigm

Each cycle takes "problem localization -- design optimization -- verification and learning" as its core, solving problems such as high basal levels, component interaction, and fragment degradation step by step, and providing a reusable DBTL path for the engineering development of environmental sensing systems