Engineering Process

Overview Chassis Strains Strategy 1: Construction of Arsenic Sensor Strategy 2: GFP Self-Assembly System Strategy 3: Functional Verification Strategy 4: Performance Optimization

Overview

Arsenic contamination poses a threat to food safety and public health through the rice food chain. Current monitoring methods relying on large-scale instruments are costly and complex to operate, making it difficult to achieve rapid on-site screening in farmlands. Meanwhile, existing biosensors suffer from limitations such as insufficient sensitivity and specificity, high background noise, lack of ecological relevance, and single functionality, which prevent them from meeting practical application needs.

To tackle these issues, this project puts forward a comprehensive and systematic solution that combines ecological insights with engineering innovations. Methodologically, it breaks away from traditional approaches in three aspects:

First, it proposes the "ecological niche matching" design principle. By excavating functional elements from CML2, a native endophytic bacterium of rice in paddy fields, it enhances the field application potential and reliability of the sensor.

Second, it innovatively constructs a "class - and - gate" circuit. By integrating the split - GFP self - assembly system into the sensing loop, it reduces background noise and improves the signal - to - noise ratio at the system level.

Third, it implements a multi - round closed - loop DBTL (Design - Build - Test - Learn) engineering optimization process to quickly identify and solve problems.

Chassis Strains

DH5α was selected as the chassis strain for the experiments. DH5α is a commonly used Escherichia coli strain in molecular biology laboratories, renowned for its high transformation efficiency. This characteristic is crucial for the successful co - transformation of recombinant plasmids, such as pET28a - splitGFP₁₋₁₀, pET28a - splitGFP₁₁, and the recombinant plasmids from the sensor basic construction, during the establishment of the GFP self - assembly system and sensor - related vector construction.

This strain has a stable genetic background and lacks certain restriction-modification systems, avoiding the degradation of exogenous recombinant plasmids and ensuring the stable maintenance and replication of the constructed gene circuits (including the "constitutive expression-inducible response" transcription regulatory module in the sensor basic part and the split-GFP-related expression elements).

In terms of culture and induction conditions, DH5α is compatible with the experimental procedures described: it can grow well in LB medium (both solid and liquid) containing corresponding antibiotics (carbenicillin, kanamycin) at 37°C, and after reaching the target OD₆₀₀ value (0.6), it can efficiently induce the expression of target genes (such as split-GFP fragments, ArsR protein, and sfGFP) under the induction of IPTG at appropriate temperatures (16°C for induction expression in the self-assembly system, 37°C for culture in dose-response curve determination), meeting the functional verification and optimization needs of the sensor and GFP self-assembly system.

Strategy 1: Construction of Arsenic Sensor Based on Functional Elements from Endophytic Bacterium CML2

The core of this strategy is to excavate the ArsR protein-encoding gene from the rice endophytic bacterium CML2 (with ecological niche matching advantages) and construct a basic transcription regulatory module of the arsenic sensor, laying the foundation for subsequent signal recognition and response.

Module 1: Acquisition of Target Gene (arsR-sfGFP Fusion Gene)

To obtain the functional gene fragment required for the sensor, we adopted polymerase chain reaction (PCR) and DNA recombination technology:

1. Amplification of arsR gene: Using the genomic DNA of rice endophytic bacterium CML2 as the template, PCR was performed to specifically amplify the gene encoding the ArsR protein (a key regulatory protein that can specifically bind to arsenic ions and mediate the regulation of downstream promoters).

2. Construction of recombinant plasmid: Through DNA recombination technology, the amplified arsR gene was fused with the sfGFP (superfolder green fluorescent protein) gene, and the fusion gene fragment was inserted into the pET-46a expression vector. After ligation and transformation, the recombinant plasmid pET-46a-sfGFP-arsR was finally obtained, which realizes the linkage of ArsR protein expression and sfGFP fluorescent signal.

Module 2: Construction of Basic Module Vector (Constitutive Expression-Inducible Response Gene Circuit)

Based on the construction principle of whole-cell microbial sensors mediated by repressive transcription factors, we constructed a core gene circuit on the backbone of the pET-46a vector, named pET46a-P constitutive - ArsR-P inducible - sfGFP. The circuit consists of two functionally complementary modules:

1. arsR gene expression module driven by constitutive promoter (P constitutive): The constitutive promoter continuously drives the transcription and translation of the arsR gene, ensuring that a stable concentration of ArsR protein is maintained in the host cell under non-inducing conditions.

2. sfGFP reporter gene expression module regulated by inducible promoter (P inducible): The inducible promoter is specifically inhibited by the ArsR protein (ArsR binds to P inducible to block sfGFP transcription) under non-inducing conditions; when arsenic ions (target signal molecules) exist, arsenic ions bind to ArsR protein and change its conformation, making it dissociate from P inducible, thereby activating the expression of sfGFP.

The two modules work together to form a "constitutive expression of regulatory protein - inducible activation of reporter gene" transcription regulatory total module, which realizes the specific recognition of arsenic ions and the output of fluorescent signals.

Strategy 2: Construction and Functional Verification of GFP Self-Assembly System

This strategy aims to construct a split-GFP (split-green fluorescent protein) self-assembly system, which provides a reliable fluorescent signal generation mechanism for the sensor and lays the foundation for subsequent functional verification of the system.

Module 1: Cloning of split-GFP Fragments

To obtain the split fluorescent protein gene fragments that can self-assemble to produce fluorescence, we performed gene cloning and plasmid construction:

1. Amplification of split-GFP fragments: Two gene fragments, split-GFP₁₋₁₀ (the first 1-10 β-sheets of GFP) and split-GFP₁₁ (the 11th β-sheet of GFP), were amplified by PCR respectively.

2. Construction of recombinant expression plasmids: The amplified split-GFP₁₋₁₀ and split-GFP₁₁ fragments were cloned into the pET-28a vector (located downstream of the T7 promoter, ensuring efficient transcription driven by the T7 promoter) respectively, to construct two recombinant plasmids: pET28a-splitGFP₁₋₁₀ and pET28a-splitGFP₁₁. Both plasmids carry kanamycin resistance gene (Kanᵣ) for screening.

3. Verification of plasmid correctness: The constructed recombinant plasmids were sent for sequencing, and the consistency between the sequencing results and the target gene sequence was compared to confirm the correctness of the plasmid sequence (avoiding gene mutation or wrong ligation affecting subsequent protein expression).

Module 2: Establishment of split-GFP Co-Expression System

Using DH5α competent cells as the host, a co-expression system was established to verify the self-assembly function of split-GFP:

1. Plasmid co-transformation: The two correctly sequenced recombinant plasmids (pET28a-splitGFP₁₋₁₀ and pET28a-splitGFP₁₁) were co-transformed into commercially purchased DH5α competent cells (with high transformation efficiency and stable genetic background).

2. Screening and culture of positive clones: The co-transformed bacterial solution was spread on LB solid medium containing carbenicillin (for screening positive clones carrying recombinant plasmids), and cultured at 37°C for 12-16 hours.

3. Induced expression and fluorescence detection: Single positive colonies were picked and inoculated into LB liquid medium containing carbenicillin, and cultured with shaking at 37°C until the bacterial solution OD₆₀₀ reached 0.6 (exponential growth phase). Isopropyl-β-D-thiogalactoside (IPTG) was added to a final concentration of 0.5mM, and induced expression was performed at 16°C for 16 hours. After induction, samples were taken to detect fluorescence intensity, and the self-assembly function of split-GFP (split-GFP₁₋₁₀ and split-GFP₁₁ assemble to form active GFP and emit fluorescence) was verified.

Strategy 3: Functional Verification of Arsenic Sensor System

This strategy focuses on evaluating the core performance of the constructed arsenic sensor system, including the dose-response relationship to arsenic ions (sensitivity) and the recognition specificity to target signals, ensuring that the system meets the requirements of arsenic detection.

Module 1: Determination of Dose-Response Curve (Evaluation of Sensitivity)

To clarify the response law of the sensor to different concentrations of arsenic ions, the dose-response curve was determined through gradient concentration treatment and fluorescence detection:

1. Bacterial culture and grouping: The co-expression strain (carrying both the arsenic sensor gene circuit and the split-GFP co-expression system) was cultured with shaking at 37°C until OD₆₀₀ =0.6, and then divided into multiple experimental groups (each group with 3 biological replicates).

2. Gradient induction treatment: Different concentrations of arsenic ions (0-1μM, covering the target detection range) were added to each experimental group, and the groups were cultured at 37°C for different time periods (2h, 4h, 6h, 8h) to ensure sufficient response of the sensor to arsenic ions.

3. Sample processing and fluorescence detection: After induction, bacterial cells were collected by centrifugation, washed twice with phosphate-buffered saline (PBS) to remove residual medium and arsenic ions, and then resuspended in PBS to adjust OD₆₀₀ =1.0 (normalizing the number of bacteria). Using a fluorescence spectrophotometer, the fluorescence intensity was detected under the conditions of excitation wavelength 488nm and emission wavelength 510nm.

4. Curve drawing: Taking the arsenic ion concentration as the abscissa and the ratio of fluorescence intensity to OD₆₀₀ (normalizing the fluorescence signal to the number of bacteria) as the ordinate, the dose-response curve of the sensor was drawn to analyze the sensitivity and response range of the sensor.

Strategy 4: Optimization of Arsenic Sensor System Performance

Aiming at improving the signal output efficiency and detection performance of the sensor, this strategy optimizes the key factors affecting the system function from two aspects: promoter intensity and induction conditions.

Module 1: Adjustment of Constitutive Promoter Intensity

The intensity of the constitutive promoter (P constitutive) directly affects the expression level of ArsR protein, and further affects the inhibition of the inducible promoter and the sensitivity of the sensor. Therefore, we replaced the original constitutive promoter with promoters of different intensities (such as J100, J111, etc., which have different transcription initiation efficiencies):

1. Promoter replacement: By DNA recombination technology, promoters J100, J101, J102, J104 J111 with known intensities were inserted into the arsenic sensor gene circuit to replace the original P constitutive, and a series of recombinant plasmids with different promoter intensities were constructed.

2. Performance detection: The recombinant plasmids were transformed into DH5α competent cells, and the dose-response curves of each group were determined according to the method in Strategy 3. The effect of different promoter intensities on the sensor's fluorescence signal output and sensitivity was compared, and the optimal constitutive promoter was screened.

Module 2: Optimization of Induction Conditions

The induction time and the bacterial concentration at induction directly affect the expression amount of the reporter gene (sfGFP) and the fluorescence signal intensity. Therefore, we optimized these two parameters:

1. Optimization of induction time: The co-expression strain was cultured to OD₆₀₀=0.6, induced with NaAsO₂, and samples were taken at different induction times to detect fluorescence intensity. The induction time with the highest fluorescence signal output was determined.

2. Optimization of bacterial concentration at induction: The co-expression strain was cultured to different OD₆₀₀ values, and induced with NaAsO₂ for the optimal induction time (screened in the previous step). After induction, fluorescence intensity was detected, and the optimal bacterial concentration at induction (which can maximize the fluorescence signal output) was determined.