This project successfully constructed and validated an engineered bacterial system for the detection and control of bacterial wilt. The biosensor, based on an AHL-sensing component, can specifically recognize signal molecules released by the Ralstonia solanacearum, enabling early pathogen detection. The visualization module successfully converted the signal into a visible blue colorimetric reaction, confirming its feasibility for rapid on-site detection. The therapeutic module's engineered bacteria can effectively synthesize salicylic acid, enhancing disease resistance by activating plant immune responses. The integrated system achieves the goal from pathogen detection to immune intervention, providing a new technical approach to address the challenge of bacterial wilt control.
To construct an engineered E. coli strain capable of specifically detecting AHL quorum-sensing signal molecules. This strain constitutively expresses the AHL receptor protein QscR. Upon QscR binding to AHL, it activates the expression of the downstream reporter gene mRFP, allowing quantitative detection of AHL presence and concentration via fluorescence intensity.
Figure 1 AHL Sensor Genetic Circuit
The codon-optimized QscR gene (from Pseudomonas aeruginosa PAO1) and its promoter PQscR were first cloned into the pSB1A3 vector [1]. Specifically, the J23100-B0034-QscR-B0015-PQscR-mRFP expression cassette was inserted into the vector using XbaI and SpeI restriction enzymes, constructing a recombinant plasmid compliant with BioBricks™ standard RFC#10 [2][3]. The recombinant plasmid was transformed into E. coli BL21(DE3) super competent cells via heat shock (42°C, 1 min) and plated on LB agar plates containing 50 µg/mL ampicillin, followed by incubation at 37°C for 16 h. Single colonies were picked and inoculated into 5 mL of LB/Amp⁺ liquid medium, shaken at 37°C, 180 rpm until OD600 reached 0.6-0.8. Positive clones were verified by colony PCR (using 2×Taq Master Mix) and sent for sequencing validation [4]. The sequence-verified engineered strain was inoculated into 5 mL of LB/Amp⁺ medium, cultured at 37°C, 180 rpm to OD600 ≈ 0.6. Then, 1 mL of the culture was mixed with 1 mL of 50% glycerol and stored at -20°C for backup.
Figure 2 AHL Sensor Recombinant Plasmid Map
As shown in the Figure 3, clear bands were observed at the expected sizes in the PCR amplification products. The QscR gene amplification band was located at 711 bp, and the mRFP gene amplification band was at 678 bp, completely consistent with the theoretical design sizes, with no non-specific amplification bands present.
Figure 3 Agarose gel electrophoresis of QscR and mRFP. (A) Agarose gel electrophoresis of QscR. (B) Agarose gel electrophoresis of mRFP.
These results successfully verified the correctness of the codon-optimized QscR gene and the mRFP fragment, indicating accurate gene synthesis and cloning processes. This provides complete core functional components for the subsequent construction of the AHL biosensor engineered bacteria.
To validate the specific recognition capability of the constructed biosensor engineered bacteria for characteristic AHL molecules of R. solanacearum (3OC12-HSL, C10-HSL, 3OHC10-HSL), confirming its ability to specifically respond to key signal molecules associated with bacterial wilt, laying the foundation for subsequent applications.
The sequence-verified engineered strain was inoculated at a 1:100 ratio into 5 mL of LB medium containing 50 µg/mL ampicillin and activated overnight at 37°C, 150 rpm. Then, 100 µL of the culture was re-inoculated into a 50 mL sterile centrifuge tube containing 5 mL of Amp⁺ LB medium and cultured at 37°C until OD600 reached 0.3. Then, 1 mL of the culture was transferred to a 24-well plate, and 10 µM of 3OC12-HSL, C10-HSL, or 3OHC10-HSL was added respectively. After 3 hours of induction at 37°C, the OD600 and mRFP fluorescence intensity at 584/607 nm were measured simultaneously using a microplate reader. After subtracting the LB medium background, the normalized fluorescence value (fluorescence intensity/OD600) for each sample was calculated.
Figure 4 Experimental procedure for testing AHL sensor response to three AHL molecules
As shown in the figure 5, after induction with 10 µM different AHL molecules, the engineered strain showed significantly different fluorescence responses. The 3OC12-HSL induced group had the highest normalized fluorescence intensity (11.40 ± 0.52 RFU/OD), followed by the C10-HSL group (7.64 ± 0.31 RFU/OD), and the 3OHC10-HSL group had the weakest response (5.33 ± 0.16 RFU/OD). Statistical analysis indicated extremely significant differences among the three groups (P < 0.001), demonstrating the sensor's ability to distinguish between different types of AHL molecules.
Figure 5 Relative mRFP fluorescence intensity after 3h induction with different AHLs
The successfully constructed AHL biosensor possesses specific recognition capability for all three characteristic AHL molecules of R. solanacearum, with the strongest response to 3OC12-HSL. This sensor can effectively distinguish AHL molecules with different structures, laying the foundation for the specific detection of R. solanacearum.
To determine the detection sensitivity of the constructed AHL biosensor for characteristic AHL molecules of R. solanacearum, determine its minimum effective detection threshold, and verify whether the system can be effectively activated within the expected field AHL concentration range produced during R. solanacearum infection (literature reports are typically at the nanomolar level [5]), thereby evaluating its practical feasibility for field early warning.
Concentration gradient experiments were performed using three AHL molecules: 3OC12-HSL, C10-HSL, and 3OHC10-HSL (0.001, 0.01, 0.1, 1, 10 µM). The strain culture and induction conditions were the same as before. After induction, fluorescence signals were detected using a microplate reader, and normalized fluorescence values were calculated.
The concentration gradient experiment results (Figure 6) showed that all three AHL molecules exhibited typical dose-dependent response trends:
Figure 6 Analysis of biosensor strain sensitivity and dose-response relationship
(A) Relative mRFP fluorescence intensity after 3h induction with different concentrations of 3OC12-HSL. (B) Relative mRFP fluorescence intensity after 3h induction with different concentrations of C10-HSL. (C) Relative mRFP fluorescence intensity after 3h induction with different concentrations of 3OHC10-HSL.
The constructed AHL biosensor has high detection sensitivity, with a detection limit for 3OC12-HSL reaching 0.01 µM. The sensor shows a good linear response characteristic in the 0.01-1 µM, meeting the quantitative detection requirements for R. solanacearum quorum sensing signal molecules.
Based on the AHL biosensor, replace the fluorescent reporter gene with the indigo synthesis pathway (TnaA and FMO) to construct a colorimetric sensor that produces a visible blue precipitate (indigo) for rapid on-site detection of AHL.
Figure 7 Indigo colorimetric sensor genetic circuit
First, the mRFP reporter gene in the AHL biosensor was replaced with TnaA and FMO. The J23100-B0034-QscR-B0015-PQscR-TnaA-B0034-FMO sequence was cloned into the pSB1A3 vector using XbaI and SpeI [6][7]. Subsequently, the recombinant plasmid was transformed into E. coli BL21(DE3) competent cells via heat shock (42°C, 1 min) and plated on LB agar plates containing 50 µg/mL ampicillin (Amp⁺) (pH 7.0), followed by incubation at 37°C for 16 h. Single colonies were picked and inoculated into 5 mL of LB/Amp⁺ liquid medium, shaken at 37°C, 180 rpm until OD600 reached 0.6-0.8. Positive clones were verified by colony PCR (2×Taq Master Mix, Vazyme). The sequence-verified engineered strain was inoculated into 5 mL of LB/Amp⁺ medium and cultured at 37°C, 180 rpm. The growth curve was monitored using a FlexStation 3 (Molecular Devices, USA) until the mid-log phase (OD600 ≈ 0.6). Then, 1 mL of the culture was mixed with 1 mL of 50% glycerol and stored at -20°C for backup.
Figure 8 Indigo colorimetric biosensor plasmid map
Agarose gel electrophoresis results showed clear bands for the PCR amplification products of the TnaA and FMO genes, with sizes at 1422 bp and 1368 bp respectively, completely consistent with the expected fragment sizes.
Figure 9 Construction of AHL-inducible indigo biosynthesis engineered bacteria. (A) Agarose gel electrophoresis of TnaA. (B)Agarose gel electrophoresis of FMO.
The AHL-inducible indigo biosynthesis engineered bacteria were successfully constructed. Gene cloning and sequencing results indicated that the indigo synthesis pathway genes TnaA and FMO were accurately integrated into the expression vector, providing reliable engineered bacterial material for subsequent colorimetric analysis.
To evaluate the response characteristics of the indigo biosensor to characteristic AHL molecules of R. solanacearum, determine the minimum detection time required for the sensor to produce visible blue color by measuring indigo yield changes at different time points, and analyze its detection sensitivity and dynamic response range, providing an experimental basis for determining the optimal observation time window in field applications.
The verified engineered strain was inoculated into 5 mL of LB broth containing 50 µg/mL ampicillin and activated overnight in a shaking incubator at 37°C and 180 rpm. The engineered bacteria were then inoculated at a 1:100 ratio into M9 medium containing 0.4% glucose, and 1 µM of 3OC12-HSL, C10-HSL, or 3OHC10-HSL was added to induce indigo synthesis [8]. Then, 1 mL of the culture was collected every 2 hours, centrifuged at 10,000 × g for 1 minute, and the supernatant was collected. The absorbance at 620 nm was measured using a microplate reader. A 10 mM indigo solution was prepared using DMSO. A standard curve was prepared using different concentrations of indigo (0 mM, 0.25 mM, 0.5 mM, 1 mM, 1.5 mM, 3 mM). Each experimental group was performed in triplicate. indigo yield was quantitatively analyzed by comparing it with the indigo standard curve.
Figure 10 Operation procedure for colorimetric analysis of the indigo biosensor
Time-course monitoring results (Figure 11) showed that all three AHL molecules could induce indigo synthesis, exhibiting a similar time-dependent accumulation pattern:
Comparison of induction efficiency showed that 3OC12-HSL had the strongest induction capability (yield at 8 hours: 2.14 ± 0.14 mM), significantly higher than C10-HSL (1.70 ± 0.06 mM) and 3OHC10-HSL (1.42 ± 0.09 mM) (P < 0.01). Visible blue precipitate formation was observed with the naked eye, and the color intensity positively correlated with the AHL concentration.
Figure 11 Colorimetric analysis of the indigo biosensor.
(A) indigo standard curve. (B) Physical images of indigo production induced by different AHLs. (C) indigo content after treatment with 1 µM 3OC12-HSL for different times. (D) indigo content after treatment with 1 µM C10-HSL for different times. (E) indigo content after treatment with 1 µM 3OHC10-HSL for different times.
The constructed indigo colorimetric biosensor exhibited good time-dependent response characteristics to all three AHL molecules, with the highest sensitivity to 3OC12-HSL. Experimental results indicate that the minimum effective detection time for the sensor should not be less than 6 hours: rapid color development begins 4 hours after induction, and the ideal detection effect is achieved within 6-8 hours, forming a clearly distinguishable blue precipitate visible to the naked eye. Therefore, it is recommended to set the detection time within the 6-8 hour range in practical applications. This ensures sufficient completion of the colorimetric reaction while meeting the timeliness requirements of rapid on-site detection, providing a clear basis for judgment and a reliable time window for field diagnosis.
To construct an engineered bacterial strain capable of efficiently synthesizing salicylic acid (SA). Salicylic acid is a key hormone in plant disease resistance signaling pathways. This engineered bacteria can be used to study the feasibility of enhancing plant resistance to bacterial wilt through the application of exogenous SA elicitors.
Figure 12 Salicylic acid synthesis genetic circuit
First, the codon-optimized salicylic acid synthesis genes ICS and IPL were cloned into the pET28a vector [9][10] and transformed into E. coli BL21(DE3). Positive clones were screened on LB plates containing kanamycin and verified for correctness by colony PCR and sequencing. The verified engineered strain was inoculated into M9Y medium. When the OD600 reached 0.6, 0.5 mM IPTG was added to induce protein expression. Cultivation continued, and 1 mL of the culture was collected every 4 hours. After centrifugation at 10,000 × g for 1 minute, the supernatant was collected. The salicylic acid concentration in the supernatant was quantitatively analyzed using a salicylic acid ELISA detection kit, and the normalized yield was calculated [11].
Figure 13 Salicylic acid synthesis plasmid map
As shown in Figures 14A and 14B, this study established a salicylic acid standard curve using the ELISA method. Figure 14A illustrates the direct relationship between salicylic acid concentration and A450 absorbance, while Figure 14B displays the linear relationship between the logarithm (Log10) of salicylic acid concentration and A450 absorbance, verifying the reliability of the quantitative detection.
Regarding salicylic acid production, the time-course curve (Figure 14C) demonstrates that after induction with 0.5 mM IPTG, the engineered strain exhibited a time-dependent accumulation of salicylic acid over 24 hours. Specifically, the yield increased continuously from 5.61 ± 1.55 mg/L at 4 hours to 84.02 ± 4.66 mg/L at 24 hours.
The between-group comparison conducted after 24 hours of induction (Figure 14D) further revealed that the salicylic acid yield of the engineered strain group (BL21-ICS-IPL) exceeded 80 mg/L, while the yield from the blank control group (BL21 empty strain) was nearly negligible. The difference between the two groups was highly significant (P < 0.0001), conclusively demonstrating that the constructed engineered strain possesses the capability for efficient salicylic acid synthesis.
Figure 14 Salicylic acid production test.
(A) Salicylic acid ELISA standard curve (A450). (B) Log10 values of salicylic acid concentration and A450 absorbance from ELISA analysis. (C) Salicylic acid production over time. (D) Salicylic acid content after 24h induction in different engineered strains.
The experiment successfully constructed the engineered strain BL21-ICS-IPL capable of efficiently synthesizing salicylic acid. The combination of heterologously expressed ICS and IPL genes endowed the engineered bacteria with a strong salicylic acid synthesis capacity, and the yield was significantly higher than that of the original strain. This proves the effectiveness of this genetic design strategy and provides an efficient biosynthesis platform for the subsequent development of salicylic acid-based plant immune activators.
The experimental results of this project systematically validate the feasibility of a synthetic biology-based strategy for the detection and control of bacterial wilt. Through the constructed AHL biosensor, we have not only achieved nM-level high-sensitivity detection of the characteristic signal molecules of Ralstonia solanacearum but also successfully transformed the detection signal into a visible blue colorimetric reaction, establishing a 6-8 hour optimal detection window. Crucially, all experimental data were validated within the natural secretion range of AHL by R. solanacearum, which fully demonstrates that the system possesses the sensitivity foundation for field application.
Particularly noteworthy is that our modular design enabled the transition from laboratory instrument-based detection to field visual interpretation. Research confirmed that replacing the fluorescent reporter system with the indigoidine synthesis pathway not only maintained detection sensitivity but also significantly enhanced the practicality and promotion potential of the technology. This design approach effectively addresses the key limitations of traditional molecular detection technologies, namely their strong dependence on equipment and operational complexity.
We believe that this technology not only represents a novel approach to disease detection but also heralds the future direction of precision agriculture and green plant protection.
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