Mutation libraries were established using two strategies: error-prone PCR (epPCR) and site-directed mutagenesis. Two rounds of epPCR targeting the fdeR gene with the StarMut Random Mutagenesis Kit, constructing a mutation library of 5.9×106mutants. Optimization of the PCR ion environment via gradient experiments resulted in a stable epPCR system with an appropriate mutation rate, ranging from 5 ~ 22 mutation bases per kilobase (kb) (Table S5). Meanwhile, site-directed mutation was performed on the fdeR gene through seamless cloning, creating five combinations of mutation sites. Based on the known amino acid sequence of FdeR,1 the three-dimensional (3D) structure of FdeR was predicted using AlphaFold22 (Figure S3, S4A, and B). AutoDock 4.2.63 was employed to explore potential liquiritigenin binding sites on FdeR (Figure S4C, D, and E). Following energy minimization principle, the optimal docking result was selected from 50 docking results, demonstrating hydrogen bond interactions between FdeR's Arg220, Thr244, and Leu268 and liquiritigenin (Figure 1A and B). Besides, the docking analysis of FdeR and promoter indicated liquiritigenin is unlikely to cause steric hindrance with DNA (Figure S5). The binding energy of this conformation was within an reasonable range, justifying its selection for site-directed mutation. Subsequently, a full mutation scanning on amino acids within 3 Å of liquiritigenin identified mutations L268W, H170Y, and H170W (Table 1).4 Containing five mutation combinations: L268W, H170Y, H170W, L268W + H170Y and L268W + H170W, the site-directed mutation library was transformed into E. coli BL21 for subsequent fluorescence screening experiments.
Index | Mutation | Mutation Energy (kcal/mol) | Effect |
---|---|---|---|
1 | A:HIS170>TRP | -0.83 | STABILIZING |
2 | A:LEU268>TYR | -0.73 | STABILIZING |
3 | A:HIS170>TYR | -0.57 | STABILIZING |
With a sorting rate of up to 70,000 events per second, FACS technology was applied to screening for mutants that specifically and efficiently identify liquiritigenin to while having no or only low responsiveness to naringenin (Figure 2 and Figure S6).
In the first round of epPCR, six parallel epPCR systems were established for positive screening. Supplemented with 0.5 mM liquiritigenin and naringenin separately, positive selections and negative selections were performed on the library (Figure 3A and B). Subsequently, 320 individual clones were used for 96-well microplate screening.
This yielded a mutant library with a 2-10-fold increase in liquiritigenin response intensity compared to the wild type (Figure 4A). Among them, Mut_49 stood out, displaying a remarkable 10-fold increase in peak fluorescence compared to the wild type. To further enhance the sensitivity and specificity of this biosensor in responding to liquiritigenin, we subjected four variants, namely Mut_39, Mut_49, Mut_58, and Mut_81, with favorable fluorescence characteristics to the next round of epPCR (Figure 3C and D). Finally, Mut_49_70 with a fluorescence intensity nearly 100 times higher than the wild type was isolated (Figure 4B), indicating a significant enhancement in this biosensor in responding to liquiritigenin.
In site-directed mutation experiments, we performed a 96-deep well microplate screening with 1.0 mM liquiritigenin or 1.0 mM naringenin. Regrettably, the mutants exhibit no significant increase in response intensity to liquiritigenin or naringenin compared to the wild-type FdeR (Figure 5). Consequently, a dose-response analysis for these mutants was not conducted, necessitating a reevaluation of our bioinformatics-based predictions.
R. Wassem et al.5 demonstrated that FdeR recognizes and binds to the PfdeA promoter, inhibiting RNA polymerase access. Upon binding flavonoids, like naringenin, FdeR undergoes conformational changes altering its DNA-binding site, enabling RNA polymerase binding and transcription initiation. Initial molecular docking focused on the individual FdeR monomer interaction with liquiritigenin, neglecting dynamic changes in FdeR binding to the PfdeA promoter and its oligomeric state in vivo. Future research will readdress this aspect through molecular docking to identify novel mutation sites.5
Based on the 96-well microplate fluorescence screening, eight mutants with high fluorescence intensity responses to liquiritigenin were selected for gradient experiments, varying liquiritigenin concentration and induction time, to characterize their performance as biosensor, including sensitivity, detection range, response time, and specificity, modeled using Hill functions to analyze dose impact.5 We defined operational range as the ligand concentration range and response intensity as fluorescence intensity divided by OD 600. We also introduced a Noise parameter to quantify average relative error in fluorescence response across concentrations, a vital aspect in biosensor development and application.
Compared to the initial state, all eight mutants showed substantial 10-100-fold enhancements in fluorescence response intensity (Figure 4B), exhibiting a positive correlation within the 0-1 mM liquiritigenin concentration range, with most reaching a plateau at 0.5 mM liquiritigenin (Figure 6, Table S4). Notably, Mut_49_70 and Mut_49_39 exhibited more higher fluorescence intensity and a broader detection range. Even at 1 mM liquiritigenin, their response curves continued to show an upward trend, while Noise parameters both remained around 20%. Next, the response time capabilities of the eight mutants were assessed (Figure 7). While the wild-type FdeR displayed an S-shaped time response curve, with fluorescence intensity increasing soomthly within 6 hours and peaking at 10 hours, Mut_49_70 exhibited a significantly longer response time. It showed rapid early-stage intensity rise within 2 hours, reaching its peak at approximately 12 hours, indicating Mut_49_70 can respond quickly to liquiritigenin in the environment.
As a real-time, highly sensitive concentration detection element, a biosensor is just such a suitable tool. But biosensors that specifically and efficiently recognize liquiritigenin are not yet available. However, naringenin, a structural analog of liquiritigenin, can be efficiently identified by artificial biosensors in previous research, including transcription factors and riboswitches.6 Based on this, we selected allosteric transcription repressor factor FdeR and a riboswitch that identify naringenin as starting points.
To evaluate specificity, we conducted dose-response experiments for naringenin in the 8 mutants (Figure 8 and Figure S8). These mutants exhibited strong specificity, with most displaying over a 5-fold difference in response intensity to liquiritigenin compared to naringenin. Notably, Mut_49_70 showed the highest fluorescence intensity among the mutants, exceeding 1.7×107 a.u.. However, its response intensity to naringenin also ranked the highest, reaching approximately 2.1×106 a.u. (still lower than wild-type FdeR). And Mut_49_39 exhibited impressive fluorescence response intensity, reaching up to 1.2×107 a.u., with significantly stronger specificity for liquiritigenin than Mut_49_70. Mut_49_39 displayed a broader dynamic range, maintaining an upward trend in fluorescence intensity even at a 1.0 mM liquiritigenin concentration, which is often considered as an ideal characteristic for a biosensor.
Additionally, We identified mutations, including D71E, S140P, M191T, and S297C, enriched in most mutants. Remarkably, elongation mutations were observed in Mut_33, Mut_48, Mut_49, and Mut_64, enhancing their responsiveness and specificity to liquiritigenin (Figure 9A and 4). Compared to Mut_49, F106L and V134L mutations enriched in Mut_49_75, Mut_49_77, Mut_49_78, Mut_49_18, and Mut_49_36, while E163V and I308S mutations were enriched in Mut_49_40 and Mut_49_41 (Figure 9B). And Mut_49_39 and Mut_49_70 showed no additional mutations, but an mutation (G to A) was detected between the promoter and RBS, potentially influencing biosensor performance at the transcriptional or translational level, resulting in both mutants constructing the best-performing biosensors. These sequencing results serve as the foundation for subsequent molecular dynamics simulations.
In summary, among the 8 mutants studied, Mut_49_39 and Mut_49_70 demonstrated a broader response range (exceeding 1 mM), faster response kinetics (within 2 hours), and a relatively normal Noiseparameter (about 20%). Consequently, Mut_49_39 and Mut_49_70 hold substantial potential for constructing highly efficient and specific liquiritigenin biosensors.
After predicting the three-dimensional structure of Mut_49, this mutant was docked with liquiritigenin, analying changes in intermolecular forces and binding pocket hydrophobicity(Figure 10). The results indicated that Mut_49 revealed higher hydrophobicity in the binding region compared to the wild-type, suggesting enhanced affinity and specificity for liquiritigenin. Unfortunately, upon analyzing its mutation energy, it was found that all mutations were neutral, contradicting the experimental results.
Index | Mutation | Mutation Energy (kcal/mol) | Effect |
---|---|---|---|
1 | A: VAL134>LEU | 0.05 | NEUTRAL |
2 | A: PHE106>LEU | 0.07 | NEUTRAL |
3 | A: PHE106>LEU A: PHE13>LEU |
0.07 | NEUTRAL |
4 | A: MET60>LEU | 0.00 | NEUTRAL |
5 | A: HIS246>GLU | 0.41 | NEUTRAL |
We then carried out molecular dynamics simulations on the complexes of FdeR with liquiritigenin and Mut_49 with liquiritigenin, respectively.6 The structural changes and the binding between WT and liquiritigenin were investigated by viewing the system potential energy, Rg, RMSD, and interaction energy (Figure 11). MMPBSA analysis was carried out to correlate the MD data with peptide characteristics (Table 3). The interaction energy of the WT was about 15% lower than that of MU, demonstrating that the binding affinity between the WT and the receptor was much higher than the mutant type.
Energy | WT | MU |
---|---|---|
Van der Waals Energy (KJ/mol) | -156.689 | -131.048 |
Electrostatic Energy (kJ/mol) | -29.978 | -7.192 |
Polar solvation Energy (KJ/mol) | 101.397 | 67.802 |
Nonpolar solvation Energy (KJ/mol) | -16.180 | -17.227 |
Total Binding Energy (KJ/mol) | -101.451 | -87.666 |
T∆S(KJ/mol) | 4.444 | 10.645 |
Total Binding Free Energy (KJ/mol) | -97.007 | -77.021 |
Currently, we have successfully constructed the plasmids (SP, AP and CP) required for PACE (Figure 12, Figure S13,S14).
We also successfully built the PACE device, tuned the operational parameters of syringe pumps and peristaltic pumps, as well as the length of needles in the chemostat and lagoons(Figure 13). This ensured the host bacteria maintained a stable cell density and allowed M13 phage propagate normally in the lagoons. The mutation rate μbp of DP6 has been measured as 1.007×10-5, and the PACE system has been initiated. We will isolate promising individuals from this pool and further test their performance as biosensors.
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4. J. Li, F. Xu D. Ji et al, Diversion of metabolic flux towards 5-deoxy(iso)flavonoid production via enzyme self-assembly in Escherichia coli. Metabolic Engineering Communications13, e00185, (2021).
DOI:https://doi.org/10.1016/j.mec.2021.e00185
5. S.G. Stahlhut, S. Siedler S. Malla et al, Assembly of a novel biosynthetic pathway for production of the plant flavonoid fisetin in Escherichia coli. Metabolic engineering 31, 84-93, (2015).
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