Thoughts Behind Our Circuit Design

Lychees have long been celebrated as an iconic fruit in Cantonese culture, but their thin, porous peel and rapid post-harvest metabolism can lead to quick moisture loss and oxidation, making them highly perishable within hours. This limits access to fresh lychees in distant markets.

To overcome this, we propose a preservation method combining a wax membrane and exogenous melatonin. The wax membrane works primarily by reducing water loss(thus decreasing weight loss) and preventing microbial infection (Figure 1.1), while the melatonin acts as a potent antioxidant , anti-aging , and immune-boosting agent (Figure 1.2). This combined approach is designed to enhance the fruit's natural defenses, extend shelf life, and maintain its freshness.

Three cute pictures
Figure 1.1. Schematic Diagram of Wax Membrane Produced by Bacteria for Lychee Protection.
Biology mechanism
Figure 1.2. Mechanism of Melatonin in Lychee Preservation
This diagram illustrates the mechanism by which melatonin (MT) exerts its effects via the phytomelatonin receptor (PMTR). MT activates PMTR, which then regulates antioxidant, anti-aging, and immune pathways. In the antioxidant pathway, superoxide dismutase (SOD) and catalase (CAT) eliminate reactive oxygen species. The anti-aging pathway involves the MAPK pathway to inhibit senescence-related transcription factors (TF). For immunity, salicylic acid (SA) and jasmonic acid (JA) levels increase.
PMTR1: Phytomelatonin Receptor; SOD: Superoxide Dismutase; CAT: Catalase; TF: Senescence-related Transcription Factor; SA: Salicylic Acid; JA: Jasmonic Acid; MT: Melatonin; MAPK: Mitogen-Activated Protein Kinase.

Components of Our Circuit

Melatonin Synthesis Pathway

Overview

Regarding the biosynthesis of melatonin, our research divides it into two components: melatonin-synthesizing proteins and cofactor regeneration. As illustrated in Figure 2, we utilize tryptophan hydroxylase (TPH), Aromatic-L-amino-acid decarboxylase (TDC), serotonin N-acetyltransferase (SNAT), and catechol-O-methyltransferase (COMT) to first convert L-tryptophan into serotonin, and then further synthesize melatonin from serotonin. Additionally, we realize the cofactor regeneration of TPH with the assistance of human-derived hQDPR (quinoid dihydropteridine reductase) and hPCBD1 (pterin-4α-carbinolamine dehydratase).

Big biology and chemical picture
Figure 2. A biosynthetic pathway for melatonin, starting from tryptophan.
hPCDB1: Human Pyran C4 Dioxygenase B1;hQDPR: Human Quinoid Dihydropteridine Reductase;MH4: 5,6,7,8 - tetrahydromonapterin;MH4-4a: 5,6,7,8 - tetrahydromonapterin - 4a;TPH: Tryptophan Hydroxylase;5 - HTP: 5 - hydroxytryptophan;TDC: Aromatic-L-amino-acid decarboxylase;COMT: Catechol - O - methyltransferase;SNAT: Serotonin N - acetyltransferase;NAD+: Nicotinamide Adenine Dinucleotide (Oxidized Form);NADH: Nicotinamide Adenine Dinucleotide (Reduced Form).

Serotonin Synthesis

There are two pathways for the synthesis of serotonin. One is to convert tryptophan into tryptamine and then into serotonin via TDC and tryptamine 5-hydroxylase (T5H), and the other is to convert tryptophan into 5-hydroxytryptophan and then into serotonin via TPH and TDC .

Chemical reaction
Figure 3. Two Pathways for Tryptophan to Synthesize Serotonin.
TDC: Aromatic-L-amino-acid decarboxylase; T5H: Tryptamine 5 - Hydroxylase; TPH: Tryptophan Hydroxylase.

Regarding the selection of hydroxylases T5H and TPH, we have conducted the following analysis. TPH enzymes belong to the family of aromatic amino acid hydroxylases, which are found in animals and bacteria, and require a tetrahydrobiopterin as a cosubstrate . The CCU-Taiwan team (2024) demonstrated successful expression of functional monomeric-human Tryptophan Hydroxylase 1 (m-hTPH1) in Escherichia coli DH5α.

However, the reaction at the T5H step is associated with the hydroxylation reaction, which is predominantly mediated by cytochrome P450-dependent monooxygenases (P450s) and 2-oxoglutarate-dependent dioxygenases . T5H is a type of cytochrome P450 enzyme that is difficult to fold successfully in prokaryotic cells such as Escherichia coli and Bacillus subtilis. The SCU China/Malatonion team (2017) also reported failure in synthesizing the T5H enzyme in Escherichia coli.

Based on the above information, we will select the TPH-TDC pathway for serotonin production.

Regarding Aromatic-L-amino-acid decarboxylase (TDC), we observed that it catalyzes two reactions: one involves the decarboxylation of L-tryptophan, and the other is the decarboxylation of 5-hydroxytryptophan. So, we conducted model analysis on these two reactions (Figure 3.1).

The TDC protein in Part: BBa_K2276001 was once used to realize the synthesis of tryptamine.

TDC (Part: BBa_K2276001) and tryptophanTDC (Part: BBa_K2276001) and 5 - hydroxytryptophan
Kcat1.11[1/sec]1.11[1/sec]
Km0.07[mM]0.08mM
Table 1. Kinetic parameters Kcat and Michaelis constant Km of the enzyme-cofactor pair TDC (Part: BBa_K2276001) and tryptophan and that of the enzyme-cofactor pair TDC (Part: BBa_K2276001) and 5 - hydroxytryptophan.
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Figure 3.1(a) TDC (Part: BBa_K2276001) and Tryptophan
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Figure 3.1(b) TDC (Part: BBa_K2276001) and 5-hydroxytryptophan
Figure 3.1. Kinetic Parameters of TDC (Part: BBa_K2276001) with Different Substrates
(a): Kinetic parameters of the enzymatic reaction corresponding to TDC (Part: BBa_K2276001) and tryptophan Kcat and Michaelis constant Km calculated using the prediction model, and the positions of the predicted values of Kcat and Km in the respective histograms of experimental values are shown.
(b): Kinetic parameters of the enzymatic reaction corresponding to TDC (Part: BBa_K2276001) and 5 - hydroxytryptophan Kcat and Michaelis constant Km calculated using the prediction model, and the positions of the predicted values of Kcat and Km in the respective histograms of experimental values are shown.

Based on this model analysis (Table 1 and Figure 3.1), we found that, we found that, under the condition of the same reaction rate, TDC exhibits a higher affinity for 5-hydroxytryptophan than for tryptophan. We aim to inhibit the TDC-catalyzed synthesis of tryptamine through protein evolution of TDC, thereby increasing the production of serotonin and melatonin.

It was found that Part:BBa_K3999004 involves modifications to the protein sequence of TDC; however, the direction of these modifications has not been clearly specified, and the team did not perform product detection either. Therefore, we used the model from Hebditch et al. (2017) to predict the solubility of TDC.

ProteinBBa_K3999001 TDC (modified)BBa_K2276001 TDC (original)FadR (Insoluble Protein)
Solubility0.120.2470.145
Table 2. Solubility Prediction: This model specializes in Escherichia coli.
Prediction of proteins we desgined by SOLUPROT

The model predicts that the solubility of the TDC protein in this Part:BBa_K3999004 is extremely low. We hypothesize that it may lack functionality, so we will not use this part. After evaluation, the cycle for protein engineering is quite long, and unfortunately, we do not have sufficient time to conduct protein evolution.

So we choose the TDC protein of this part to realize the synthesis of serotonin, and on this basis, we carry out codon optimization for Bacillus subtilis.

Melatonin Synthesis

As illustrated in Figure 4, the synthesis of melatonin from serotonin involves two key reactions: acetylation and methylation. Among these, for the methylation step, there are two alternative enzymes available, namely acetylserotonin O-methyltransferase (ASMT) and catechol-O-methyltransferase (COMT).

Chemical reaction
Figure 4. Two Pathways for Tryptophan to Synthesize Melatonin from Serotonin. (Back et al., 2016)
SNAT: Serotonin N-Acetyltransferase; ASMT: Acetylserotonin *O*-Methyltransferase; COMT: Catechol-O-Methyltransferase.

Byeon and Back (2016) demonstrated the peak melatonin production of 1465 ng/mL detected after 24 h in the dual sheep SNAT plus rice COMT expression cassette in E. coli was considerably greater than that of other heterologous host plants overexpressing either SNAT or ASMT.

So, we decided to apply the dual sheep SNAT and rice COMT to transfer serotonin to melatonin.

Cofactor Regeneration

In our research, we found that TPH requires tetrahydrobiopterin as cofactor.

In mammals, tryptophan hydroxylase (TPH) utilizes tetrahydrobiopterin (BH4) as its cofactor; meanwhile, in bacteria, 5,6,7,8-tetrahydromonapterin (MH4) serves as an alternative cofactor for TPH .

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Figure 5(a) Mammals
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Figure 5(b) Bacteria
Figure 5. Utilization of Different Cofactors by TPH and Cofactor Cycling in Various Organisms.
(a) The cycling of TPH cofactor BH4 in mammals. BH4: Tetrahydrobiopterin; qBH2: Quinonoid Dihydrobiopterin; 4α-OH-BH4: Tetrahydrobiopterin-4α-carbinolamine; hPCBD1: Human Pterin-4α-carbinolamine dehydratase; hQDPR (/DHPR_HUMAN): Human quinoid dihydropteridine reductase.
(b) The cycling of TPH cofactor BH4 in mammals.
MH4: 5,6,7,8-tetrahydromonapterin; MH4-4a: tetrahydromonapterin-4a; MH2:Dihydromonapterin, PCD: Pterin-4α-carbinolamine dehydratase; DHMR: Dihydromonapterin reductase;

We observed that BH4 and MH4 have similar structures (Figure 6). Notably, the CCU-Taiwan team (2024) achieved regeneration of MH4 in E. coli by leveraging the recycling enzymes of BH4, without the supplementation of exogenous BH4. This strategy resulted in a significant increase in 5-hydroxytryptophan (5-HTP) production.

Chemical reaction
Figure 6. Common Structural Formula of BH4 and MH4.
BH4 (Tetrahydrobiopterin): -R = -CH3;
MH4 (5,6,7,8-tetrahydromonapterin): -R = CH2OH.

So we hypothesized that the BH4 recycling enzymes hPCBD1 and hQDPR may be applicable to the recycling of MH4.

According to the article by , we found that the substrate binding mode and the reaction mechanisms of pterin-4a-aminoalcohol dehydrogenase (PCD) isolated from Pseudomonas aeruginosa are basically the same as those of human PCD. That is to say, hPCBD1 can catalyze MH4 - 4a.

Based on this finding, our model study focuses on the binding efficiency and catalytic efficiency of hQDPR (human Quinonoid Dihydropteridine Reductase) and DHMR toward MH2 (Figure 6.1).

MH2 and hQDPR(/DHPR_HUMAN)MH2 and DHMR
Kcat54.64[1/sec]17.68[1/sec]
Km0.03[mM]0.03mM
Table 3. Kinetic parameters Kcat and Michaelis constant Km of the enzyme-cofactor pair MH2 and hQDPR(/DHPR_HUMAN) and tryptophan and that of the enzyme-cofactor pair MH2 and DHMR.
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Figure 6.1(a) MH2 and hQDPR(/DHPR_HUMAN)
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Figure 6.1(b) MH2 and DHMR
Figure 6.1. Kinetic Parameter Predictions for Different Enzyme - Cofactor Pairs.
(a): Kinetic parameters Kcat and Michaelis constant Km of the enzyme-cofactor pair MH2 and hQDPR (/DHPR_HUMAN) calculated using the prediction model, and the positions of the predicted values of Kcat and Km in the respective histograms of experimental values are shown.
(b): Kinetic parameters Kcat and Michaelis constant Km of the enzyme-cofactor pair MH2 and DHMR calculated using the prediction model, and the positions of the predicted values of Kcat and Km in the respective histograms of experimental values are shown.

Through model analysis, hQDPR exhibits higher catalytic efficiency toward MH2 than DHMR. We initially concluded that the two recycling enzymes of BH4 are applicable to the recycling of MH4; however, due to subtle differences in molecular structure, this recycling process is expected to generate new substances.

Subsequently, during our consultation with Professor Qi Feng, Vice Dean of the College of Life Sciences at Fujian Normal University, he indicated that the recycling enzymes of BH4 could be applied to MH4. Furthermore, we ultimately achieved a relatively high yield using these recycling enzymes; for details, please refer to the Results section.

Plasmids We utilized the two Bacillus subtilis - Escherichia coli shuttle plasmids, pWB980 - ori and pHY300PLK, as vectors for the elements involved in the synthesis of Serotonin and Melatonin, respectively.

In addition, we employed P43 promoters and double terminators that are functional in both Escherichia coli and Bacillus subtilis. Moreover, we performed Bacillus subtilis codon optimization for these six proteins.

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Figure 7(a)
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Figure 7(b)
Figure 7. Plamsids for Melatonin Synthesis.
(a): pWB980-ori_TPH_hPCBD1_hQDPR-flag_TDC-his can synthesize serotonin from L-tryptophan;
(b): pHY300PLK_SNAT-his_COMT-flag can synthesize melatonin from serotonin.

Wax Synthesis Pathway

Overview

Our wax synthesis process is mainly divided into two parts: fatty acid synthesis and accumulation, synthesis from fatty acid to wax. As Figure 8 (a) shows, we promote the synthesis of fatty acids and fatty acyl-CoA in bacteria through fatty acid metabolism regulator(FadR) under the condition of existing lactose. Under the condition of no lactose, we first transform fatty acyl-CoA into fatty alcohols by fatty acyl-CoA reductase A(AcrB), and then synthesize waxes from fatty alcohol and fatty acids by wax ester synthase/acyl-CoA:diacylglycerol acyltransferase(WS/DGAT). At the same time, we also express glucose-6-phosphate dehydrogenase(G6PD) to promote the synthesis of NADPH, providing sufficient sources for the reaction of first step.

During the experimental phase, for convenience, we employed the pBluescript II sk(+) plasmid as our plasmid backbone. This plasmid offers advantages such as high copy number, low loss rate, and multiple cloning sites, making it suitable for use in E. coli. Our plasmid construction is illustrated as shown in figure 8 (b):

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Figure 8 (a). Schematic diagram of lactose-inducible regulatory sequences. lacl: lacl gene, Lacl: Lac repressor protein, lacO: lac operator, λ repressor: Lambda repressor, fadR: Fatty Acid Degradation Repressor.
J23119, P43: Promoter
J435361, K5040003, K3914035: Terminator
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Figure 8 (b). The complete plasmid of wax group

In potential future developments, we plan to transition to the pWB980-ori plasmid backbone. This vector serves as an E. coli-Bacillus subtilis shuttle vector, enabling efficient expression in both bacterial species. This allows us to conduct initial experiments in genetically manipulable E. coli, then directly transform the successful construct into Bacillus subtilis.

Fatty acid synthesis and accumulation

The most significant advantage of introducing and overexpressing an exogenous FadR plasmid is its global regulatory capacity. FadR activates the transcription of almost all fatty acid synthesis (fab) genes , while simultaneously inhibiting the expression of all fatty acid degradation (fad) genes . This coordinated "push-and-pull" effect allows carbon flux to be more efficiently directed toward fatty acid synthesis and prevents the degradation of the synthesized products, thereby systematically increasing the net yield of fatty acids . Research has shown that merely by regulating the expression of FadR, fatty acid production can be increased several-fold, reaching 73% of the theoretical yield .

Regulatory Target GeneBasic Regulatory Type of FadRWhen long-chain fatty acyl-CoA concentration is LOWWhen long-chain fatty acyl-CoA concentration is HIGH
Fatty acid synthesis genes (fab family)ActivatorFadR binds to the promoter, activating gene expression.FadR dissociates from the promoter, turning off gene expression.
Fatty acid degradation/transport genes (fad family)RepressorFadR binds to the operator, repressing gene expression.FadR dissociates from the operator, allowing gene expression.
Table 4. The dual-sided effect of FadR

Another optional enzyme is Thioesterase. It promotes the whole synthesis process by remove the products, hydrolyzing synthetic fatty acyl-ACP to release free fatty acids. However, this enhancement pathway depends on the total amount of fatty acyl-ACP synthesized upstream. If the fundamental synthesis level remains unchanged, the ultimate enhancement effect will not be significant . So we did not choose it in the end.

Synthesis from fatty acyl-CoA to wax

The common pathway from fatty acid synthesis to wax production is: acyl-CoA → fatty acid → fatty aldehyde → fatty alcohol → wax (synthesis of macromolecular fatty acids and fatty alcohols). However, in typical bacteria (such as Escherichia coli and Bacillus subtilis), the genes encoding the individual enzymes in this pathway are missing. Therefore, completely reconstructing this pathway would require an enormous amount of work.

To optimize this pathway, we reviewed extensive literature. Ultimately, we discovered a unique fatty acyl-CoA reductase in the novel strain Marinobacter aquaeolei VT8, capable of directly catalyzing the reduction of fatty acyl-CoA to fatty alcohols . Through further research, we obtained the corresponding nucleotide sequence and constructed it into our plasmid.

Figure 9. (Willis et al., 2011)
Figure 9. Two reaction schemes of fatty acyl-CoA reductase .
Proposed reaction schemes for fatty acyl-CoA reductase. Scheme I shows the two-step reduction mechanism proposed for known bacterial fatty acyl-CoA reductases. Scheme II shows the single-step reduction proposed for the fatty acyl CoA reductase enzymes of many higher eukaryotes.

Compared to conventional pathways, this enzyme combines the functions of fatty acyl-CoA reductase and fatty aldehyde reductase into a single enzyme, significantly enhancing reaction efficiency. We have pioneered its application in Escherichia coli to achieve highly efficient wax synthesis within the bacterium.

Below are our predictions for the enzyme's structure and fundamental kinetic parameters such as Km and Kcat. Please note that the protein sequence used for prediction has been modified by us to include an MBP sequence for enhanced solubility. For detailed explanations regarding this modification, please refer to the solubility notes section at the end of this subsection.

Predicted structure of AcrB by Alphafold
Figure 9.1(a). Predicted structure of AcrB by Alphafold

The AcrB enzyme has two substrates: one is acyl-CoA, which acts as the reduced main reactant, and the other is NADPH, which serves as the reducing agent. We have also further predicted the kinetic parameters of this enzyme, and the results are as follows.

AcrB (Part: BBa_25IXNS8H) and Acyl-CoAAcrB (Part: BBa_25IXNS8H) and NADPH
Kcat9.03[1/sec]9.03[1/sec]
Km0.03[mM]0.04[mM]
ESP0.520.82
Table 5. Kinetic parameters (Kcat and Michaelis constant Km) of enzyme AcrB (Part: BBa_25IXNS8H) for acyl-CoA (as reactant/substrate) and for cofactor NADPH
Prediction of Km and Kcat by DeepMolecules
Figure 9.1(b). Prediction of Km and Kcat by DeepMolecules: For substrate: Acyl-CoA (Enzyme: AcrB)
Prediction of Km and Kcat by DeepMolecules
Figure 9.1(c). Prediction of Km and Kcat by DeepMolecules: For substrate: NADPH (Enzyme: AcrB)

The prediction results indicate that it performs well in real situations.

To ensure high efficiency of this pathway, we added an additional safeguard for the critical step catalyzed by AcrB. Since the AcrB catalytic process requires substantial NADPH, and NADPH itself participates in numerous biochemical reactions within the cell, it can easily lead to NADPH deficiency in engineered bacteria. Therefore, we introduced G6PD to guarantee sufficient NADPH availability. In bacteria, NADPH is primarily generated via the pentose phosphate pathway (PPP), with G6PD serving as the first rate-limiting enzyme in this pathway. Thus, overexpressing this enzyme enables NADPH enrichment, thereby promoting the synthesis and accumulation of fatty acids and fatty alcohols .

Predicted structure of AcrB by Alphafold
Figure 9.2(a). Predicted structure of G6PD by Alphafold
G6PD (Part: BBa_25XVCT22) and NADP+
Kcat263.24[1/sec]
Km0.03[mM]
ESP0.52
Table 6. Kinetic parameters (Kcat and Michaelis constant Km) of enzyme G6PD (Part: BBa_25XVCT22) for NADP+
Prediction of Km and Kcat by DeepMolecules
Figure 9.2(b). Prediction of Km and Kcat by DeepMolecules: For substrate: NADP+ (Enzyme: G6PD) ESP prediction score: 0.75

We also use models to analyse the structure and enzyme kinetic reaction parameters of G6PD, showing that it performs well in real situations.

The step of synthesizing wax from fatty alcohols is achieved by expressing WS/DGAT. WS/DGAT is a “star enzyme” frequently used in metabolic engineering. It possesses exceptionally high catalytic efficiency and activity, along with broad substrate selectivity, enabling full utilization of fatty alcohols and fatty acids generated in preceding pathways .

Below are our predictions for the enzyme's structure and fundamental kinetic parameters such as Km and Kcat. Please note that the protein sequence used for prediction has been modified by us to include an MBP sequence for enhanced solubility. For detailed explanations regarding this modification, please refer to the solubility notes section at the end of this subsection.

Predicted structure of AcrB by Alphafold
Figure 9.3(a) Predicted structure of WS/DGAT by Alphafold

The WS/DGAT enzyme has two substrates: acyl-CoA and fatty alcohol. We further predicted the kinetic parameters of this enzyme, and the results are as follows.

WS/DGAT (Part: BBa_2533I9RA) and Acyl-CoAWS/DGAT (Part: BBa_2533I9RA) and Fatty alcohol
Kcat2.82[1/sec]2.82[1/sec]
Km0.02[mM]0.02[mM]
ESP0.600.67
Table 7. Kinetic parameters (Kcat and Michaelis constant Km) of enzyme WS/DGAT (Part: BBa_2533I9RA) for substrate acyl-CoA and Fatty alcohol
Prediction of Km and Kcat by DeepMolecules
Figure 9.3(b) Prediction of Km and Kcat by DeepMolecules: For substrate: Acyl-CoA (Enzyme: WS/DGAT)
Prediction of Km and Kcat by DeepMolecules
Figure 9.3(c) Prediction of Km and Kcat by DeepMolecules: For substrate: Fatty alcohol (Enzyme: WS/DGAT)

The prediction results indicate that WS/DGAT performs well in real situations.

The complete wax synthesis pathway
Figure 10. The complete wax synthesis pathway. WS/DGAT: Wax ester synthase/acyl-CoA diacylglycerol acyltransferase, G6PD: Glucose-6-phosphate dehydrogenase, AcrB: Acridine resistance protein B.

However, due to the low solubility of AcrB and WS/DGAT in E. coli, we consulted additional literature and decided to create a fusion protein by appending the Maltose-Binding Protein (MBP) sequence after their nucleic acid sequences . MBP aids in the proper folding of AcrB, thereby enhancing solubility. We predicted solubility using SOLUPROT, and the results confirmed that the solubility of AcrB and WS/DGAT increased significantly, demonstrating the effectiveness of our approach. The comparsions are as follows:

P.S. The solubility value are normalized into the range of (0,1), a higher value represent a higher solution. Solubility score above 0.5 indicates soluble expression, score below 0.5 indicates insoluble expression in Escherichia coli. For a better informing, we also provide two normal proteins within E.Coli for comparsion.
SoulProt / Job ID: ea4d3961c6
Gene namePart IDSolubility before modificationSolubility after modificationModification (Location)
WS/DGATBBa_2533I9RA0.3550.9093xFlag+MBP (N')
AcrBBBa_25IXNS8H0.2760.8916xHis+MBP (N')
G6PDBBa_25XVCT220.5920.589G4S2+HA (C')
CIBBa_25LEDVII0.9090.919G4S2+Myc (C')
LacIBBa_K10880180.925N/AN/A
FadRBBa_K30401160.932N/AN/A
Table 8. The solubility of genes before and after tag modification, predicted by SoluProt .WS/DGAT: Wax Ester Synthase / Acyl-CoA:Diacylglycerol Acyltransferase, AcrB: Acridine resistance protein B, G6PD: Glucose-6-phosphate dehydrogenase, CI: Lambda cI repressor protein, LacI: Lac repressor, FadR: Fatty acid degradation Regulator.

Wax Synthesis Regulation

As previously described, our gene expression occurs in two distinct phases: Phase One, in the presence of lactose, involves the expression of fatty acid metabolism regulator(FadR) and the suppression of the aforementioned three proteins; Phase Two, in the absence of lactose, involves the expression of these three proteins and the suppression of FadR. This "NOT gate" structure ensures that the two phases—carbon source uptake and carbon source utilization—remain mutually independent, enabling bacterial growth on lychee under “carbon-deprived” conditions.

Loss curves abnormal scale
Figure 12.(a) Logic Not circuits design
Prediction of Km and Kcat by DeepMolecules
Figure 12.(b) Two states of the Biological Logic Circuits

After reviewing extensive literature, we ultimately selected the lactose operon and λ repressor as the regulatory switches for our biological logic circuit.

The lactose operator boasts a long history, extensive research, and strong modifiability and adaptability . Most importantly, in our final design, we can replace the majority of the carbon source in the bacterial culture medium with lactose, simultaneously achieving gene regulation and bacterial cultivation.

The λ repressor also exhibits excellent modifiability and has been employed in numerous experiments . A noteworthy point is the high orthogonality between the λ repressor and the lactose operator, meaning these two regulatory systems operate independently without interfering with each other. This is both rare and critically important in multi-system interactive modifications.

The First Phase

Chemical reaction
Figure 13.1. Gene interactions in the first stage
lacl: lacl gene, Lacl: Lac repressor protein, lacO: lac operator, λ repressor: Lambda repressor, fadR: Fatty Acid Degradation Repressor
J23119, P43: Promoter
J435361, K5040003, K3914035: Terminator

At this phase, we aim for bacteria to take up carbon sources in large quantities and store them within the cells. Since the subsequent environment lacks carbon sources, failure to store them in advance may lead to reduced yields or even bacterial death.

In the regulatory sequence, the lac inhibitor(lacI) is controlled by a constitutive promoter, enabling its continuous expression in bacteria. When lactose is present, it non-competitively binds to lacI, thereby inhibiting lacI's interaction with the lac operator(lacO) and preventing the activation of downstream lacO genes.

We designed two downstream genes: λ repressor and FadR. The λ repressor binds to the OR1/OR2 operon, inhibiting expression of downstream wax synthase genes. FadR promotes the accumulation of acyl-CoA, preparing for its subsequent extensive utilization. Therefore, at this stage, we only achieve cellular uptake and storage of the carbon source without its utilization.

Chemical reaction
Figure 13.2. OR1/OR2 and wax synthesis proteins
G6PD: Glucose-6-phosphate dehydrogenase, AcrB: Acridine resistance protein B, WS/DGAT: Wax ester synthase/acyl-CoA diacylglycerol acyltransferase

The Second Phase

Chemical reaction
Figure 13.3. Gene interactions in the second stagebu
lacl: lacl gene, Lacl: Lac repressor protein, lacO: lac operator, λ repressor: Lambda repressor, fadR: Fatty Acid Degradation Repressor
J23119, P43: Promoter
J435361, K5040003, K3914035: Terminator

During this phase, we expect bacteria to convert the carbon source accumulated in the previous phase into wax. Due to the absence of lactose in the growth environment, lacI can directly bind to lacO, thereby inhibiting the expression of the downstream λ repressor and FadR. Consequently, the λ repressor cannot bind to OR1/OR2, allowing the expression of the three wax synthesis genes.

Therefore, the cells transition from a phase of taking up and storing carbon sources to a phase of extensively utilizing carbon sources to produce wax.

References

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