iGEM 2024 — Team nnu-china

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.{% endblock %}
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**May**

**June**

**July**

**August**

**September**

May

### **Wet Lab**

1. We reviewed literature and communicated with the PI to preliminarily determine the project content.

2. We visited the laboratory, conducted initial lab safety training and engaged in learning and training of basic operations.

3.We consulted literatures, communicated with the instructors to understand the background of droplet microfluidic high-throughput screening and learned related operations.

### **Human Practice**

1. We held the first meeting of NNU-CHINA to collectively understand the philosophy of iGEM, the significance of the project and its potential impact, and conducted team division of labor.

2. We established official accounts on WeChat, Bilibili and Tik Tok to better promote the NNU-CHINA team's project and green environmental protection concept.

3. We conducted a questionnaire survey on "How to Carry Out Synthetic Biology Education and Popularization."

4. We engaged in offline project exchanges with the iGEM team from Hubei University to discuss challenging problems, learn together and make mutual progress.

![](https://static.igem.wiki/teams/5303/notebook/h1.png)

June

### **Wet Lab**

1. We communicated with the PI and instructor to finalize the project content.

2. We reviewed extensive literatures to understand the background of biopesticides and 5-ALA, as well as the current context of food production.

3. We screened highly active *ALAS* genes from different sources, synthesized the key enzyme of 5-ALA through the C4 pathway. And we synthesized *ALAS* genes from various sources, constructed expression plasmids, transformed them into *E. coli BL21(DE3)* chassis strains for fermentation verification and obtained a highly active chassis strain.

4. We conducted single-factor experiments to optimize the concentration and timing of IPTG addition during fermentation to achieve optimal experimental conditions. In the experiment, we found that the production of 5-ALA changed little after 24h of bacterial growth, so we suspected that there might be a situation of plasmid loss. We further verified the plasmid loss finding that the plasmid loss rate reached 45% after 24h, which affected the stable expression of *ALAS* gene. Through literature research, we plan to enhance stability using CRISPR-associated transposons (CASTs) system in the next steps.

### **Human Practice**

1. We completed the design and production of the team logo, uniforms, and flag.

2. We designed a questionnaire on "Understanding of Biopesticides and Synthetic Biology Among Different Countries, Industries, and Age Groups", conducted online and offline surveys and analyzed the data.

3.We published articles on our WeChat public account to introduce the project and popularize a series of green agriculture policies.

4. We held a project discussion meeting to plan upcoming promotional activities, including team material production, interviews, science popularization lectures, and other practical events.

![](https://static.igem.wiki/teams/5303/notebook/h2.png)

July

### **Wet Lab**

1. According to the structural characteristics of ALAS enzyme, we used artificial intelligence prediction method to predict the key enzyme, and obtained some mutation sites that may improve the enzyme activity.

2. We performed mutations based on predicted sites, conducted sequence alignment and carried out cultivation and fermentation verification in batches.

3. We constructed plasmids related to transposition to integrate the key enzyme gene *ALAS* into the genome and verified the transposition results.

4. We learned the relevant operations of ARTP mutagenage, explored the best mutagenesis time of *E. coli* and mutagenesis the modified engineering bacteria.

5. We explored suitable oil phases, surfactants and optimal inner and outer phase flow rates to generate droplets with uniform size, good stability and low emulsification tendencies.

### **Human Practice**

1. We participated in the CCiC conference in Suzhou to further promote the team project and gain practical insights.

2. We visited the Lingshan Grain Logistics Base in Nanjing for research, gaining a more intuitive and in-depth understanding of modern grain production and logistics.

3. We carefully produced various materials to better promote the project, showcase team creativity and present our image. These included bookmarks, brochures, posters, comics, board games, picture books and poetry collections.

4. We visited Laifeng Street Community and Yudaiyuan Community in Nanjing to introduce our team, synthetic biology and green agriculture to children in simple and engaging ways. For the elderly, we went to Qimin Road Community and Guochang Community to discuss our project and share knowledge about green agriculture and biopesticides.

5. We held an online exchange with the iGEM team from Huazhong Agricultural University to learn about each other's research topics, solve project challenges and learn collaboratively.

6. We engaged in in-depth discussions with NJTech-China-A and Ulink-SZ teams at Nanjing Tech University, exploring scientific research paths and sharing experiences and insights.

7. We visited the agricultural industrial park in Nanjing to understand crop growth conditions and the use of biopesticides.

8. We released a series of micro-lesson videos on Bilibili to promote knowledge about green agriculture and microbiology.

9. We conducted a volunteer activity picking up garbage along the Yangtze River to promote green and environmental protection concepts, and visited the Nanjing Sewage Treatment Plant.

![](https://static.igem.wiki/teams/5303/notebook/h3.png)

August

### **Wet Lab**

1. We used enzyme-constrained models to predict beneficial targets for 5-ALA synthesis, applied pJ23119 and strong RBS to enhance the expression of related genes. Finally, we verified which targets were beneficial for product synthesis through fermentation.

2. We calculated the required concentration of *E. coli* for single encapsulation in droplets using the Poisson distribution formula.

3. We used high-throughput equipment to encapsulate bacterial solutions and culture media into droplets, observing the growth of *E. coli*. Simultaneously we cultured the same amount of *E. coli* in liquid media for comparison.

### **Human Practice**

1. We visited Jiangsu Fangcaodu Agricultural Technology Co., Ltd. for interviews to further understand the development status of green agriculture and explore the field more deeply.

2. We collaborated with our university's teaching support group to conduct online lectures on microbiology and recorded experimental video demonstrations at Xiliang Village Complete Primary School in Xinxiang City, Henan Province.

3. We organized a debate competition on "Do Genetically Modified Crops Bring More Advantages or Disadvantages?"

4. We teamed up with NJTech-China-A, OUC-Haide, SDU-CHINA, and HUBU-China to create the "Biological Glow, Becoming a Torch" science popularization podcast series.

5. We developed an online dictionary providing concise explanations of professional terms related to the project to eliminate public understanding barriers caused by technical jargon.

6. We carefully produced a promotional video to better showcase our project and key technical points.

![](https://static.igem.wiki/teams/5303/notebook/h4.png)

September

### **Wet Lab**

1. We cultivated *E. coli* at appropriate temperatures. When they reached the logarithmic phase in droplets, we used OD measurements to select droplets with higher bacterial counts.

2. We broke the selected droplets, plated them on agar plates and conducted fermentation verification of the products after single colonies grew.

3. We used GraphPad Prism to process experimental data and refined all scripts.

### **Human Practice**

1. We set up a booth on green agriculture and synthetic biology at the college's orientation site to educate new students and parents.

2. We hosted a laboratory open day, inviting nearby high school students for a tour.

3. We created an audio picture book series tailored for young children and collaborated with other teams to inspire kids through vivid and interesting stories.

![](https://static.igem.wiki/teams/5303/notebook/h5.png)

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a technical aspect of your project by going through at least one iteration of the engineering design cycle. This achievement should be distinct from your Contribution for Bronze.{% endblock %}
{% block page\_content %}

**Overview**

**Cycle1**

**Cycle2**

**Cycle3**

**Cycle4**

**Cycle5**

**Reference**

Overview

Our goal is to achieve cost-effective and sustainable production of 5-ALA. We hope to improve the production efficiency by improving the stability and activity of ALAS in *E. coli*, and to develop a high-throughput screening method for high-yielding strains of 5-ALA to further reduce costs and improve economic benefits in order to solve the time-consuming and laborious problem of screening existing high-yielding strains.

To do this, we employ the Design-Build-Test-Learn (DBTL) loop, an important engineering framework in synthetic biology. Each cycle consists of multiple modules that build and optimize the final system step by step, and can be iterated over and over again to achieve full optimization.

First, we reviewed the relevant literature and discussed with the project leader and mentor to design a preliminary plan. Then, as we show on our Human Practice page, we interviewed experts and stakeholders to gather feedback on the context of the project and incorporate their recommendations into the project design. We applied DBTL loops in all aspects of the project. On this page, we'll explain in detail how to implement this cycle in our project design.

![](https://static.igem.wiki/teams/5303/engineering/e1.png)

Cycle 1: Screening of *ALAS* genes from different species

### **Design**

The pathways of microbial synthesis of 5-ALA mainly include C4 pathway and C5 pathway. In the C4 pathway, glycine and succinyl-CoA are catalyzed by 5-aminolevulinic acid synthetase (ALAS) to generate 5-ALA in a ratio of 1:1 under the action of coenzyme pyridoxal 5-phosphate (PLP). The C5 pathway uses glutamic acid as a precursor to generate 5-ALA through three enzymes: glutamine-tRNA synthetase (GltX), NADPH-dependent glutamine-tRNA reductase (HemA), and glutamine-1-semialdehyde aminotransferase (HemL). In contrast, the C5 pathway is more complex, while the C4 pathway requires only one step of enzyme-catalyzed reaction, and the exogenous substrate glycine is inexpensive. Therefore, we believe that the production of 5-ALA through the C4 route is a more economical and sustainable option.

![](https://static.igem.wiki/teams/5303/description/1.png)

Fig. 1 5-ALA synthesis pathway

In the C4 pathway, one of the key problems in promoting the synthesis of 5-ALA is the low enzyme activity of the ALAS currently being tried, so we tried to find an ALAS enzyme with the highest activity to promote the production of 5-ALA.

### **Build**

Through literature research, we selected some ALAS genes with high activity reported in the literature, which are from *Rhodobacterium capsulatum*, *Agrobacterium*, and *Rhodopseudomonas palustris*, respectively. We downloaded the gene information from the NCBI (National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/)) according to the serial number, and synthesized and loaded it onto the pET24a plasmid vector for expression testing.

![](https://static.igem.wiki/teams/5303/engineering/e2.png)

Fig. 2 ALAS genes from different sources were composed into plasmids

### **Test**

The ALAS genes derived from *Agrobacterium*, *Rhodobacterium capsulatum* and *Pseudomonas marsh* (*AFhemA-ALAS*, *RChemA-ALAS*, *RPhemA-ALAS*) were introduced to *E. coli BL21(DE3)* (the generated strains were named Af, Rc, Rp), and the growth (OD600) and 5-ALA yield of the recombinant strain were detected in real time. Then we selected ALAS genes with higher activity and suitable for further exploration for the next experiments.

At the same time, we also optimized the induction time and dose of IPTG to obtain the optimal expression of ALAS.

### **Learn**

The results showed that after 24 h, the yield of 5-ALA in the Af, Rc and Rp was significantly higher than that in the control group. The yield of 5-ALA in each group stabilized after 24 h. At this time, the average yield of 5-ALA in the control group was only 0.05 g/L, while the average yield of 5-ALA in the Af, Rc and Rp groups reached 1.45, 1.67 and 1.55 g/L respectively, which was 280%, 324% and 300% of the control group. It can be seen that *E. coli* with *RChemA-ALAS* showed relatively higher yields of 5-ALA, and we decided to use the ALAS gene from *Rhodobacterium capsulatum* for the next study.

![](https://static.igem.wiki/teams/5303/results/od.png)

Fig. 3 Comparison of ALAS from different sources with *E. coli BL21(DE3)* fermentation results

Next, we explored the dosage of IPTG that needs to be added and the timing of addition to obtain the optimal conditions for fermentation. We used different amounts of IPTG (0.05, 0.1, 0.15, 0.2 mmol/L) and different treatment times (2, 4, 6, 8 h) to induce ALAS expression. The results showed that the yield of IPTG at 0.05 mmol/L was significantly higher than that at other concentrations, and the maximum was 1.60 g/L. The best effect was achieved by adding IPTG after 2 h of fermentation.

In the end, we chose to add 0.05 mmol/L of IPTG as our fermentation conditions after 2 h of fermentation.

![](https://static.igem.wiki/teams/5303/engineering/iptg0930.png)

Fig. 4 Optimization of IPTG addition quantity and addition time

Cycle 2: Modify ALAS and verify mutation sites

### **Design**

In order to improve the activity of ALAS, we modified ALAS enzyme molecules. Under the guidance of Prof. Yang Gu, we predicted a series of mutation sites through artificial intelligence calculations (Table. 1). Here, we cloned the ALAS gene in *Rhodobacterium capsulatum* on the PET24a plasmid to construct the PET24a-RCALAS recombinant plasmid, and introduced it into *E. coli* for fermentation detection.

Table. 1 Predicted 12 mutation sites

|  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
| RChemA-ALASG58A | RChemA-ALASG60A | RChemA-ALASS139E | RChemA-ALASA218I | RChemA-ALASG244A | RChemA-ALAST245Q |
| RChemA-ALASV252W | RChemA-ALASI292A | RChemA-ALASS378M | RChemA-ALASM393L | RChemA-ALASK13R | RChemA-ALASI203L |

### **Build**

Mutant construction was carried out on the 10 predicted mutation sites. *E. coli BL21 (DE3)* was used as the recipient cell to transform the constructed plasmid. After transformation, it was transferred to a test tube containing LB medium and incubated in a 37°C shaker incubator for culture and fermentation experiments.

### **Test**

The OD600 (detected growth) of each test tube after fermentation for 12 h, 24 h, 36 h, and 48 h was measured by spectrophotometer, and the yield of 5-ALA was measured by microplate reader after treatment (see Experiment).

### **Learn**

Fig. 5 shows the OD600 of RC (non-mutated group) and 12 mutants at 12 h, 24 h, 36 h, and 48 h, and the OD600 values of each group increased steadily, and there was no big difference.

Fig. 6 shows the yield of 5-ALA corresponding to the 12 mutation sites, and the yield of 5-ALA tends to stabilize after 24 h. The results of 24 h fermentation are shown in the figure. We found that the 5-ALA yield of mutants RChemA-ALASG58A, RChemA-ALASG60A, RChemA-ALASS139E, RChemA-ALASG244A, RChemA-ALASI292A, RChemA-ALASS378M, and RChemA-ALASM393L was significantly higher than that of RC. Among them, the production of RChemA-ALASG244A and RChemA-ALASS378M is 264% and 238% of RC production, respectively. The above experimental results showed that we screened a series of beneficial mutation sites such as RChemA-ALASG244A and RChemA-ALASS378M, and effectively improved the activity of ALAS through enzymatic modification.

![](https://static.igem.wiki/teams/5303/home/eng5.png)

Fig. 5 RC OD600 at different times with 12 mutants

![](https://static.igem.wiki/teams/5303/results/mut.png)

Fig. 6 Percentage of 5-ALA yield of RC with 12 mutants

Enzyme modification is one of the core technologies of metabolic engineering, which can significantly increase the yield of target products, improve the production process or develop new biotechnology applications by changing the structure and activity of enzymes, providing important support for the biomanufacturing industry. Through this engineering cycle, we realized that enzyme modification is not a simple mutation and screening, but needs to consider a variety of factors, including the strength of the promoter, etc. This experience gave us a deeper understanding of the complexity and challenges of enzyme engineering, and the need for more detailed experimental design and parameter optimization. We believe that in the future, more beneficial mutation sites can be screened through enzymatic modification, so as to further improve the yield of 5-ALA.

Cycle 3: Insert ALAS into genome using a CRISPR-associated transposon system to stabilize expression

### **Design**

After the first cycle of screening and condition optimization, we transferred the 5-aminolevulinate synthetase gene derived from *Rhodobacterium capsulum* into *E. coli* for expression to catalyze the synthesis of C4 pathway 5-ALA. However, during the fermentation, the yield of 5-ALA leveled off or even decreased after 24 h, and it was suspected that the plasmid may be lost after 24 h. Therefore, we performed validation and found that the plasmid loss rate was high for a certain period of time, as high as 55% at 36 h (as shown in Fig. 7), indicating that the genetic stability of the free plasmid is not high.

![](https://static.igem.wiki/teams/5303/results/plasmid-loss.png)

Fig. 7 Plasmid loss verification

In order to improve plasmid genetic stability, we decided to utilize CRISPR-associated transposon (CAST) system [1] to insert the ALAS gene directly into a specific locus in the *E. coli* genome and eliminate the functioning free plasmid. Under the induction of tetracycline, this technology can randomly integrate the ALAS gene into one or more of the eight bypass pathway targets, improving the genetic stability of the ALAS gene while maximizing the carbon flux towards the 5-ALA synthesis pathway.

### **Build**

We constructed a pQCasTns(Ptr)- array 8 plasmid carrying the Tns transposase gene and array sequence (the plasmid map is shown in Fig. 8) and a PtDonor plasmid carrying the ribosomal binding site and *ALAS*, and transferred the two plasmids into the *E. coli BL21 (DE3)* strain to achieve site-directed genomic integration. After the successful integration was verified by experiments, the above two plasmids were eliminated by the pFree plasmid, and then the pFree plasmid was eliminated by sucrose stress [2].

![](https://static.igem.wiki/teams/5303/engineering/eng3.png)

Fig. 8 The left image shows the pQCasTns(Ptr)-array 8 map, and the right image shows the ptDonor plasmid map

### **Test**

Under tetracycline-induced conditions, multiple rounds of integration experiments were performed. Every 24 h of induction, 10 strains were randomly selected, and a total of 40 strains were screened after 48 h. After the plasmid elimination of these 40 integrated strains with pFree plasmid, the successful transposition of the strains was verified by fermentation, and the fermentation results are shown in Fig. 9.

### **Learn**

Among the fermentation results of 40 transposable strains, the yield of strains 2, 3, 8 and 27 was significantly improved, especially No. 2, which was 74% higher than that of the control group, indicating that the expression of ALAS integrated insertion into the genome of *E. coli*  was more stable than that of free expression, and the integration site would also affect the yield of the strains.

![](https://static.igem.wiki/teams/5303/results/cast.png)

Fig. 9 Fermentation results of 40 transposable strains

Cycle 4: Enzyme-constrained model predicts novel targets

### **Design**

The intracellular metabolic activities of microorganisms are complex, and it is difficult to systematically understand their regulatory mechanisms and efficiently obtain the required phenotypes by a single research method. Genome-scale metabolic model (GSMM) is a mathematical model used to describe the relationship between genes, proteins, and responses, and has been widely used to analyze network properties, predict cell phenotypes, guide strain design, and analyze interactions. Based on GSMM, the enzyme-constrained model constructed by integrating large-scale enzyme kinetics and proteomics data has more accurate phenotypic prediction ability.

In our experiments, in order to improve the yield of 5-ALA, we used an enzyme-constrained model to simulate the effects of the following strategies on the target product from a global perspective while enzymatically modifying ALAS, combined with different genetic modification methods: 1) eliminate the competition pathway; 2) strengthen the synthesis of pathway-critical genes; 3) eliminate feedback inhibition; 4) optimize cofactors. This method is helpful to screen and combine different metabolic modification targets and guide experimental design, which not only realizes the rational design of strain modification, improves the efficiency of metabolic engineering, but also comprehensively considers the impact of genetic perturbation on microbial intracellular metabolism, so as to achieve precise metabolic flux regulation. We used the enzyme-constrained model ec\_iECBD\_1354 [3] that had been constructed in *E. coli BL21(DE3)* as the initial model, and since the initial model did not include relevant reactions regarding the synthesis of 5-ALA by the C4 pathway, we introduced the synthesis pathway of 5-ALA by the C4 pathway into the original model.

Succinyl-CoA[c] = >5-Amino-4-oxopentanoate[c]+ Coenzyme A[c] + CO2[c]

In addition, the transport and exchange reactions of 5-ALA are described:

Transport reaction:

5-Amino-4-oxopentanoate[c]=>5-Amino-4-oxopentanoate[e].

Exchange reaction:

5-Amino-4-oxopentanoate[e]=>

And since the transport and exchange reaction of 5-ALA is a reversible reaction, catalyzing the reversible enzymes in two directions corresponding to two different kcat values, the reverse reaction of the transport and exchange reaction of 5-ALA is also introduced into the model.

### **Test**

We will tune the model to identify synthetic targets that can enhance 5-ALA. When comparing the differences in protein requirements between the cell growth stage and the product synthesis stage, 30 proteins were identified as the most demanded proteins that needed to be upregulated. While 10 proteins are classified as having reduced demand and need to be downgraded.

Table. 2 Increased demand for protein

| Gene | Function | Reaction | Changes(%) |
| --- | --- | --- | --- |
| ECBD\_0309 | Aspartate-semialdehyde dehydrogenase | 40.0176 prot\_pool[c] => prot\_A0A140N4Y5[c] | -870.26 |
| ECBD\_0142 | Aldehyde dehydrogenase | 56.3358 prot\_pool[c] => prot\_A0A140N649[c] | -1474.25 |
| ECBD\_0950 | Enolase | 45.6544 prot\_pool[c] => prot\_A0A140N6G0[c] | -131.97 |
| ECBD\_1865 | Glyceraldehyde-3-phosphate dehydrogenase | 35.532 prot\_pool[c] => prot\_A0A140N783[c] | -131.96 |
| ECBD\_2786 | Glucose sorbosone dehydrogenase | 41.1042 prot\_pool[c] => prot\_A0A140N928[c] | -141.22 |
| ECBD\_1284 | Glucokinase | 34.7227 prot\_pool[c] => prot\_A0A140N9C3[c] | -90.52 |
| ECBD\_2932 | Succinate-CoA ligase | 29.7771 prot\_pool[c] => prot\_A0A140N9G2[c] | -4971.47 |
| ECBD\_3227 | Cytochrome bo(3) ubiquinol oxidase subunit 1 | 74.3671 prot\_pool[c] => prot\_A0A140NA88[c] | -111.89 |
| ECBD\_3182 | Adenylate kinase | 23.5858 prot\_pool[c] => prot\_A0A140NAW9[c] | -1505.18 |
| ECBD\_2798 | Fructose-6-phosphate aldolase | 22.9867 prot\_pool[c] => prot\_A0A140NB08[c] | -118.68 |
| ECBD\_2933 | Succinate--CoA ligase [ADP-forming] subunit beta | 41.3922 prot\_pool[c] => prot\_A0A140NBF4[c] | -4971.47 |
| ECBD\_3770 | Short-chain dehydrogenase/reductase SDR | 27.5624 prot\_pool[c] => prot\_A0A140NBP9[c] | -565.15 |
| ECBD\_3228 | Cytochrome bo(3) ubiquinol oxidase subunit 3 | 22.6224 prot\_pool[c] => prot\_A0A140NC92[c] | -111.89 |
| ECBD\_3226 | Ubiquinol oxidase subunit 2 | 34.955 prot\_pool[c] => prot\_A0A140NCS6[c] | -111.89 |
| ECBD\_2667 | Aminotransferase | 43.5729 prot\_pool[c] => prot\_A0A140ND68[c] | -477.41 |
| ECBD\_4298 | ATP synthase subunit alpha | 55.2216 prot\_pool[c] => prot\_A0A140ND72[c] | -108.75 |
| ECBD\_4013 | Aspartokinase | 48.5313 prot\_pool[c] => prot\_A0A140NEC0[c] | -870.23 |
| ECBD\_4105 | Triosephosphate isomerase | 26.9716 prot\_pool[c] => prot\_A0A140NEL5[c] | -125.01 |
| ECBD\_3229 | Cytochrome bo(3) ubiquinol oxidase subunit 4 | 12.0294 prot\_pool[c] => prot\_A0A140NER3[c] | -111.89 |
| ECBD\_4299 | ATP synthase gamma chain | 31.5772 prot\_pool[c] => prot\_A0A140NF41[c] | -108.75 |
| ECBD\_4083 | Bifunctional aspartokinase/homoserine dehydrogenase | 88.9453 prot\_pool[c] => prot\_A0A140NF74[c] | -1021.32 |
| ECBD\_4295 | ATP synthase subunit c | 8.2561 prot\_pool[c] => prot\_A0A140NFR9[c] | -108.75 |
| ECBD\_4297 | ATP synthase subunit delta | 19.3321 prot\_pool[c] => prot\_A0A140NFT6[c] | -108.75 |
| ECBD\_3615 | Homoserine kinase | 33.6094 prot\_pool[c] => prot\_A0A140NFW3[c] | -1097.26 |
| ECBD\_3614 | Threonine synthase | 47.0833 prot\_pool[c] => prot\_A0A140NG09[c] | -1097.27 |
| ECBD\_4294 | ATP synthase subunit a | 30.303 prot\_pool[c] => prot\_A0A140NGC2[c] | -108.75 |
| ECBD\_4300 | ATP synthase subunit beta | 50.3249 prot\_pool[c] => prot\_A0A140NHS0[c] | -108.75 |
| ECBD\_4296 | ATP synthase subunit b | 17.2638 prot\_pool[c] => prot\_A0A140NI98[c] | -108.75 |
| ECBD\_4068 | Phosphoenolpyruvate carboxylase | 99.0759 prot\_pool[c] => prot\_A0A140SS67[c] | -1052.38 |
| ECBD\_4301 | ATP synthase epsilon chain | 15.0681 prot\_pool[c] => prot\_A0A140SSC5[c] | -108.75 |

Table. 3 Declining demand for protein

| Gene | Function | Reaction | Changes% |
| --- | --- | --- | --- |
| ECBD\_0084 | Orotate phosphoribosyltransferase | 23.5667 prot\_pool[c] => prot\_A0A140N4N0[c] | 98.78 |
| ECBD\_0115 | Glutaredoxin | 9.1374 prot\_pool[c] => prot\_A0A140N4R0[c] | 98.78 |
| ECBD\_0907 | Amino-acid acetyltransferase | 49.1948 prot\_pool[c] => prot\_A0A140N6K1[c] | 98.78 |
| ECBD\_0967 | Phosphoadenosine 5'-phosphosulfate reductase | 27.9905 prot\_pool[c] => prot\_A0A140N725[c] | 98.78 |
| ECBD\_0569 | Argininosuccinate synthase | 49.921 prot\_pool[c] => prot\_A0A140N7C3[c] | 98.78 |
| ECBD\_1133 | Serine hydroxymethyltransferase | 45.3161 prot\_pool[c] => prot\_A0A140N8X9[c] | 66.21 |
| ECBD\_2468 | Adenylosuccinate lyase | 51.5693 prot\_pool[c] => prot\_A0A140NCQ6[c] | 98.78 |
| ECBD\_4180 | 3-ketoacyl-CoA thiolase | 40.846 prot\_pool[c] => prot\_A0A140NDQ6[c] | 98.78 |
| ECBD\_3501 | Aconitate hydratase B | 93.4973 prot\_pool[c] => prot\_A0A140NFP9[c] | 98.78 |
| ECBD\_4265 | Ketol-acid reductoisomerase | 54.0685 prot\_pool[c] => prot\_A0A140NI72[c] | 98.78 |

In order to test whether the selected target genes are really related to 5-ALA synthesis, we randomly selected 10 targets to up-regulate, and fermentation was verified.

Table. 4 Upregulated targets

|  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| *adk* | *tsE* | *baE* | *sucC* | *thrB* | *ppc* | *apE* | *sucD* | *asd* | *adE* |

The ZZ-2 strain (transposon group 2 strain, whose 3rd target pta was inserted by the ALAS gene) was used as the chassis cell. For the genes that needed to be up-regulated, we replaced their original promoter and ribosome binding site (RBS) sequence genes with the strong promoter pJ23119 and the more efficient RBS sequence genes. The fermentation results are shown in the figure:

![](https://static.igem.wiki/teams/5303/results/up.png)

Fig. 10 5-ALA yield of ZZ-2 with a target-upregulated engineered strain during fermentation

The fermentation results showed that among the 10 up-regulated targets, the increase in the yield of 5-ALA after the up-regulation of *baE, ppc, apE* and *asd* was the most obvious. At 36 h, the yields of the up-regulated targets above 36 h were 32%, 36%, 30%, and 39% higher than those of the control, respectively, indicating that our model was stable and the above targets were all effective targets.

### **Learn**

In the study of regulating microbial metabolic networks, enzyme -onstrained models provide us with innovative ideas, allowing us to more accurately predict metabolic flux and product synthesis, and significantly shorten the time to find effective targets. However, we are also aware that the modification of certain targets may negatively affect strain growth or 5-ALA yield, suggesting that the current model still needs to be further refined. Through in-depth literature research, we found that genome-scale metabolic models (GSMMs) can be effectively integrated with enzyme kinetics, proteomics, transcriptomics, thermodynamics, and multiomics data. We planned to iteratively upgrade the existing model to enhance its target prediction capabilities.

In addition, there is an urgent need for innovation in the genetic modification of targets. Future experiments should focus on precise regulation of targets, such as applying CRISPR/Cas9 technology for detailed gene editing, or using metabolic regulatory elements to achieve dynamic regulation, rather than relying solely on traditional knockout or overexpression methods. Through this series of improvements, we believe that we can explore more effective targets more efficiently, thereby further increasing the yield of 5-ALA and opening up new possibilities for its synthesis.

Cycle 5: High-throughput screening of high-yielding 5-ALA strains

### **Design**

In order to obtain higher performance strains, we performed ARTP mutagenesis on the modified engineering strains in order to obtain more mutant strains through this method.

Since the efficiency and accuracy of the screening method can have a huge impact on the strain screening process. Therefore, we designed a high-throughput screening technology based on droplet microfluidic chips. By exploring a suitable water-in-oil system, the modified strain was encapsulateed in droplets.

### **Build**

In this study, we constructed a self-made droplet generation chip based on glass capillaries, and used the T-channel method to screen the droplets formed by mineral oil + surfactant Span80 as the outer phase.

![](https://static.igem.wiki/teams/5303/engineering/e14.jpg)

Fig.11 Glass capillary generation chip

### **Test**

When the self-made chip and mineral oil system were tested, it was found that the generated droplets were emulsified and had a large diameter, which was not conducive to the growth and screening of bacteria.

![](https://static.igem.wiki/teams/5303/engineering/e15.png)

Fig.12 Droplets generated by mineral oil

### **Learn**

Through literature review, we realized that the type of phase and the amount of surfactant in it have a great influence on the state of the droplet after formation, and the droplet diameter is closely related to the diameter of the chip pipe and the flow rate of the inner and outer phases. We decided to redesign the project based on these circumstances.

### **Design**

We decided to study the state of droplets with different types and amounts of surfactants in multiple phases and the same phase to screen out the optimal generation system. At the same time, since 5-ALA is a protoporphyrin precursor, we hope to take advantage of the characteristics of protoporphyrin that can produce fluorescent signal at the excitation wavelength of 408 nm to screen the droplets for fluorescence signal intensity, so as to screen out the strains with high production of 5-ALA.

### **Build**

We designed a PDMS chip using the coaxial flow focusing method, planned to control the droplet size by reducing the diameter of the chip pipe and adjusting the flow rate of the inner and outer phases, and constructed a variety of droplet external phase systems: mineral oil + Span80 system; Paraffin oil + EM90 system; Fluorinated oil + fluorinated surfactant system and other droplets are generated.

![](https://static.igem.wiki/teams/5303/engineering/eng2.png)

Fig.13 PDMS generates chip design drawings and physical drawings

### **Test**

We completed the exploration of the above system under the PDMS chip, and found that the droplets formed by the fluorinated oil + 1% 008-FluoroSurfactant system in the PDMS chip were the most stable and suitable size, which met the screening conditions.

![](https://static.igem.wiki/teams/5303/engineering/e18.png)

Fig.14 Droplets generated by fluorinated oils

### **Test**

We used the excitation wavelength of 408 nm to detect the fluorescence signal intensity of protoporphyrin produced by *E. coli*, so as to sort the strains with strong fluorescence signal and high yield of 5-ALA. However, due to the lack of 408 nm fluorescence screening equipment, the weak fluorescence signal replaced by other excitation wavelengths, and the proportional relationship between protoporphyrin and the target product 5-ALA cannot be effectively verified by experiments, so the experimental design of fluorescence screening is difficult to carry out.

### **Learn**

Through further literature review, we found that 5-ALA is a raw material for the synthesis of heme, and heme is a substance necessary for the growth of *E. coli*, so the yield of 5-ALA is related to the growth state of *E. coli* and the biomass of *E. coli* (Fig. 15). Based on this information, we decided to redesign the droplet screening method.

![](https://static.igem.wiki/teams/5303/engineering/e19.png)

Fig.15 Validation of the relationship between *E. coli* biomass (OD value) and 5-ALA yield

### **Design**

We plan to use the fluorinated oil + 1% 008-FluoroSurfactant system to calculate the concentration of the bacterial solution required to achieve the single-encapsulation condition of droplets through Poisson distribution, and use absorbance to detect the biomass of *E. coli*, so as to screen out the strains with better growth state.

### **Test**

We used the characteristic absorption peak of *E. coli* at 600 nm to biomass screen the formed droplets. By distinguishing the biomass OD size of the droplets, the strains with faster growth rates under the same conditions were sorted, and the screened droplets were broken up and coated on the plate to facilitate subsequent fermentation.

![](https://static.igem.wiki/teams/5303/engineering/gif.gif)

Fig.16 Droplet sorting diagram

Finally, we decided to use fluorinated oil + 1% 008-FluoroSurfactant as the external phase system, based on the PDMS chip, to achieve single encapsulation of strains, and use absorbance equipment and dielectrophoresis chip to complete the high-throughput screening of biomass of high-yielding strains.

Reference

1. Strecker, J., et al., RNA-guided DNA insertion with CRISPR-associated transposases. Science, 2019. 365(6448): p. 48-53.

2. Lauritsen, I., et al., A versatile one-step CRISPR-Cas9 based approach to plasmid-curing. Microb Cell Fact, 2017. 16(1):135.

3. Zhang, Z.X., et al., Developing a dynamic equilibrium system in *Escherichia coli* to improve the production of recombinant proteins. Appl Microbiol Biotechnol, 2022. 106(18): 6125-6137.

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{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
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**Configuration of Media and Reagents**

**Basic Experimental Operations**

**Droplet Microfluidic**

Configuration of Culture Medium and Reagents

![](https://static.igem.wiki/teams/5303/experiments/experiment1.png)

Basic Molecular Biology Operations:

![](https://static.igem.wiki/teams/5303/experiments/experiment2.png)

![](https://static.igem.wiki/teams/5303/experiments/experiment3.png)

![](https://static.igem.wiki/teams/5303/experiments/experiment4.png)

Droplet Microfluidic High-Throughput Screening

![](https://static.igem.wiki/teams/5303/experiments/experiment5.png)

![](https://static.igem.wiki/teams/5303/experiments/experiment6-2.png)

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{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block lead %}You can describe the results of your project and your future plans here.{% endblock %}
{% block page\_content %}

**Overview**

**Enzyme Modification**

**Model**

**High-Throughput Screening**

**Discussion**

Overview

Our project is dedicated to the efficient synthesis of 5-Aminolevulinic acid (5-ALA) by metabolic engineering of *E. coli.* Firstly, we conducted an in-depth study on the stability and activity of 5-Aminolevulinate synthase (ALAS), and effectively improved the activity and genetic stability of ALAS by screening different sources of ALAS, enzyme modification, and integrating ALAS into the genome by using CRISPR-associated transposons (CASTs) system. Meanwhile, we predicted the relevant targets in the 5-ALA synthesis pathway by using enzyme-constrained model and up-regulated the expression of some genes to enhance 5-ALA synthesis. In addition, we established an efficient screening method for high-yielding strains with the help of ARTP mutagenesis and droplet microfluidics.

![](https://static.igem.wiki/teams/5303/results/fig1.png)

Fig.1 Project introduction

Enzyme Modification and CRISPR-Associated Transposons (CASTs) System

Through literature research, we selected genes that can effectively express ALAS (*RChemA-ALAS, AFhemA-ALAS, RPhemA-ALAS*) from *Rhodobacter capsulatus*, *Agrobacterium*, and *Rhodopseudomonas palustris* and respectively loaded them into *E. coli* PET-24a plasmid vector, which was introduced into *E. coli BL21(DE3)* for expression(Hereinafter referred to as Af, Rc and Rp). *E. coli BL21 (DE3)*  was used as a control, and the fermentation results are shown in Fig.2.

![](https://static.igem.wiki/teams/5303/results/od.png)

Fig.2 Fermentation levels corresponding to ALAS genes from different sources

Among them, *E. coli* introduced with *RChemA-ALAS* showed relatively higher 5-ALA production, and we decided to adopt this gene for the next experiments. The results showed that the growth rate of the strain slowed down after 24 h, and the 5-ALA production of each group leveled off after 24 h. At this time, the average 5-ALA yield of *E. coli BL21 (DE3)* was only 0.05 g/L, whereas the average 5-ALA yields of Af, Rc, and Rp imported with *ALAS* genes from  *Agrobacterium, Rhodobacter capsulatus* , and  *Rhodopseudomonas palustris* amounted to 1.45, 1.67, and 1.55 g/L, were 280%, 324% and 300% of the control group and showed higher activity.

In order to further improve the activity of ALAS and promote the production of 5-ALA, we decided to carry out enzyme modification. We predicted a series of mutations by artificial intelligence and composed them into plasmids for fermentation validation using *E. coli* introduced with unmutated *RChemA-ALAS* (hereafter RC) as a control.

Table 1 Predicted 12 mutation sites

|  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
| RChemA-ALASG58A | RChemA-ALASG60A | RChemA-ALASS139E | RChemA-ALASA218I | RChemA-ALASG244A | RChemA-ALAST245Q |
| RChemA-ALASV252W | RChemA-ALASI292A | RChemA-ALASS378M | RChemA-ALASM393L | RChemA-ALASK13R | RChemA-ALASI203L |

The fermentation results are shown in figures (Fig.2, Fig.3). As can be seen from the Fig.2, the value of OD600 increased steadily in each group without any major difference.

![](https://static.igem.wiki/teams/5303/results/fig3.png)

Fig.3 OD600 of the mutants and RC after fermentation

Fermentation yield measurements showed that the 5-ALA yield stabilized after 24 h. The results of 24 h fermentation are shown in Fig.4. We found that the 5-ALA yields of mutants RChemA-ALASG58A, RChemA-ALASG60A, RChemA-ALASS139E, RChemA-ALASG244A, RChemA-ALASI292A, RChemA-ALASS378M, and RChemA-ALASM393L were significantly higher than those of RC. Among them, the yields of RChemA-ALASG244A and RChemA-ALASS378M were 264% and 238% of those of RC, respectively. The above experimental results indicated that we screened a series of beneficial mutation sites, such as RChemA-ALASG244A and RChemA-ALASS378M, and through enzyme modification, we effectively improved the activity of ALAS.

![](https://static.igem.wiki/teams/5303/results/mut.png)

Fig.4 5-ALA yield of the mutants compared with RC after fermentation

During the fermentation validation process, we found that the 5-ALA production tended to be constant or even decreased after 24 h of fermentation. We suspected that the plasmid expression level decreased after a certain time of fermentation for some reasons, and the most likely scenario was that the free plasmids were discarded over time. To test whether this hypothesis was valid, we performed plasmid elimination experiments and found that the plasmid loss rate was as high as 55% after 36 h of bacterial incubation (Fig.5). Therefore, improving the stability of plasmid expression is crucial for improving the efficiency of enzyme expression.

![](https://static.igem.wiki/teams/5303/r100.png)

Fig.5 Plasmid loss rate versus time

To improve the genetic stability of the ALAS expression plasmid in *E. coli* , we used the CRISPR-associated transposons (CASTs) system to introduce *ALAS* gene fragments into the *E. coli*  genome.CRISPR-associated transposons system is a special class of mobile genetic elements that combines the RNA guidance properties of the CRISPR system with the DNA mobility of transposons. CASTs enable precise insertion of large segments of DNA at specific sites in the genome without creating DNA double-strand breaks. The system contains the pQCasTns plasmid with targeting and transposition functions and the ptDonor plasmid with *ALAS* fragments. After the CRISPR effector complex recognizing and binding to the array target sequences, the N20 guides transposase Tns to achieve targeted insertion of *ALAS*. The origin, characterization and mapping of the two plasmids are shown below:

pQCasTns(Ptr)-array8 plasmid: It corresponds to the target fragment targeting sequences. Sequence synthesis and cleavage enzyme linkage sites can be expressed as [NcoI-“Repeat Sequence 1 + Guide Sequence + Repeat Sequence 2”+ “”+ BamHI], and the screening marker is kanamycin resistance (kan).

pDonor plasmid:Gibson assembly, screening labeled for chloramphenicol resistance (cm).

![](https://static.igem.wiki/teams/5303/results/fig6.png)

Fig.6 Plasmid mapping of pQCasTns: TetR (Tetracycline Repressor) protein can bind tightly to the manipulated sequences and prevent the formation of the transcription start complex, thus repressing the expression of the tetracycline gene; the spacer is the targetarray sequence, which can be specifically recognized by the N20 sequence.

![](https://static.igem.wiki/teams/5303/results/fig7.png)

Fig.7 ptDonor plasmid mapping

We used electroporation transfection to integrate the above two plasmids, pQCasTns and ptDonor, into *E. coli* for in vivo expression (the medium was supplemented with kan and cm), and at the same time, in order to improve the expression efficiency of the transposable elements, we used tetracycline resistance for multiple rounds of induction of the plasmids. After successful transposition, in order to further ensure the integration of *ALAS* gene into the *E. coli* genome to function stably and at the same time to avoid the resource competition of the newly introduced plasmids to other functional genes, we used pFree plasmid to discard the two free plasmids.

The strains that had been successfully transposed were subjected to fermentation. The results of the transposition fermentation are shown below, including 40 strains, of which RC was the strain that did not undergo transposition and served as the control group. Yield stabilized after 36 h. Several strains, such as 2, 3, and 4, had significantly higher yields than RC. Among the 40 transposed strains, strain 2 had the highest yield, which was 74% higher than that of the control group. The yields of other transposable strains were high and low, indicating that the insertion target site has a great influence on the yield of 5-ALA. This suggested that *ALAS* integrally inserted into the *E. coli* genome expression is more stable than free expression.

![](https://static.igem.wiki/teams/5303/results/cast.png )

Fig. 8 Fermentation results of 40 transposable strains(RC as a control)

Enzyme-Constrained Model

Microbial intracellular metabolic activities are of high complexity, and a single research tool is difficult to systematically understand their regulatory mechanisms and cannot efficiently obtain the desired phenotypes. Genome-scale metabolic model (GSMM) is a mathematical model used to characterize gene-protein-response (GPR) relationships, which has been widely used to analyze network properties, predict cellular phenotypes, guide strain design and analyze interactions. The enzyme-constrained model constructed by integrating large-scale enzyme kinetics and proteomics data based on GSMM has more accurate phenotype prediction ability. In our experiments, in order to improve the yield of 5-ALA, we enzymatically modified ALAS. We hope that using the enzyme-constrained model, combined with different genetic modification methods, we can simulate the effects of modification strategies such as 1) elimination of competing pathways; 2) reinforcement of key genes of the synthetic pathway; 3) elimination of feedback inhibition; 4) optimization of cofactor, etc. on the target product at a global level, to help us screen out different metabolic transformation targets and combine them for guiding our experiments. This approach not only realizes the rational design of strain transformation and improves the efficiency of metabolic engineering transformation, but also can consider the effect of genetic perturbation on the whole microbial intracellular metabolism at the global level, which can help to realize the precise regulation of metabolic flow.

Table.2 Enzyme-constrained models versus traditional genome-scale metabolic models

| Characteristics | Enzyme-constrained models | Genome-scale metabolic models |
| --- | --- | --- |
| Enzyme kinetics considerations | Considering enzyme concentration and catalytic efficiency to provide more realistic reaction dynamics | Usually ignoreing enzyme kinetics and relying on metabolic homeostasis |
| Predictive accuracy | Enabling more precise prediction of metabolic flow and product generation | Lower precision, limited by assumptions and static modeling |
| Response to environmental change | Flexibility to model the effects of environmental changes on metabolism | Weak response to environmental change |
| Model complexity | More sophisticated, capable of capturing subtle biochemical processes | Simpler, easier to build and understand |
| Scope of application | Ideal for studies requiring detailed metabolic regulation | Suitable for large-scale genome analysis |
| Data requirements | Detailed enzyme kinetic data required | Primary reliance on genomic and metabolic pathway information |

To evaluate the effectiveness of target genes, We selected ten of the predicted targets for upregulation. The experiment used ZZ-2 strain (transposome 2 strain) as chassis cells. For the genes to be up-regulated, we used the strong promoter pJ23119 for overexpression, together with an efficient ribsome binding site (RBS) sequence.

![](https://static.igem.wiki/teams/5303/r101.png)

Fig.9 Map of targets to be up-regulated

Subsequently, we conducted fermentation experiments with these 10 engineered strains using ZZ-2 strain as a control group. We verified the effect of these gene regulation strategies by OD600 measurement and 5-ALA yield measurement during the fermentation process. The yield stabilized after 36 h and the fermentation results are shown in Fig.10.

![](https://static.igem.wiki/teams/5303/results/up.png)

Fig.10 5-ALA production at 36 h in engineered strains of ZZ-2 with target up-regulation

Table.3 Up-regulated genes corresponding to expression products

| Abridge | Expression product |
| --- | --- |
| *adk* | Adenylate kinase |
| *tsE* | Threonine synthase |
| *baE* | Bifunctional aspartate kinase/homoserine dehydrogenase |
| *sucC* | Succinate coenzyme A synthetase beta subunit |
| *thrB* | Homoserine kinase |
| *ppc* | Phosphoenolpyruvate carboxylase |
| *apE* | Aspartate kinase |
| *thrS* | Threonine-tRNA synthetase |
| *sucD* | Succinate coenzyme A synthetase alpha subunit |
| *asd* | Aspartate-semialdehyde dehydrogenase |

The fermentation results showed that among the 10 up-regulated targets, *baE, ppc, apE* and *asd* were better up-regulated. The yields corresponding to the above up-regulated targets were 32%, 36%, 30% and 39% higher than the control at 36 h, respectively, indicating that our model was stable and all the above targets were effective.

These targets are inextricably linked to aspartate and the TCA cycle. Among them, *baE, apE,*  and  asd  express bifunctional aspartate kinase/homoserine dehydrogenase, aspartate kinase, and aspartate-semialdehyde dehydrogenase respectively, which are key enzymes of the amino acid metabolism pathway in microorganisms and can facilitate glycine synthesis. And *ppc* expresses phosphoenolpyruvate carboxylase, which catalyzes the reaction between phosphoenolpyruvate and carbon dioxide in the TCA cycle to produce oxaloacetate, in which phosphoenolpyruvate reacts with carbon dioxide to form oxaloacetate. Up-regulation of these genes may further drive the TCA cycle, facilitating the production of 5-ALA raw materials such as succinyl coenzyme A, which in turn drives the synthesis of 5-ALA.

Droplet Microfluidic High-Throughput Screening

After completing the modification of the *E. coli* strain, we designed a high-throughput screening technique based on microfluidic chip. By screening the suitable oil-in-water system, the modified strain was encapsulated in the oil phase. And we tried to utilize the fluorescence signal screening and biomass screening to realize the screening of high yielding strains.

### 1.Exploration of the optimal ARTP mutagenesis time in *E. coli*

In order to obtain more mutations, we performed ARTP mutagenesis on  *E. coli* strains that had been transformed. By setting a time gradient and observing it after coated plate incubation, and comparing the growth of *E. coli* under each time gradient according to the standard observation that a lethality rate of 95% or more is the success of mutagenesis (Fig.11), we set 70 s as the optimal ARTP mutagenesis time for *E. coli* .

![](https://static.igem.wiki/teams/5303/results/fig11.jpg)

Fig.11 Growth of *E. coli* at different mutagenic time

### 2.Exploration of droplet encapsulating system

Droplets are formed by interfacial tension and shear force between oil and water phases, and surfactant is introduced to keep stability between droplets and prevent agglomeration, in addition, the flow rate will affect the size of droplet generation, so the oil phase, surfactant and flow rate are critical to the formation of droplets.

To maintain droplet stability, prevent droplet fusion and support prolonged incubation, we explored the effects of different oil phases and surfactants on droplets (Table 3). We tried a variety of systems as external phases to observe the effect of droplet generation, and we found that the droplets formed by fluorinated oil with 1% 008- FluoroSurfactant in the PDMS chip were the most stable, homogeneous, and did not emulsify easily during incubation, which was the most compatible with the conditions for droplet generation.

Table.4 Experimental system of inquiry and corresponding results

| External phases | Surfactant | Results |
| --- | --- | --- |
| Soybean Oil | / | Droplet fusion |
| 8%F108 | Droplet fusion |
| 2%Span80 | Droplets emulsify after 19 h |
| 3%Span80 | Droplets poorly homogenized, emulsified after 4 h |
| 2%EM90 | Droplets fused easily |
| 3%EM90 | Droplet homogeneity is good, long time without fusion and emulsification, difficult for bacteria to grow |
| ETPTA | / |
| Soybean Oil | / | Droplet fusion |
| 1%Span80 | Droplets fused easily |
| 2%Span80 | Droplets do not fuse easily, emulsify after 19 h || 3%Span80 | Droplets not easily fused, emulsified after 10 h |
| 4%Span80 | Droplets do not fuse easily, emulsified after 4 h |
| 5%Span80 | Droplets do not readily fuse, emulsified after 2 h |
| 2-Methyl Silicone Oil | 2%EM90 | Droplet fusion |
| 3%EM90 | Droplet fusion |
| Paraffin Oil | 3%EM90 | Droplet homogeneity is good, no fusion and emulsification for a long time, difficult for bacteria to grow. |
| Fluorinated oil | **1%008- FluoroSurfactant** | Good droplet homogeneity, no fusion and emulsification for a long time, good growth of bacteria |

The size of the droplets is closely related to the initial fluid velocity, the diameter of the droplet generation chip, and the properties of the inner and outer phase solutions, with the fluid velocity having the most significant impact. Based on literature and droplet generation size, we tested the effects of different flow rates on droplet diameter and found that droplet diameter is inversely proportional to the outer phase flow rate and directly proportional to the inner phase flow rate. Through experimentation, we determined that droplets with a diameter of approximately 90 μm are most suitable for subsequent screening of *E. coli* . Therefore, we ultimately selected an inner phase flow rate of 1 mL/h and an outer phase flow rate of 7 mL/h as the conditions for subsequent experiments.

The initial concentration of *E. coli* added to the internal phase solution and the rate of single encapsulateing were calculated according to the Poisson distribution formula. The Poisson distribution formula is given below:

![](https://static.igem.wiki/teams/5303/results/droplet.png)

(K denotes the number of cells contained in a single droplet; λ denotes the average number of cells contained in each droplet.)

The average diameter of the droplet: 90μm, then the droplet volume: V=4/3π\*(90)3=3053628 μm3, which can be calculated that there are 3.27\*105 droplets in 1 mL;

Let λ=0.4 and λ=(C/3.27\*105), then the initial concentration of *E. coli*  C=130992 droplets/mL;

Let K=1, then the droplet single encapsulating rate P=0.268.

### 3.Droplet generation and incubation

We generated droplets with a diameter size of about 90 μm (Fig.12) using a fluorinated oil system containing 1% 008- FluoroSurfactant at an internal-phase flow rate of 1 mL/h and an external-phase flow rate of 7 mL/h, and collected the droplets into the culture medium.

![](https://static.igem.wiki/teams/5303/results/gif.gif)

Fig.12 Real-time generation of dynamic maps of liquid droplets

The generated droplets were incubated in a 37°C incubator and sampled regularly and placed under a microscope. A significant increase in the number of bacteria within the droplets was observed, indicating that *E. coli* was growing well within the droplets (Fig.13). Subsequent screening was carried out when cultured to the logarithmic growth phase of *E. coli*.

![](https://static.igem.wiki/teams/5303/results/fig14.png)

Fig.13 Droplet generation morphology (left) Droplet incubation 24 h morphology (right)

### 4.Droplet screening and product determination

#### (1)Fluorescence screening

5-ALA is a precursor to protoporphyrin, and protoporphyrin exhibits a specific fluorescence signal under an excitation wavelength of 408 nm. Therefore, the fluorescence signal detected from the strain is positively correlated with its ability to produce 5-ALA. We aimed to measure the fluorescence signal intensity of protoporphyrin produced by *E. coli* using a 408 nm excitation wavelength, thereby enabling screening. Initially, we observed the products in droplets containing *E. coli* using a fluorescence microscope (Fig.14). However, due to the lack of a fluorescence screening device with a 408 nm wavelength, the fluorescence signal was unstable, and using other excitation wavelengths resulted in weaker fluorescence signals. Additionally, the proportional relationship between protoporphyrin and the target product 5-ALA could not be effectively verified through experiments. Factors above led to less than ideal fluorescence screening results.

![](https://static.igem.wiki/teams/5303/results/fig15.png)

Fig.14 Fluorescence signal observation diagram of a droplet

#### (2)Biomass screening

In the case of unsatisfactory fluorescence screening scheme, through literature review we found that 5-ALA is a raw material for the synthesis of heme, which is a substance necessary for the growth of *E. coli*, therefore, the high yield of 5-ALA is related to the growth status with *E. coli*.

Since the *gltx* gene in *E. coli* is the key gene for the synthesis of 5-ALA in the C5 pathway, we first constructed a plasmid and introduced it into *E. coli* to inhibit the expression of the *gltx* gene, and found that the growth of the strain was significantly inhibited. When we introduced ALAS with different activities into it, the growth status of *E. coli* was somewhat improved as seen by the OD600 data: the growth of *E. coli* was positively correlated with the production of 5-ALA. (Fig. 15) Therefore, we utilized this relationship and chassis with low activity ALAS and growth inhibition, and chose to use absorbance to detect the growth status of *E. coli*  for screening.

![](https://static.igem.wiki/teams/5303/results/fig16.png)

Fig.15 Validation of the relationship between *E. coli* biomass (OD) and 5-ALA production

The first prerequisite for screening high-yielding 5-ALA strains using AADS is to ensure that a single strain is encapsulated in each droplet. However, the encapsulation of cells in a single droplet is not uniform but follows a Poisson distribution, meaning that many droplets are empty while others may encapsulate multiple cells. Therefore, before the experiment we controlled the concentration of *E. coli* at 1.3x105 cells/mL (λ=0.4) to achieve as many single encapsulations as possible.

The AADS technique utilizes absorption spectroscopy combined with microfluidics to differentiate the biomass OD size of droplets by measuring their absorption intensity of 600 nm light, thus sorting out droplets with faster *E. coli* growth (Fig.16).

Using a dielectrophoresis chip, we successfully sorted over 1000 positive droplets. After randomly selecting 20 droplets from the sorted batch and demulsifying them, we spread their contents on agar plates for cultivation. Once colonies grew on the plates, we conducted shake flask fermentation and then measured their 5-ALA production. The results showed that four strains (A3, A8, A15, A17) exhibited increased enzyme activity to varying degrees, with the highest enzyme activity increasing to 126.30% (Fig.17).

![](https://static.igem.wiki/teams/5303/results/droplet-gif.gif)

Fig.16 Droplet Sorting Video

![](https://static.igem.wiki/teams/5303/results/20t.png)

Fig.17 Screening strain 5-ALA yield validation plot

In summary, we ultimately decided to screen for high-yield strains by leveraging the differences in bacterial biomass. Through our experimental investigation, using fluorinated oil with 1% fluorinated surfactant as the outer phase system and a PDMS chip as the base, we successfully generated stable droplets with a diameter of approximately 90 nm, achieving a screening throughput of millions per hour. Subsequently, we performed online biomass detection using OD600 as the absorbance indicator and utilized a dielectrophoresis chip for droplet sorting.

From the screened positive droplets, we randomly selected 20 for fermentation validation. The final results indicated that we successfully screened four strains capable of high 5-ALA production, with the highest enzyme activity increased to 126.30%.

Discussion

5-ALA is a sophisticated and sustainable biopesticide, especially when compared with the various highly toxic and non-degradable chemical pesticides currently in use. The exploration of the biological manufacturing of 5-ALA in recent years has driven our research. However, issues such as the stability of *ALAS* gene expression, the obsolescence of targets, and the low efficiency of screening for target strains still persist. This project addresses these three issues by utilizing CRISPR-associated transposons system, enzyme-constrained model, and droplet microfluidic high-throughput screening technologies to explore solutions.

By combining enzyme modification techniques, we have obtained some beneficial mutants (RChemA-ALASG258A, RChemA-ALASG260A, RChemA-ALASG244A, etc.) that can enhance the activity of the ALAS enzyme, significantly increasing the production of 5-ALA. Compared to traditional plasmid expression, CRISPR-associated transposons system represents an advanced engineering strain modification technology. It directly introduces the *ALAS* gene into the genome of the engineering strain, greatly improving the stability of gene expression and reducing the impact of plasmid loss on the genetic stability of gene expression. We hope that our approaches and achievements will inspire further exploration in this research field and provide additional guidance.

By utilizing the high-throughput screening method of droplet microfluidics, we have completed the screening of target strains more efficiently and accurately, reducing the time cost for the laboratory production of the biopesticide 5-ALA, which is beneficial for future research and promotion. Additionally, as a universal and efficient screening method, our experiment also provides a new idea for other teams to achieve efficient strain screening. We hope that our experimental results can offer some assistance to other teams in their experimental design.

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