iGEM 2022 — Team puiching-macau

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{% 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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## Protocols

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[Our protocols for experiments](https://static.igem.wiki/teams/4340/wiki/protocols-2022-v-1.pdf)

## April

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|  |  |
| --- | --- |
| Week 1 (0427-0429)  1. Prepare competent BL21 cells with calcium chloride 2. Make LB plates 3. Transformation of GFP 4. Pick colony | Detail  1. Make LB broth (400 ml) and LB agar (400 ml) 2. GFP and RFP transformation practices ![](https://static.igem.wiki/teams/4340/wiki/notebook1.png) 3. Cell preparation (BL21) competent cell using CaCl2 4. Make LB agar plate 5. Transformation of GFP 6. Pick colony (concentration: 1000, 500, 100) 7. Incubate at PuiChing: cover the tube with the cap at second stop (fully covered, tight), use parafilm to pack the tubes. Make sure that all tubes are vertical. |

## May

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|  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
| Week 2 (0504-0507)  1. Check GFP and OD for growth curve 2. Pick colony of GFP || Week 3 (0514-0521)  1. Check hydroponic pH and conductivity 2. Check OD and GFP for growth curve 3. Renew nutrient solution of hydroponic system | Detail   ![](https://static.igem.wiki/teams/4340/wiki/notebook2.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook3.png) || Week 4 (0523-0528)  1. Check hydroponic pH and conductivity 2. make plates 3. Transformation of GFP 4. pick colony of GFP 5. glycerol stock of GFP |
| Week 5 (0530-0601)  1. Check hydroponic pH and conductivity 2. Make LB agar and broth, check ABS and FL for growth curve | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook4.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook5.png) |

## June

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|  |  |
| --- | --- |
| Week 6 (0609-0610)  1. Transformation of pH shooting (twice) 2. Make LB agar 3. Spread BL21 plates | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook7.png) |
| Week 7 (0617)  1. Transformation of glsA, pH shooting and sf-gfp result: only glsA has colonies | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook9.png) |

## August

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|  |  |
| --- | --- |
| Week 8 (0803-0805)  1. Primer design for PYL8 and T2R4 2. Make LB agar, broth, and plates 3. Spread BL21 cells 4. Pick three colonies, and prepare competent cells with calcium chloride | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook10.png) |
| Week 9 (0808-0812)  1. Transformation of glsA, K, pH shooting and control 2. result: only glsA and control had colonies, so we will midi K and pH shooting 3. Pick colony and culture glsA and control 4. Make pH 4, 5, 6, 7 LB broth for functional test 5. 1st time glsA and control functional test 6. Transformation of K and pH shooting with DH5a cells 7. Pick colony and culture 8. Primer design for glsA and ldhA | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook11.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook12.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook13.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook14.png) |
| Week 10 (0815-0819)  1. 2nd time glsA and control functional test: measure pH and OD 2. result: likely contaminated so we do control again 3. Midi prep of K and pH shooting 4. Transformation of glsA, K, pH shooting, and control 5. Pick colony and culture control 6. Control functional test | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook15.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook16.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook17.png) |
| Week 11 (0822-0826)  1. Make pH 8, 9, 9.5 LB broth, autoclave for functional tests 2. culture control, pH shooting, spread glsA and K plate 3. functional test of pH shooting and control: measure pH, OD and FL 4. Pick colony and culture glsA and K 5. functional test of K and glsA | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook18.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook19.png) |
 Week 12 (0829-0901)  1. Make pH 5, 6, 7 LB broth, autoclave for functional tests 2. functional test of pH shooting and control: measure pH, OD and FL 3. spread glycerol stock cells (pH shooting and control) 4. Pick colony and culture |

## September

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|  |  |
| --- | --- |
| Week 13 (0905-0908)  1. Western Blot of pHS and glsA 2. results: no visible bands, too little sample 3. pick colony 4. mini prep PYL8 and T2R4 5. measure growth of soybean and water spinach in different pH nutrient solution | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook20.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook22.png) |
| Week 14 (0914-0918)  1. Design and start functional tests for hormone system 2. measure growth of soybean and water spinach in different pH nutrient solution 3. Redo pH system western blot with 20 microliter samples 4. result: success 5. Prepare cDNA for RT-qPCR for PYL8 and T2R4 genes | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook23.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook24.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook25.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook26.png)  ![](https://static.igem.wiki/teams/4340/wiki/notebook27.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook28.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook29.png) |
| Week 15 (0920-0924)  1. RT-qPCR glsA and ldhA 2. Culture and extract protein for Western blot of PYL8 and NSP4-T2R4 3. Transformation of new pH system vectors using BL21 and DH5a cells (low efficiency as it is not IPTG induced) 4. Pick colony (used wrong antibiotics) | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook30.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook31.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook32.png) |
| Week 16 (0926-0928)  1. RT-qPCR for PYL8 and T2R4 2. results:  1. PYL8: 1.33 fold change against BL21 2. T2R4: 1.60 fold change against BL21  3. Transformation of new pSB1C3 vectors 4. Western blot for PYL8 and NSP4-T2R4 5. result: Obvious band for PYL8, no band for NSP4-T2R4 6. Start functional tests of new pSB1C3 vectors using raspberry pi and cloud system | Detail ![](https://static.igem.wiki/teams/4340/wiki/notebook34.png) ![](https://static.igem.wiki/teams/4340/wiki/notebook35.png)  ![](https://static.igem.wiki/teams/4340/wiki/notebook36.png)  ![](https://static.igem.wiki/teams/4340/wiki/notebook37.png)  ![](https://static.igem.wiki/teams/4340/wiki/notebook38.png) |

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{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a part of your project by going through at least one iteration of the engineering design cycle.{% endblock %}
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## Introduction

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Our team has been focusing on food issues. Food is a basic necessity, however, COVID-19 and other human conflicts have brought devastating consequences. With the collapse in demand from restaurants, hotels, and catering, the closure of open markets, but a surge in demand from supermarkets during COVID-19, over 828 million people are suffering from hunger. It is painfully clear that we need a more sustainable and efficient farming system. Our team therefore set a target to improve current hydroponics and aim to establish a sustainable hydroponic agriculture system in the near future.

### Hardware

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#### Please click the buttons below to show detailed information:

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Research
Brainstorm & Design
Build & Test (Phase I)
Learn & Improve (Phase I)
Build & Test (Phase II)
Learn & Improve (Phase II)
Build & Test (Phase III)
Learn & Improve (Phase III)

### Research

---

#### Hydroponics system

Advantages

* Greater plant density- plants can be moved as they grow. The use of a growth room for germination and seedling production and the spacing of certain crops in the greenhouse decreases the average area needed per plant over conventional soil production.
* Higher yields- Reports of higher yields and better quality are common although equal yields should be able to be obtained from a conventional cropping system.
* Less water consumption- In methods where the root system is contained in a closed trough or tube, less evaporation occurs and water consumption is reduced.

#### Problem

However, the costs of hydroponics are high and it is unsustainable. In addition, since the capacity of the machine is large, an excessive amount of nutrition liquid is required to meet the concentration we want. Our experimental site is the school laboratory rather than a large laboratory, both space and resources will be less than in other laboratories which means that it is hard to produce such a large amount of the nutrition liquid.

#### Overall

Growth period: 55 to 60 days

Germination: 1 to 2 weeks

Temperature:

* Optimum temperature for germination: 20∼22°C
* Suitable temperature for growth: 20～30℃
* Sunlight time: 8 to 12 hours (the longer the dark period, the earlier the flowering)
* Optimum soil pH: 6.5 to 7.5

### Brainstorm & Design

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We would like to nvestigate possible methods to further reduce water usage/ waste in hydroponics.

Our team believes hydroponics to be a promising solution. However, since the capacity and the size of the hydroponics is large, an excessive amount of nutrition liquid is required. This will increase the costs of using hydroponics and it is less sustainable. Therefore, we came up with an idea to enhance urban hydroponics systems by redesigning the hydroponics.

![](https://static.igem.wiki/teams/4340/wiki/engineering-12.jpg)

Photo 1. Using the hydroponic machine as apparatus

Furthermore our experimental site is the school laboratory rather than a large laboratory, so both space and resources will be less than in other laboratories. This means that it is hard to place large hydroponics and produce such a large amount of the nutrition solution. This direction of improvement would allow us to finish our experiment more easily.

### Build & Test (Phase I)

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To fulfill our needs, we, therefore, try to use a smaller container, e.g. a beaker, to investigate if this, and the limited solution would eliminate the plant seeding. In our testing, the concentration of nutrient solution we aimed to have is 0.1 M. We used the hydroponic machine and beakers as apparatus and kept the same concentration. The table below illustrates that using the hydroponic machine requires 1 L of the nutrient solution and 10 L of water to mix up the targeted culture medium.Using the beaker as the vessel only requires 0.5 L of the nutrient solution and 5 L of water to get a 0.1M of concentration of the culture medium.

To stabilize the plant in the beaker, we used wooden sticks, as shown in Photo 2.

![](https://static.igem.wiki/teams/4340/wiki/engineering-13.jpg)

Photo 2. Using a beaker as apparatus

| Vessels/Required Material | Nutrient Liquid | Water |
| --- | --- | --- |
| Hydroponic Machine | 1L | 10L |
| Beaker | 0.5L | 5L |

### Learn & Improve (Phase I)

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In our result, we showed that seeding was not eliminated. Using the hydroponic machine required 5L more water and 0.5L more nutrient solution than the beaker required. The culture medium used for the hydroponic cultivation of plants is required to be replaced regularly. Overall, using a smaller container as the planting vessel would be a more cost-effective way and less waste.

We understood that plant growth should be further investigated if the small solution volume would have an impact on this. However, since the target plant in the current project is not considered a large plant, we will focus on another issue: ‘Desipit using a breaker for our experiment is straightforward, having wooden sticks to stabilize the plant in the beaker is not user-friendly and hard to promote widely.

### Build & Test (Phase II)

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In recognised the current defect in our experiment kit, we designed a customized cover, and used a 3D printer to print this out. While designing this cover, we have to deal with several elements such as the thickness and the size. To fulfill the aim of the pH control in our project, we designed a pH detecting system and had our customized cover to accommodate this.

In this, our pH detecting system based on Arduino to connect with three pH meters and a Wi-Fi module from Dfrobot to monitor the pH value in real time. Also, we used three cups with a hole for the pH meter, a hole for plants, and a spot for the air pump. In order to make sure the other factors are the same, we connected all the air pump pipes together and put all the cups close together.

To investigate if our customized cover + pH detecting system is functional, we took the opportunity to place our plants and inoculate our engineered recombinant E.coli (transformed with glsA, pH shooting and pET11a as control) into the testing cup.

**Factors**

- 1. Thickness

As the height of the plant container is fixed, it can be imagined that the thicker the cover, the higher the nutrition liquid level in the beaker to make the nutrition liquid seep into the sponge within the plant container.

- 2. Size and number of holes

In total, there are three holes in the cover for placing a plant container, pH sensor, and also water pump. The diameter of the holes is shown in the following figure. To prevent the vertically-placed pH sensor tilting or falling, we also design two additional supporters.

![](https://static.igem.wiki/teams/4340/wiki/engineering-14.png)

Photo 3. Customized Cover Size (Left)

![](https://static.igem.wiki/teams/4340/wiki/engineering-build-and-test-cycle-2-ph-meter.jpg)
Photo 4. Our team members are designing the pH meters’ covers.

### Learn & Improve (Phase II)

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![](https://static.igem.wiki/teams/4340/wiki/proof-of-concept-ph-meters.png)

Photo 5. Monitoring the pH values of hydroponic solution

![](https://static.igem.wiki/teams/4340/wiki/improve-figure-1-2.png)

Figure 1. Comparison of glsA, pH shooting, and control under an acidic environment of a hydroponic system with an initial pH value of 5.2.

![](https://static.igem.wiki/teams/4340/wiki/engineering-15.jpg)

Photo 6. The cover is placed on the beaker.

Through the current investigation, we have demonstrated that small container design is feasible in the hydroponic system, and with our E.coli pH control system, the water efficiency in our future hydroponics would be increased. However, in consideration of massive usage, the circular design of beakers has limited real world application.

After designing a gene to control the pH environment in the hydroponic system, we used data collected from the internet to build a model (model1), thus predicting how different pH values ​​affect plant growth. The results showed that plants grow best in a neutral environment. Therefore, we decided to set up a pH monitoring system to monitor pH fluctuations every 10 minutes for a week.

### Build & Test (Phase III)

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We intend to optimize the current hardware. Through previous experiments, we found out that small containers are better than large ones, so we designed our hexagon-shaped kit.

This integrated kit was designed by hand drafting initially. We then made a three-dimensional model see if the design is workable or not. Afterward, we worked on various 2D components using Coreldraw and cut them out with a laser cutting machine using acrylic boards. Those components will be combined with acrylate adhesive and AB epoxy for the linking points to make it water-resistant.

![](https://static.igem.wiki/teams/4340/wiki/engineering-18.jpg)
![](https://static.igem.wiki/teams/4340/wiki/engineering-111.jpg)

Photo 7. The initial product of the prototype home kit.(Left)
Photo 8. The 2D design, including shapes, sizes and details of our home kit. (Right)

![](https://static.igem.wiki/teams/4340/wiki/engineering-16.png)
![](https://static.igem.wiki/teams/4340/wiki/engineering-17.png)

Photo 9. 2D Components of prototype home kit (Left)
Photo 10. 3D rendering of the design of prototype home kit. (Right)

### Learn and Improve (Phase III)

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The kits can be mounted and arranged in a honeycomb pattern in order to fit in any shape. Home users can align numbers of them in their small balconies. In this way, we can encourage more people to engage in self-farming, promoting the idea of sustainable food production. In addition, by aligning thousands of them, we would have this portal device to be placed in abandoned buildings. The current design is easy to assemble and has an additional hole in the bottom to replace the solution if needed. Depending on the needs of the customers, the size of the current kit would be scaled up, and the upper half structure would be simpified.

We understood that plant growth should be further investigated if the small solution volume would have an impact on this. Due to the time limitation, we have yet to have any plant fully grow in this device then harvest it. Since the cycle II has demonstrated that the pH control would be nicely achieved by our engineered recombinant E.coli strains, we are interested to have follow up experiments to fully investigate the potential of our design and iGEM parts.

#### Protocol of the hydroponic kit

We use the lazer-cutter to cut the acrylic, and then we first mounted the bottom part using AB epoxy, and then we puzzle the upper part. After that, we use the adhensive to stick the LED belt on the upper cover, and weld the wires together. Eventually, we put the foam and the plant holder inside the middle part and pour water, nutrition liquid and transformed E.coli for testing.

![](https://static.igem.wiki/teams/4340/wiki/engineering-19.jpg)

Photo 11. Final Product of prototype home kit.

### Hormone Binding Proteins

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* PYL8 (BBa\_K4340601)
* NSP4-T2R4 (BBa\_K4340600)

Hormone Binding Proteins is the second system we designed to accelerate the seed germination stage. Abscisic Acid (ABA) is a plant hormone that delays or stops seed germination in harsh environments (ie. extreme weather like droughts). In our indoor hydroponic setting, ABA is unnecessary and we want to stop its signaling to reduce the time required for seed germination. The system uses hormone-binding proteins PYL8 and T2R4. We have designed T2R4 with an NSP4 secretion peptide to increase the secretion of the protein out of the E.coli. PYL8, a new part, is a hypersensitive ABA binding protein that has a better ABA binding efficiency.

In our experiments, we submerged the soybeans in soft tissues absorbed with water to test out the effect of ABA and the function and efficiency of our plasmids in inhibiting ABA.

#### Please click the buttons below to show detailed information:

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Experiment 1: Hormone Soybean Tests
Experiment 2: Western Blot and Real-time PCR
Reference

### Experiment 1: Hormone Soybean Tests

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Since ABA is not soluble in water, we use DMSO (organic solvent) to dissolve ABA powder. Since DMSO is toxic to E.coli if the percentage is bigger than 1/100 in liquid [1] (Figure 1), in the first hour of the three groups of repeating experiments, 2 ul of ABA dissolved by DMSO in the ratio of 20mg/ml is added to the ABA treated groups (ABA, ABA-NSP4-ABA, ABA-PYL8, ABA-pET11a).

For all groups treated with our transformed E.coli culture, 60 ul of transformed E.coli culture is added at every checkpoint (09:00 in the morning during seven days.). All the surfaces of the soybean groups are covered with liquid by spraying distilled water at every checkpoint.

| ![](https://static.igem.wiki/teams/4340/wiki/engineering-2-1.png)  |  | |  Figure 1. The inhibition of different concentrations of DMSO to the E.coli after 25 hr. at 37°C SOURCE: Antimicrobial Activity of Dimethyl Sulfoxide Against Escherichia coli, Pseudomonas aeruginosa, and Bacillus megaterium, HOWARD C. ANSEL, WILLIAM P. NORRED\*, and IVAN L. ROTHI, 1969, https://doi.org/10.1002/jps.2600580708 |

![](https://static.igem.wiki/teams/4340/wiki/hormone-test1.png)

Figure 2. The seed germination percentage of soybeans in seven days with all factors.

![](https://static.igem.wiki/teams/4340/wiki/hormone-test-pyl8.png)

Figure 3. The seed germination percentage of soybeans in seven days with factors with ABA added.

![](https://static.igem.wiki/teams/4340/wiki/hormone-test-t2r4.png)

Figure 4. The seed germination percentage of soybeans in seven days without ABA (only transformed E.coli culture)

![](https://static.igem.wiki/teams/4340/wiki/notebook32.png)

Photo 1: Soybeans treated with ABA and related transformed E.coli

We tested whether the hormone binding domain can inhibit and attenuate the effect of ABA (plant hormone that inhibits germination and plant growth) to plant germination. The pET11a and PYL8 E.coli culture groups, and a group with ABA treatment and PYL8 E. coli culture had a higher germinated percentage than the group without ABA (blank). This indicated that the PYL8 had a positive effect on the seed germination stage both with ABA and without ABA. (Figure 2&3)

In the PYL8 group results, the percentage of germinated soybeans with PYL8 E.coli culture and ABA was significantly higher than in groups only with ABA. This shows that the PYL8 worked to produce an inhibitor of ABA. Notably, the PYL8 group (only added PYL8 cell culture) is even higher than the group without ABA. This demonstrated that PYL8 can discharge the effect of ABA, and even benefit seed germination squarely. (Figure 3)

NSP4-T2R4, however, had the same function as the pET11a vector control, (Figure 4) which means that NSP4-T2R4 might not have an outstanding effect on increasing the speed of germination of soybeans.

To sum up, the efficiency of the plasmids ranked is PYL8> NSP4-T2R4 (slightly higher than) pET11a.

### Experiment 2: Western Blot and Real-time PCR

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![](https://static.igem.wiki/teams/4340/wiki/engineering-2-2.jpg)

Figure 1. The protein expression of NSP4-T2R4 and PYL8

![](https://static.igem.wiki/teams/4340/wiki/engineering-2-3.jpg)
![](https://static.igem.wiki/teams/4340/wiki/engineering-2-4.jpg)

Figure 2: The real-time PCR result of PYL8 comparing with BL21 E.coli strain. (Left)
Figure 3: The real-time PCR result of NSP4-T2R4 comparing with BL21 E.coli strain. (Right)

We conducted a western blot experiment to validate the quality of protein expression of PYL8 and NSP4-T2R4. In the experiment, there is a clear band of PYL8 in both 10ul and 20ul samples at 35 kDa. There is a relatively more blurry band of NSP4-T2R4. (Figure 1)We conducted western blot experiment to validate the protein expression of PYL8 and NSP4-T2R4. In our experiment, there is a clear band of PYL8 in both 10ul and 20ul samples at 35 kDa. There is a relatively more blurry band of NSP4-T2R4.

In our real-time PCR test, the fold change of PYL8 against BL21 is lower than NSP4-T2R4. This means the mRNA of PYL8 is smaller than NSP4-T2R4 (while the DNA of NSP4-T2R4 is larger than PYL8), which is the same as predicted. (Figure 2&3)

### Reference

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1. [Antimicrobial Activity of Dimethyl Sulfoxide Against Escherichia coli, Pseudomonas aeruginosa, and Bacillus megaterium, HOWARD C. ANSEL, WILLIAM P. NORRED\*, and IVAN L. ROTHI](https://doi.org/10.1002/jps.2600580708)

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{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
{% block page\_content %}

## What should this page contain?

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Describe the research, experiments, and protocols you used in your iGEM project. These should be detailed enough for another team to repeat your experiments.

If you made Parts this year, please remember to put all information, characterization, and measurement data on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).

## Inspirations

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* [2019 Nantes](https://2019.igem.org/Team%3ANantes/Experiments)
* [2019 TU Eindhoven](https://2019.igem.org/Team%3ATU_Eindhoven/Experiments)
* [2019 Mingdao](https://2019.igem.org/Team%3AMingdao/Demonstrate)
* [2020 Amsterdam](https://2020.igem.org/Team%3AAmsterdam/Experiments)
* [2020 NCTU Formosa](https://2020.igem.org/Team%3ANCTU_Formosa/Experiments)
* [2020 USAFA](https://2020.igem.org/Team%3AUSAFA/Experiments)

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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 %}
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## The final results

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1. Growth curve test
[Our experiments for GFP transformed E.coli growth curve](https://2022.igem.wiki/puiching-macau/contribution)2. pH maintenance, OD and Fluorescence result (glsA and sfGFP)
[Our experiments for Pasr-glsA and sfGFP result](https://2022.igem.wiki/puiching-macau/contribution)3. pH maintenance and OD result (pH shooting system and pET11a)
[Our experiments for genetic pH shooting system\_pET11a plasmid validations](https://2022.igem.wiki/puiching-macau/improve)4. Redesigned constructs with pSB1C3
[Our experiments for Pasr-glsA, genetic pH shooting, sfGFP in pSB1C3 vectors plasmid validations](https://2022.igem.wiki/puiching-macau/improve)5. Hormone treated seed germination test (PYL8 and NSP4-T2R4)
[Our experiments for hormone seed germination test](https://2022.igem.wiki/puiching-macau/engineering)

#### Please click the buttons below to show detailed information:

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Growth curve test
pH maintenance result (Pasr-glsA and sfGFP)
pH maintenance result (genetic pH shooting system and pET11a)
Redesigned constructs with pSB1C3
Hormone treated seed germination test (PYL8 and NSP4-T2R4)

## E.coli GFP\_pET11a (BBa\_J364000) growth curve

![](https://static.igem.wiki/teams/4340/wiki/gfp-abs.png)

Figure 1a. Line plot showing GFP Fluorescence emmission rate across time (hours), with different culturing mediums. Error bar representing Mean±SEM.

![](https://static.igem.wiki/teams/4340/wiki/gfp-fl.png)

Figure 1b. Line plot showing GFP Fluorescence emmission rate across time (hours), with different culturing mediums. Error bar representing Mean±SD.

To test whether E. coli can grow and express GFP in the hydroponic nutrient solutions, we compared the growth of E. coli and GFP expression in the hydroponic solution, LB medium, and water. We calculated the standard error (SE), with n=3:

![](https://static.igem.wiki/teams/4340/wiki/se-treatment.png)

where σ is the standard deviation, and n is the number of trials.

---

We found that the E. coli transformed with GFP is most suitable to grow in LB, followed by hydroponic solution and finally pure water. As expected, the Absorbance OD600 of cell culture in LB medium is the highest. The absorbance in the hydroponic solution is lower than LB, however, it is higher than pure water, suggesting the E.coli can grow with the hydroponic solution.

The absorbance in the hydroponic solution reached a peak at the 25th hour, which shows that the E.coli grew and multiplied on the first day. Then, in the 25th to the 45th hour, the value decreases to around 0.1. After that, from the 45th to the 100th hour, the value remains stable. At the 100th to 180th hour, the value increases steadily. These suggest E. coli can grow and survive in hydroponic solution over days.

The group's absorbance in pure water peaked at the 25th hour and decreased between the 25th to the 70th hour. This demonstrates that on the first day, the E.coli can still survive in pure water, but in the following hours the E.coli died out.

The trends of GFP fluorescence emission rate correspond to the trends of absorbance rate, showing a positive correlation between GFP protein expression and the growth of E. Coli. GFP fluorescence was highest in LB, but it is still substantially present in hydroponic solution.

To sum up, the data indicate that E. coli cells can survive and express protein within hydroponic systems, as demonstrated by the growth and GFP expression. (Figure 1a and 1b)

Compared to hydroponic water and LB medium, the rank of growing environments from most to least suitable E. coli transformed with GFP is LB> hydroponic water> pure water. The Absorbance rate of cell culture in LB medium is the highest, the recorded rate in the hydroponic water is lower than LB group, however, it is higher than pure water.

The Absorbance rate in LB medium is the highest and keeps increasing through out the 180 hours. This indicates that the LB medium is suitable for E.coli to grow.

The Absorbance rate in hydroponic water reached a peak at the 25th hour, which shows that the E.coli survived and multiplied in the first day. Then, in the 25th to the 45th hour, the value decreases to around 0.1. After that, from the 45th to the 100th hour, the value remains stable. At the 100th to 180th hour, the value increases steadily.

The group in pure water has reached its peak at the 25th hour, and the rate decreases between the 25th to the 70th hour. In the following 100 hours, the rate slightly fluctuate. This demonstrate that in the first day, the E.coli can still survive in the pure water, but in the following hours the E.coli died.

To sum up, the data indicates that E. coli cells can survive and express protein within hydroponic systems, as demonstrated by the GFP expression. (Figure 1)

## Pasr-glsA (BBa\_K4340603) and sfGFP\_pET11a (BBa\_K4340605) pH maintenance functional test

These are the results of our Pasr-glsA in pET11a plasmid test (comparing with sfGFP\_pET11a). We measured the pH, OD 600, and fluorescence of the cell culture of the transformed E.coli. We use these three indicators to validate the pH neutralizing function and the survival and growth curve of the E.coli transformed with Pasr-glsA\_pET11a plasmid.

#### 1. pH change test

|  |  |  |
| --- | --- | --- |
| ![](https://static.igem.wiki/teams/4340/wiki/glsa-ph-ph5.png)   Figure 1. The pH change in 24 hours of glsA compared to sfGFP (BBa\_K4340605) as the control group starting from initial pH 5. | ![](https://static.igem.wiki/teams/4340/wiki/glsa-ph-ph7.png)   Figure 2. The pH change in 24 hours of glsA compared to sfGFP (BBa\_K4340605) as the control group starting from initial pH 7. | ![](https://static.igem.wiki/teams/4340/wiki/glsa-ph-ph9.png)   Figure 3. The pH change in 24 hours of glsA compared to sfGFP (BBa\_K4340605) as the control group starting from initial pH 9. |

As the Pasr-glsA\_pET11a plasmid is an acid shooting circuit that functions at a low pH environment, the pH change of Figure 1 is very significant in that the pH converges to pH 7 after 24 hours. For figure 3, which is in a pH 7 environment, the pH of Pasr-glsA\_pET11a culture drops to pH 6.5 in the first three hours and increases gradually from the third to the ninth hour. Then, starting from the ninth to the 24th hour, the pH increases to around pH 7.4. Overall, the Pasr-glsA\_pET11a plasmid does not make a shift change in the pH 7 environment, which is the same result as predicted. In Figure 4, which is in a pH 9 environment where Pasr-glsA\_pET11a should not function, the pH first drops to around pH 7.4 in the first nine hours and climbs up to pH 7.8 slowly from the ninth hour to the 24th hour. At the same time, the control group sfGFP (BBa\_K4340605)follows the same pattern, which indicates that the Pasr-glsA\_pET11a does not function in a high pH environment.

#### 2. OD change test

|  |  |  |
| --- | --- | --- |
| ![](https://static.igem.wiki/teams/4340/wiki/glsa-od-ph5.png)   Figure 4. The OD changes of Pasr-glsA-pET11a and sfGFP-pET11a transformed E.coli in a pH 5 environment. | ![](https://static.igem.wiki/teams/4340/wiki/glsa-od-ph7.png)   Figure 5. The OD changes of Pasr-glsA-pET11a and sfGFP-pET11a transformed E.coli in a pH 7 environment. | ![](https://static.igem.wiki/teams/4340/wiki/glsa-od-ph9.png)   Figure 6. The OD changes of Pasr-glsA-pET11a and sfGFP-pET11a transformed E.coli in a pH 9 environment. |

We also tested the OD value of the E.coli transformed with Pasr-glsA\_pET11a and the sfGFP\_pET11a (as a control group). In the glsA group, E.coli grows best at pH 7, following pH 5, and finally at pH 9. Since Pasr-glsA constructs can only work to neutralize a low pH environment, the result is as predicted. However, in the sfGFP control group, the highest OD600 rate is in the pH7 environment.

#### 3. Fluorescence test

|  |  |  |
| --- | --- | --- |
| ![](https://static.igem.wiki/teams/4340/wiki/glsa-fl-ph5.png)   Figure 7. The Fluorescence Rate in pH 5 initial environment of sfGFP\_pET11a and Pasr-glsA\_pET11a. | ![](https://static.igem.wiki/teams/4340/wiki/glsa-fl-ph7.png)   Figure 8. The Fluorescence Rate in pH 7 initial environment of sfGFP\_pET11a and Pasr-glsA\_pET11a. | ![](https://static.igem.wiki/teams/4340/wiki/glsa-fl-ph9.png)   Figure 9. The Fluorescence Rate in pH 9 initial environment of sfGFP\_pET11a and Pasr-glsA\_pET11a. |

The fluorescence of sfGFP in pH 5 is significantly higher than the fluorescence of Pasr-glsA. It is possibly because the protein size of the sfGFP is smaller than Pasr-glsA, which at the same time produces ammonia to neutralize the environment. In a pH 7 environment, the fluorescence of both sfGFP and Pasr-glsA is relatively similar and reached the same point at the ninth hour. In a pH 9 environment, both fluorescence of sfGFP and Pasr-glsA are low compared to data in pH 5 and 7. This proved that sfGFP and Pasr-glsA, which have the same acid promoter (asr) show low fluorescence in a high-pH environment.

#### 4. real-time qPCR result

![](https://static.igem.wiki/teams/4340/wiki/glsa-pcr.png)

Figure 10. The real-time qPCR test result of Pasr-glsA.

In our real-time quantitative PCR test, we can see that the fold change of glsA in pH 6.0 is the highest, follow by pH 6.0 and pH 7.0. The glsA mRNA expressed in acid and weak acid environment, which demonstrate that glsA expressed appropriately as predicted.

## Genetic pH shooting system (BBa\_K4340609) and pET11a empty vector pH maintenance functional test

To improve Pasr-glsA, we designed a pH shooting system which contains two circuits: ASC (acid shooting circuit) and BSC (base shooting circuit).

#### Experiment 1: pH and OD change of genetic pH shooting system and Pasr-glsA compared with control

In our experiment, we accomplished a pH change test and monitored its OD value to demonstrate the survival of the bacteria, and ensure that the pH change is conducted by our transformed E.coli. On the other hand, we have conducted a western blot experiment to validate the expression of glsA in pH 5, 6, and 7; the expression of ldhA in pH 5, 7, and 9 by testing the existence of the related protein.

##### 1. pH change test

![](https://static.igem.wiki/teams/4340/wiki/compare-ph-glsa-phs-ph5.jpg)

Figure 1. The pH change of genetic pH shooting system\_pET11a and Pasr-glsA\_pET11a with pET11a (control) in the pH 5 initial environment.

![](https://static.igem.wiki/teams/4340/wiki/phs-ph-6.png)

Figure 2. The pH change of genetic pH shooting system\_pET11a in the pH 6 initial environment.

![](https://static.igem.wiki/teams/4340/wiki/compare-ph-glsa-phs-ph7.png)

Figure 3. The pH change of genetic pH shooting system\_pET11a and Pasr-glsA\_pET11a with pET11a (control) in the pH 7 initial environment.

![](https://static.igem.wiki/teams/4340/wiki/phs-ph-8.png)

Figure 4. The pH change of genetic pH shooting system\_pET11a in the pH 8 initial environment.

![](https://static.igem.wiki/teams/4340/wiki/compare-ph-glsa-phs-ph9.jpg)

Figure 5. The pH change of genetic pH shooting system\_pET11a and Pasr-glsA\_pET11a with pET11a (control) in the pH 9 initial environment.

The pH change of the genetic pH shooting system is larger than the control group (pET11a) in the initial pH 5 environment in the first 5 hours, indicating that the genetic pH shooting system worked to converge the pH to neutral pH level. However, compared with Pasr-glsA, this system has less efficiency in acidic environment adjusting.(Figure 1)

In the initial pH 6 environment, the convergence of the genetic pH shooting system to neutral pH performed well in the 7th to 9th hours. In the following 15 hours, both the pH levels of the control and genetic pH shooting system group raised to pH 8 due to the possibility of the ammonia generated by the died E.coli. (Figure 2)

In the initial pH 7 environment, the pH curve of both groups are relatively similar, showing that the system does not function in a pH 7 environment, which conforms to the promoter design (Pasr for acidic environment and P-atp2 for alkaline environment) (Figure 3)

In both initial pH 8 and pH 9, the pH level of the genetic pH shooting system drops more than the control group (pET11a). This demonstrated that the base shooting circuit functioned to neutralize the alkaline environment. (Figure 4&5)

To sum up, the genetic pH shooting system worked and optimize the Pasr-glsA construct, with an alkaline adjusting system and a stable pH neutralizing ability.

#### 2. OD change test

![](https://static.igem.wiki/teams/4340/wiki/compare-od-glsa-phs-ph5.png)

Figure 1. The OD change of genetic pH shooting system\_pET11a and Pasr-glsA\_pET11a with pET11a (control) in the pH 5 initial environment.

![](https://static.igem.wiki/teams/4340/wiki/phs-od-ph6.png)

Figure 2. The OD change of genetic pH shooting system\_pET11a in the pH 6 initial environment.

![](https://static.igem.wiki/teams/4340/wiki/compare-od-glsa-phs-ph7.png)

Figure 3. Figure 1. The OD change of genetic pH shooting system\_pET11a and Pasr-glsA\_pET11a with pET11a (control) in the pH 7 initial environment

![](https://static.igem.wiki/teams/4340/wiki/phs-od-ph8.png)

Figure 4. The OD change of genetic pH shooting system\_pET11a in the pH 8 initial environment.

![](https://static.igem.wiki/teams/4340/wiki/compare-od-glsa-phs-ph9.png)

Figure 5. The OD change of genetic pH shooting system\_pET11a and Pasr-glsA\_pET11a with pET11a (control) in the pH 9 initial environment

Overall, the OD of the genetic pH shooting system is higher than the Pasr-glsA. This demonstrated that the genetic pH shooting system worked to survive better in an acidic and alkaline environment. Particularly, the OD curve of the pH shooting system is significantly higher than the Pasr-glsA in a pH 9 environment, indicating that the base circuit facilitated the E.coli growth in an alkaline environment.

For the OD change of the genetic pH shooting system, The highest OD value is in the pH 7 environment, followed by pH 8, and pH 9. The OD value of pH 9 is lower than the other pH groups (pH 7, 8, and 9 of the genetic pH shooting system), which shows that the transformed E.coli might not grow as well as E.coli with an empty pET11a vector since it has to produce alkaline.

#### Experiment 2: Western blot

![](https://static.igem.wiki/teams/4340/wiki/phs-westernblot.png)

Figure 11. Our western blot result shows the protein expression in different pH environments. (glsA for Pasr-glsA, pHS for genetic pH shooting system)

The western blot was able to validate the quality of protein expression of glsA and the pH shooting system. In the experiment, there is a clear band of both the pH shooting system and glsA in 20ul samples at 30 kDa. There is a relatively more blended band of the 10ul samples. As predicted, it is clear that the glsA in the pH5 environment expresses the best.

## Pasr-glsA (BBa\_K4340603), genetic pH shooting system (BBa\_K4340609), sfGFP (BBa\_K4340605) redesigned with pSB1C3 vector pH maintenance functional test

#### Experiment 1: pH changes in different initial pH medium

![](https://static.igem.wiki/teams/4340/wiki/psb1c3-ph5-redesign.jpg)

Figure 1. The pH changes by the newly designed pH system compared with the initial pH 5 environment between glsA, pH shooting system, and control (pET11a empty vector).

![](https://static.igem.wiki/teams/4340/wiki/pet11a-ph5-redesign.jpg)

Figure 2. The pH changes by the original pH system compared with the initial pH 5 environment between glsA, pH shooting system, and control.

![](https://static.igem.wiki/teams/4340/wiki/psb1c3-ph7-redesign.jpg)

Figure 3. The pH changes by the newly designed pH system compared with the initial pH 7 environment between glsA, pH shooting system, and control.

![](https://static.igem.wiki/teams/4340/wiki/pet11a-ph7-redesign.jpg)

Figure 4. The pH changes by the original pH system compared with the initial pH 7 environment between glsA, pH shooting system, and control (pET11a empty vector)

![](https://static.igem.wiki/teams/4340/wiki/psb1c3-ph9-redesign.jpg)

Figure 5. The pH changes by the newly designed pH system compared with the initial pH 9 environment between pH shooting system and control (pET11a empty vector)

![](https://static.igem.wiki/teams/4340/wiki/pet11a-ph9-redesign.jpg)

Figure 6. The pH changes by the original pH system compared with the initial pH 9 environment between genetic pH shooting system-pET11a and control (pET11a empty vector)

In relative initial pH levels, the new glsA and pH shooting constructs regulated pH as efficiently as the original constructs within the first ten hours. When the initial pH is 5, by the tenth hour, both Pasr-glsA-pSB1C3 and genetic pH shooting system-pSB1C3 were working well due to the acid promoter (asr) of their respective glsA genes. When the initial pH is 7, the pH was roughly neutral and unchanged at the tenth hour. When the initial pH is 9, the ldhA gene of the genetic pH shooting system-pSB1C3 was expressed through its base promoter, P-atp2. It worked well in the first ten hours, lowering the pH by 0.8. In all three cases, the lines converged by the 24th hour. This can be explained by expected non-apoptotic cell death, in which the cells may discharge substances that can alter pH. In general, the results show that the newly designed constructs can function well without IPTG induction, so it is more convinient for the implementation of our transformed E.coli in the plant growth.

#### Experiment 2: pH changes in a hydroponic system, pH 5, 7, and 9

We have set up a system with three pH meters and a Wi-Fi module to test the pH of the hydroponic system for the whole day. The pH meter will mark the data and the Wi-Fi module will send it to the internet, so we can monitor the value whenever we want.

![](https://static.igem.wiki/teams/4340/wiki/improve-figure-1-2.png)

Figure 7. Comparison of glsA, pH shooting, and control under an acidic environment of a hydroponic system initial pH: pH 5.2

As the graph (Figure 7) shows, we can easily see that when there is the pH-control gene in the cell in the hydroponic system, the pH was stable at around 6.5 range. Although the line might not be so smooth due to the instability of the current in the pH meter, the overall trend shows an expected result. When it comes to the control, the pH value keeps rising continuously, which is deleterious to the germination and growth of plants.

## Hormone seed germination test (NSP4-T2R4 (BBa\_K4340600) and PYL8 (BBa\_K4340601))

Hormone Binding Proteins is the second system we designed to accelerate the seed germination stage. Abscisic Acid (ABA) is a plant hormone that delays or stops seed germination in harsh environments (ie. extreme weather like droughts). In our indoor hydroponic setting, ABA is unnecessary and we want to stop its signaling to reduce the time required for seed germination. The system uses hormone-binding proteins PYL8 and T2R4. We have designed T2R4 with an NSP4 secretion peptide to increase the secretion of the protein out of the E.coli. PYL8, a new part, is a hypersensitive ABA binding protein that has a better ABA binding efficiency.

We have tested whether hormone binding domain can inhibit and attenuate the effect of ABA (plant hormone that inhibits germination and plant growth) to plant germination. The pET11a and PYL8 E.coli culture groups, and a group with ABA treatment and PYL8 E. coli culture had a higher germinated percentage than the group without ABA (blank). This indicated that the PYL8 had a positive effect on the seed germination stage both with ABA and without ABA. (Figure 1)

In the PYL8 group results, the percentage of germinated soybeans with PYL8 E.coli culture and ABA was significantly higher than in groups only with ABA. This shows that the PYL8 worked to produce an inhibitor of ABA. Notably, the PYL8 group (only added PYL8 cell culture) is even higher than the group without ABA. This demonstrated that PYL8 can discharge the effect of ABA, and even benefit seed germination squarely. (Figure 2)

NSP4-T2R4, however, had the same function as the pET11a vector control, (Figure 1) which means that NSP4-T2R4 might not have an outstanding effect on increasing the speed of germination of soybeans.

## Experiment 1: Hormone treated germination test

![](https://static.igem.wiki/teams/4340/wiki/notebook32.png)

Photo 1: Soybeans trated with ABA and related transofrmed E.coli

![](https://static.igem.wiki/teams/4340/wiki/hormone-test1.png)

Figure 1. The seed germination percentage of soybeans in seven days with all factors.

![](https://static.igem.wiki/teams/4340/wiki/hormone-test-pyl8.png)

Figure 2. The seed germination percentage of soybeans in seven days with factors with PYL8.

![](https://static.igem.wiki/teams/4340/wiki/hormone-test-t2r4.png)

Figure 3. The seed germination percentage of soybeans in seven days with NSP4-T2R4.

To sum up, the efficiency of the plasmids ranked is PYL8> NSP4-T2R4 (slightly higher than) pET11a.

## Experiment 2: Western Blot and real-time qPCR

![]( https://static.igem.wiki/teams/4340/wiki/engineering-2-2.jpg)

Figure 4. The protein expression of NSP4-T2R4 and PYL8

![](https://static.igem.wiki/teams/4340/wiki/engineering-2-3.jpg)
![](https://static.igem.wiki/teams/4340/wiki/engineering-2-4.jpg)

Figure 5&6: The real-time qPCR result of PYL8 and NSP4-T2R4 compared with BL21 E.coli strain.

We have conducted a western blot experiment to validate the quality of protein expression of PYL8 and NSP4-T2R4. In the experiment, there is a clear band of PYL8 in both 10ul and 20ul samples at 35 kDa. There is a relatively more blended band of NSP4-T2R4. (Figure 5) Therefore, further experiments would be needed to confirm whether no protein expression is the reason why T2R4 cannot inhibit ABA as in PYL8.

However, in the real-time quantitative PCR test, we found the RNA expression of both genes, indicating there could be some protein expression problem with T2R4, but not RNA. (Figures 5&6)

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