iGEM 2023 — Team puiching-macau

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{% block title %}Notebook{% endblock %}
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## Protocols

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## April-May

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| * Training: live cell imaging, trypan blue cell counting, fluorescence microscopy, virus packaging, etc. * Preparation for E. coli dosage tests: transformation and midi prep    Week 1 (April 17-23)  1. Training live cell imaging with two-photon excitation microscopy/ confocal microscopy, using eGFP, and mCherry transfected stem cells    Week 2 (May 1-7)  1. Preparing tips, cells, and medium 2. Training with a fluorescence microscope 3. Training cell counting with trypan blue    Week 3 (May 8-14)  1. Transformation of GFP control into Top10 cells    Week 4 (May 15-21)  1. Midi prep to extract GFP from Top10 cells 2. Virus packaging of GFP to HEK cells | ![](https://static.igem.wiki/teams/4814/wiki/week1-1.jpg) ![](https://static.igem.wiki/teams/4814/wiki/week1-2.jpg)    ![](https://static.igem.wiki/teams/4814/wiki/week2-1.jpg) ![](https://static.igem.wiki/teams/4814/wiki/week4-1.jpg) |

## June

---

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| --- | --- |
| * Top10 dosage tests with UV: finding suitable concentrations and intensities of chemicals and UV to induce DNA damage * Continue preparation for E. coli dosage tests: transformation, midi prep, and preparing bacterial stock * Preparation for FRET biosensor: virus packaging and extraction    Week 5 (May 29-June 4)  1. Top10 dosage tests with UV    Week 6 (June 5-11)  1. Transformation of RecA-EGFP (K3 and K6) 2. Midi for RecA (K6 is successful)    Week 7 (June 19-25)  1. Transformation for K3-EGFP-hha, K6-EGFP-hha, K3-hha 2. Midi for K3-EGFP-hha, K3-hha 3. Pass HEK cells & E. coli stocking | ![](https://static.igem.wiki/teams/4814/wiki/week7-1.jpg) |

## July

---

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| --- | --- |
| * Preparation for FRET biosensor: virus packaging, finding a suitable cell line that can express our viruses * E. coli dosage tests with UV and H2O2: the plate reader data was not conclusive, so we decided to use cell imaging for further validation    Week 8 (June 26-July 2)  1. Virus packaging and extraction(mCherry eGFP eYFP eCFP) 2. K6-EGFP-hha transformation and stocking 3. Pass cell (HEK293 and B16F10)    Week 9 (July 3-9)  1. Infect HEK293 and B16F10 2. E.coli UV and H2O2 test 3. Thaw cells (HEK293 and B16F10)    Week 10 (July 10-16)  1. E.coli UV and H2O2 test, with cell imaging 2. Pass cell (HEK293 and B16F10) 3. Virus packaging    Week 11 (July 17-23)  1. E.coli UV and H2O2 test, with cell imaging 2. Pass cell (HEK293 and B16F10)    Week 12 (July 24-30)  1. E.coli UV and H2O2 test, with cell imaging 2. Pass cell (HEK293 and B16F10) 3. Midi for CFP, YFP, mCherry control | ![](https://static.igem.wiki/teams/4814/wiki/week8-1.jpg) ![](https://static.igem.wiki/teams/4814/wiki/week-9-1.jpg)    ![](https://static.igem.wiki/teams/4814/wiki/week10-1.jpg) ![](https://static.igem.wiki/teams/4814/wiki/week10-2.jpg)    ![](https://static.igem.wiki/teams/4814/wiki/week12-1.jpg) |

## August

---

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| --- | --- |
| * E. coli test with UV, H2O2, nalidixic acid, and aspartame: Using plate readers and cell imaging to quantify fluorescence * Preparation for FRET biosensor: virus packaging and infection, using fluorescent microscopy to find a suitable cell line, continue selecting cells * Contamination of mammalian cells: use HBSS to wash cells    Week 13 (July 31-August 6)  1. E. coli test with UV, H2O2, nalidixic acid, and aspartame, with cell imaging 2. Infect and select B16F10 and HEK293    Week 14 (August 7-13)  1. E. coli test with UV, H2O2, nalidixic acid, and aspartame, with cell imaging 2. Pass cell (A2780, OVC, B16F10, HEK293) 3. Select B16F10 and HEK293 and photograph with fluorescence microscope 4. Pack virus    Week 15 (August 14-20)  1. Pass, infect and select OVC, A2780 2. Pack virus 3. E. coli test with UV, H2O2, nalidixic acid, and aspartame, with cell imaging 4. Seed mCherry and YFP into 6 well plate    Week 16 (August 21-27)  1. Infect mCherry and YFP with GFP and CFP respectively 2. Transformation and midi for DVPR, VSVG 3. Bacteria identified, only eGFP-mCherry is viable 4. Remake medium and use HBSS to wash cells | ![](https://static.igem.wiki/teams/4814/wiki/week13-1.jpg)    ![](https://static.igem.wiki/teams/4814/wiki/week14-1.jpg)    ![](https://static.igem.wiki/teams/4814/wiki/week15-1.jpg) ![](https://static.igem.wiki/teams/4814/wiki/week16-1.jpg) |

## September-October

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| --- | --- |
| * Contamination of mammalian cells: repack viruses because old viruses are contaminated * Preparation for FRET biosensor: infecting and passing selected cells to confocal dish * Observing FRET: Using confocal microscopy find the optimal intensity of UV, and observe the congregation of fluorescence before and after UV treatment, quantifying FRET * E. coli with different RBS tests with UV, H2O2, nalidixic acid, and aspartame    Week 17 (August 28-September 3)  1. Pass cell (HEK293 and A2780) 2. Repack viruses    Week 18 (September 4-10)  1. Infect A2780 and HEK293 (mCherry, eGFP, eYFP, eCFP, mCherry-eGFP, eYFP-eCFP) 2. Pass YFP and mCherry (HEK293) to 6 well plate 3. Continue to wash cells with HBSS    Week 19 (September 11-17)  1. Pass YFP, GFP, mCherry, and GFP-mCherry (HEK293) to confocal dishes and UV test 2. Infect and select CFP-YFP (HEK293) 3. TUNEL assay on HEK cells    Week 20 (September 18-24)  1. Infect CFP and CFP-YFP (HEK293) 2. Transformation and midi prep for E. coli with different RBS (B0032, strong) 3. E. coli test of new RBSs with UV, H2O2, nalidixic acid, and aspartame 4. Pass GFP, GFP+mCherry (mCherry double infected), mCherry (double infected) to confocal dishes    Week 21 (September 25-October 1)  1. Pack CFP and YFP virus 2. Thaw HEK293 cells, re-infect with CFP and YFP, then select 3. UV test on cells in confocal dishes from week 20 4. Pass CFP, CFP+YFP, GFP to confocal dish and do UV test    Week 22 (October 2-8)  1. E. coli test of new RBSs with UV, H2O2, nalidixic acid, and aspartame 2. mCherry and GFP-mCherry UV test | ![](https://static.igem.wiki/teams/4814/wiki/week19-1.jpg) ![](https://static.igem.wiki/teams/4814/wiki/week20-1.jpg)    ![](https://static.igem.wiki/teams/4814/wiki/week20-2.jpg) ![](https://static.igem.wiki/teams/4814/wiki/weeek20.jpg) |

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{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
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Throughout the development of our systems, we have utilized iGEM's engineering cycle, particularly in the following aspects:

##### [RecA(BBa\_K3020001)-EGFP test with UV, H2O2, Nalidixic acid, and Aspartame](#section1)

---

In order to validate the findings of a previous iGEM team and investigate the genotoxic effects of carcinogens, we conducted dosage-dependent tests on the TOP10 E. coli. We utilized the RecA(K3) promoter ([BBa\_K3020001 (K3)](https://parts.igem.org/Part%3ABBa_K3020001)) in conjunction with EGFP ([BBa\_K3020002](https://parts.igem.org/Part%3ABBa_K3020002)) as a bioreporter to detect DNA-damaging agents such as UV, H2O2 and nalidixic acid. The RecA(K3) This promoter is an optimized version of [BBa\_K629001 (K6)](https://parts.igem.org/Part%3ABBa_K629001), a design by team SYSU 2011 aimed at driving the motor system of E. coli in a radioactive environment. To determine the comparative DNA-damage detection ability of the RecA(K3) and RecA(K6) promoters, we designed RecA(BBa\_K3020001)-EGFP[BBa\_K4814015](https://parts.igem.org/Part%3ABBa_K4814015) and RecA(BBa\_K629001)-EGFP [BBa\_K4814014](https://parts.igem.org/Part%3ABBa_K4814014)constructs, and measured the GFP emission from these two promoter designs when exposed to carcinogens. Furthermore, we added a New Composite Part using different RBS: Strong [BBa\_K4814013](http://parts.igem.org/Part%3ABBa_K4814013) and B0032 [BBa\_K4814002](http://parts.igem.org/Part%3ABBa_K4814002).

##### [3D Protein Modeling and Docking](#section2)

---

As the efficiency of FRET is largely dependent on the degree of the molecular separation and overall spatial arrangement of the two fluorophores involved, further ratification is needed to ensure the reliability of the sequences we designed for our FRET system and thereby validate the credibility of the data acquired from FRET imaging. To accomplish this, we have undertaken the development of a protein model. Our protein model involves a series of scientific methodologies, including sequence predictions, RMSD calculations, protein-protein docking, and distance measurements. We aim to predict and elucidate the interaction between ATRIP-eGFP and RPA1-mCherry, shedding light on the properties exhibited by the two fluorescent proteins as they interact. Our model also serves to outrule the possibility of unexpected or failed experimental outcomes being due to the feasibility of protein binding interactions. As our experiments progressed and our model advanced to measure the separation between fluorescent proteins, our model serves as further proof that the observation of increased FRET after UV treatment was most likely due to the close proximity of the proteins.

#### RecA(BBa\_K3020001)-EGFP test with UV, H2O2, Nalidixic acid, and Aspartame

---

Cycle 1 Dosage dependent test
Cycle 2 Cell imaging
Cycle 3 RBS test

### Design 1.1

To test and investigate promoter RecA(K3) and RecA(K6), we designed two composite parts, RecA(K6)BBa\_K4814014 and RecA(K3)BBa\_K4814015.

### Build 1.1

We transformed our designed plasmids into E.coli top10. Bacterial culture was incubated over night for 18 hours and diluted to OD 0.23 before the experiments.

### Test 1.1

##### Dosage Dependent test

Initially, the bacteria were treated and transferred into 96-well plates to measure fluorescence and optical density (absorbance at 600nm). The dosage references were obtained from team BIT 2019. Additionally, we tested Aspartame at a concentration of 1 g/L based on the study by Yılmaz and Uçar (2014).

During the initial three experiments, a plate reader was employed to perform fluorescence scans on 96-well plates. However, as demonstrated by the graphical representations, the observed fluorescence levels do not exhibit an increase with the escalating quantities of UV and H2O2. On the contrary, a decrease in fluorescence is observed. Our hypothesis postulates that the utilization of the peripheral wells of the 96-well plate may result in the evaporation of the well contents during the incubation process, thereby inducing an edge effect. Consequently, this phenomenon may introduce a potential bias in the obtained results.

![](https://static.igem.wiki/teams/4814/wiki/lab/first-reca-test-uv.png)

Figure 1. The fluorescence of RecA(K3020001), RecA(K629001) with different intensity of UVB and UVA lights exposure for six minutes. Data is collected three hours after the treatment.

![](https://static.igem.wiki/teams/4814/wiki/lab/first-reca-test-h2o2.jpg)

Figure 2. The fluorescence of RecA(K3020001)-EGFP, RecA(K629001)-EGFP with different concentrations of H2O2. Data is collected three hours after the treatment.

We calculated the standard error (SE), with n=3:

![](https://static.igem.wiki/teams/4814/wiki/lab/se.png)

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

### Learn 1.1

1. After conducting the tests, as the fluorescence decreases when exposed to higher concentration of carciogen, we discovered the need to mitigate the potential fluorescence emitted by deceased cells. We improved the experiment by (1) normalizing the fluorescence using SFU (Test 1.2); (2) using cell imaging to obtain mean fluorescence of each bacteria. (Cycle 2)

2. We observed that our previous configuration, which involved utilizing all the wells of the plate, resulted in the occurrence of the "edge effect." This effect has the potential to significantly impact the standardization of the data (Mansoury, M. et al, 2021). The edge effect arises when there is a discrepancy in the rate of evaporation and aeration among the cultures within the multi-well plate (Ross, D., Tonner, P. D., & Vasilyeva, O. B., 2022).

### Build 1.2.1

Consequently, we restructured the arrangement of our samples within the 96-well plates to mitigate this issue.

##### Plate Rearrangement

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/before-rearrangement.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/after-rearrangement.jpg) | | |

Figure 3. The GFP emission fluorescence data before (left) and after (right) the rearrangement.

### Build 1.2.2

We conducted empirical investigations to further confirm the requisite conditions for the experiment.

##### Selecting time points for FL and OD measurements

After our first improved dosage dependent experiment of RecA(BBa\_K3020001)-EGFP and RecA(BBa\_K629001)-GFP, we plotted the change of the SFU of RecA(BBa\_K3020001)-EGFP over several time points: 1, 2, 3, 6, and 24 hours after the treatment.

As can be seen from the graphs, the SFU rises considerably when the dosage increases at the third hour (red points), which also confirms the data collecting time (2-3 hours after treatment) stated by team BIT 2019.

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/3d-k3-uvb.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/3d-k3-h2o2.png) | | |

Figure 4. The three-dimensional (time, concentration/ intensity, SFU) graph of RecA(BBa\_K3020001)-EGFP treated with UVB and H2O2.

### Test 1.2 and Result 1

##### Fluorescence over OD

As per the methodology described by team BIT 2019 (https://2019.igem.org/Team:BIT/Bio), we used SFU=(RFU/OD600) to normalize fluorescence. We compared the EGFP signal among different groups at the third hour. After comparing the data from three independent experiments, we observed a direct correlation between SFU and the intensity/concentration of the carcinogens, specifically UV and H2O2. In the aspartame group, both K3 and K6 exhibit a marginal increase in specific fluorescence units (SFU). This phenomenon can be attributed to the complex and indirect mechanism by which aspartame may potentially contribute to cancer development. Moreover, the performance of the K3 promoter was superior to that of the K6 promoter, indicating that the optimized K3 promoter was able to reduce background noise.

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/floverod-uvb.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/floverod-h2o2.png) | | |

Figure 5. The fluorescence of RecA(BBa\_K3020001)-EGFP, RecA(BBa\_K629001)-EGFP with different intensity of UVB and concentrations of H2O2. Data is collected three hours after the treatment.

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| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/floverod-ap.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/floverod-na.png) | | |

Figure 6. The fluorescence of RecA(BBa\_K3020001)-EGFP, RecA(BBa\_K629001)-EGFP with different concentrations of Aspartame and Nalidixic acid. Data is collected three hours after the treatment.

We calculated the standard error (SE), with n=3.

The results collected at the third hour indicates a more significant performance of K3 concerning the reoprting of DNA damages.

### Design 2.1

We sought guidance from our advisors, Prof. Leo Tsz On Lee and Prof. Tzu-Ming Liu from the University of Macau, to obtain a more robust approach for quantifying the fluorescence emission of each bacterium. They recommended employing cell imaging techniques utilizing microscopes.

### Build 2.1

##### Cell imaging and observation method improvement

Upon further investigation, we observed that bacteria containing the RecA-EGFP plasmid exhibited green fluorescence even without any treatment. Consequently, to obtain accurate and objective measurements of the GFP levels, we employed image processing software such as ImageJ. This enabled us to analyze the images and derive reliable results rather than relying on subjective assessments to determine the GFP quantities.

During our initial attempt to capture images of the bacteria, we used a direct placement method by placing droplets of bacteria onto a glass slide. However, the resulting images exhibited blurriness and cloudiness, as depicted in Figure 1. This undesirable outcome was attributed to the lack of lubrication oil application after each slide capture. Furthermore, due to the rapid movement of the bacteria within the culture, obtaining a clear and stable image of E.coli proved challenging. Consequently, the calculation of fluorescence measurements became a difficult task (July 14th).

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-h2o2-1mm-bf.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-h2o2-1mm-gfp.jpg) | | |

Figure 1. The bright field (left) and GFP channel image (right) of RecA(BBa\_K3020001)-EGFP E.coli treated with H2O2 1 mM.

During our subsequent attempt, we implemented several improvements to enhance the quality of our bacterial imaging. Firstly, we ensured the consistent application of lubricating oil and adjusted the microscope focus accordingly. Additionally, we introduced the use of agarose gel (0.1 g/ml) to stabilize the bacteria, yielding favorable outcomes with the bacteria ceasing their movement (Figure 3).

###### Here is the revised procedure:

1. Add 1g of agarose into 10 mL MilliQ water;
2. Microwave the mixture until all the powder dissolves;
3. Allow the gel to cool down to a touchable temperature and then pour it onto a sterilized plastic surface treated with 75% alcohol;
4. Cover the gel with another sterilized plastic sheet and store it in a refrigerator at 4 degrees Celsius for 2-3 minutes;
5. Cut the gel into 4-8 pieces and place them on the glass slide;
6. Add 4 μL of bacterial culture onto the gel and wait for the liquid to dry (approximately 2-3 minutes);
7. Place a coverslip on top of the gel;
8. Invert the slide so that the side with bacteria faces the objective lens of the inverted confocal microscope;
9. Apply oil on the lens after observing each slide.

### Learn 2.1

We optimized our familiarity with the experimental procedure, leading to a reduction in the treatment duration and precise control of the post-treatment time, which was set at three hours.

Moreover, we discovered that bacteria carrying the RecA-EGFP plasmid exhibited inherent green fluorescence without any additional treatment. Consequently, we employed software such as ImageJ to process the images, enabling us to obtain reliable results rather than relying on subjective determination of GFP levels (July 20th)

![](https://static.igem.wiki/teams/4814/wiki/lab/cell-imaging.jpg)

Figure 2. The gel with bacteria is placed on the slide and covered with coverslip.

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| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/k6-no-treatment-bf-2.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k6-no-treatment-gfp-2.jpg) | | |

Figure 3. The bright field (left) and GFP channel image (right) of RecA(BBa\_K629001)-EGFP E.coli has no treatment.

### Design 2.2

Later, we conducted the first of our three independent experiments using confirmed conditions. We also used Image J to analyze our data.

### Build 2.2

It is evident that the disparity between group (c) and (d) (RecA K6 without H2O2 and with H2O2) is not highly significant. However, individual bacteria within group (b) (RecA(BBa\_K3020001)-EGFP treated with H2O2) exhibit noticeably higher fluorescence intensity compared to group (a) (no treatment). Nevertheless, due to the absence of a readily discernible difference, we opted to employ ImageJ software for calculating the mean values. (August 8th)

|  |  |  |  |
| --- | --- | --- | --- |
| RecA(BBa\_K3020001)-EGFP no treatment | | RecA(BBa\_K3020001)-EGFP H2O2 5mM | |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-nt-bf.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-nt-gfp.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-h2o2-5-bf.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-h2o2-5-gfp.jpg) |
| RecA(BBa\_K629001)-EGFP no treatment | | RecA(BBa\_K629001)-EGFP H2O2 5mM | |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/k6-nt-bf.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k6-nt-gfp.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k6-h2o2-5-bf.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k6-h2o2-5-gfp.jpg) |

Figure 4. Bright field and GFP channel images of (a) RecA(BBa\_K3020001)-EGFP group without treatment.
(b) RecA(BBa\_K3020001)-EGFP group treated with H2O2 5mM. (c) RecA(BBa\_K629001)-EGFP group without treatment. (d) RecA(BBa\_K629001)-EGFP group treated with H2O2 5 mM.

##### Image processing

In order to ensure a consistent and standardized approach to handling the images, we employed image processing techniques. Specifically, we utilized ImageJ software to convert the images into an 8-bit format and applied the RenyiEntropy model to modify the threshold. This particular model, known for preserving additivity in independent events and aligning with the axioms of probability due to its multilevel thresholding property, was selected after testing various models.

By implementing this method, we were able to effectively encircle all bacteria, including those with lower fluorescence levels. This approach prevents biased selection solely based on brighter E.coli and ensures fairness and high-quality results in our analysis.

|  |  |  |
| --- | --- | --- |
| Default | Mean | RenyiEntropy |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-h2o2-5-gfp-default.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-h2o2-5-gfp-mean.jpg) | ![](https://static.igem.wiki/teams/4814/wiki/lab/k3-h2o2-5-gfp-renyi.jpg) |

Figure 5. The pictures of applying different thresholds: Default, Mean, and RenyiEntropy.

In certain instances, E. coli tends to aggregate and form clusters of fluorescent units. To ensure accurate analysis, we exclusively considered objects whose area ranged from 100 to 3000 square units. After restricting the selection to this specific range, we generated box plots using the resulting data points. The raw images were captured using a 100x confocal microscope, utilizing the GFP channel (488). Subsequently, the images were processed using ImageJ, applying an 8-bit format and RenyiEntropy threshold auto-adjustment.

### Test 2.2 and Result 2

After confirming the imaging method, we decided to present both the SFU and cell imaging data. The imaging data will enhance the accuracy and assist us to judge the ability of K3 and K6 promoters.

##### Cell imaging results

As depicted in Figure 6, the fluorescence intensity of RecA(BBa\_K3020001)-EGFP bacteria exhibits an upward trend with increasing duration of UVB exposure. This observation suggests that UVB radiation induces the SOS response in bacteria. A similar pattern is observed in the other treatment groups, including H2O2, nalidixic acid, and aspartame. Through this experiment, we have successfully validated the efficacy of utilizing the RecA promoter and EGFP fluorescent protein combination for evaluating genotoxicity based on the extent of DNA damage.

The imaging results (Fig. 7) of the K6 group (E. coli with RecA(BBa\_K629001)-EGFP) indicate a lack of significant dosage-dependent response. This can be attributed to the enhanced performance of RecA(BBa\_K3020001)-EGFP, which has undergone sequence optimization. The fluorescence intensity in the UVB exposure groups ranging from 6 to 18 minutes remained relatively unchanged. Similarly, in the aspartame and nalidixic acid groups, the fluorescence exhibited minimal variations. Conversely, the H2O2 group displayed a slight decrease, but the overall changes were not significant.

![](https://static.igem.wiki/teams/4814/wiki/lab/k3-analyzed-group-1.png)

Figure 6. The images were taken with a confocal microscope using EGFP channel, exposure time 80.024 ms. (a) The graph demonstrates the fluorescent units of cells treated with UVB at intensity 20 for 6, 12, and 18 minutes.
All photos were taken 2-3 hours after the treatment. (b) Fluorescence of cells treated with H2O2 at 0.05, 0.5, 5 mM. (c) Fluorescence of cells treated with nalidixic acid at 1, 10, and 100 ug/ml concentration. (d) Fluorescence of cells treated with Aspartame at 0.25, 0.5, and 1 ug/ml concentration.

![](https://static.igem.wiki/teams/4814/wiki/lab/k6-analyzed-group-2.png)

Figure 7. RecA(BBa\_K629001)-EGFP group: The images were taken with a confocal microscope using an EGFP channel, exposure time of 80.024 ms. (a) The graph demonstrates the fluorescent units of cells treated with UVB at intensity 20 for 6, 12, and 18 minutes.
All photos were taken 2-3 hours after the treatment. (b) Fluorescence of cells treated with H2O2 at 0.05, 0.5, 5 mM. (c) Fluorescence of cells treated with nalidixic acid at 1, 10, and 100 ug/ml concentration. (d) Fluorescence of cells treated with Aspartame at 0.25, 0.5, and 1 g/ml concentration.

All figures above have one-way ANOVA significance p < 0.001, with significance comparison (with No Treatment) indicated by: ns = no significance; \* = p < 0.05; \*\* = p < 0.01; \*\*\* = p < 0.001; \*\*\*\* = p < 0.0001.

### Design 3

We aimed to further improve the reporter by replacing RBS B0034 (our composite part design) with RBS B0032 and new RBS strong. In order to evaluate different compositions of RecA-RBS-EGFP and enhance the design of RecA-EGFP by substituting the RBS region, we conducted a tests that target different RBSs.

### Build 3

The provided charts display the SFU of the DNA damage reporter with various RBS sequences, namely Strong ([BBa\_K4814013](http://parts.igem.org/Part%3ABBa_K4814013)), B0032 ([BBa\_K4814002](http://parts.igem.org/Part%3ABBa_K4814002)), and B0034 ([BBa\_K4814015](http://parts.igem.org/Part%3ABBa_K4814015)). The original design of RecA(BBa\_K3020001)-EGFP (K4814015) incorporated the B0034 RBS. In our study, we replaced B0034 with B0032 and [Strong, a new RBS generated through machine learning analysis by Zhang, M. et al (2022)](https://pubs.acs.org/doi/10.1021/acssynbio.2c00015). In this study, a machine learning model was developed to predict the translation initiation rate of different RBS sequences. Then, the designed sequences were synthesized and experimentally tested for their translation initiation rates.

### Test 3 and Result 3

We compared the fluorescence normalized to OD600 when exposed to genotoxic agents such as UV, H2O2, Aspartame, and Nalidixic acid.

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/rbs-uvb.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/rbs-h2o2.png) | | |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/rbs-ap-1.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/rbs-na-1.png) | | |

Figure 1. The graphs of different RBS designs treated with UV, H2O2, Aspartame, and Nalidixic acid.

We calculated the standard error (SE), with n=3.

### Learn 3

In the case of aspartame, the SFU of all engineered bacteria decreases as the concentration of Aspartame increases. On the other hand, in the nalidixic acid group, the SFU of both the Strong and B0034 designs initially increases from 0 to 10 μg/ml, followed by a sharp decline between 10 and 100 μg/ml.

##### References

Zhang, M., Holowko, M. B., Zumpe, H. H., & Ong, C. S. (2022). Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site. ACS Synthetic Biology, 11(7), 2314-2326. <https://doi.org/10.1021/acssynbio.2c00015>

[https://parts.igem.org/Part:BBa\_B0032](https://parts.igem.org/Part%3ABBa_B0032)

## References

---

Yılmaz, S., & Uçar, A. (2014). A review of the genotoxic and carcinogenic effects of aspartame: does it safe or not?. Cytotechnology, 66(6), 875–881. Yılmaz, S., & Uçar, A. (2014). A review of the genotoxic and carcinogenic effects of aspartame: does it safe or not?.
Cytotechnology, 66(6), 875–881. <https://doi.org/10.1007/s10616-013-9681-0>

Mansoury, M., Hamed, M., Karmustaji, R., Al Hannan, F., & Safrany, S. T. (2021). The edge effect: A global problem. The trouble with culturing cells in 96-well plates. Biochemistry and Biophysics Reports, 26, 100987. ISSN 2405-5808. <https://doi.org/10.1016/j.bbrep.2021.100987>.

Ross, D., Tonner, P. D., & Vasilyeva, O. B. (2022). Method for reproducible automated bacterial cell culture and measurement. Synthetic Biology, 7(1), ysac013. <https://doi.org/10.1093/synbio/ysac013>

#### 3D Protein Docking and Modeling

---

Cycle 1: The Initial Approach
Cycle 2: Alternative Approaches
Results

##### Design 1

---

We obtained the structures of the fluorophores (eGFP, mCherry, eCFP, YFP) and their respective DNA damage response (DDR) proteins (ATRIP, RPA1) from Protein Data Bank (PDB). By docking them together, we plan to gain insight into their arrangements, their respective feasibilities, and their binding configurations, and then dock the bound structures together to further our understanding.

##### Build 1

---

###### Using ZDock to dock fluorescent proteins with their ATRIP and RPA

At first, we planned to use Discovery Studio’s function ZDOCK to predict the protein conformations. ZDOCK is an algorithm that predicts the structures of protein complexes by leveraging individual protein structures. It also takes into account critical factors such as Pairwise Shape Complementarity (PSC), Desolvation (DE), and Electrostatics (ELEC) to calculate and rank the docking[1].

Below are the equations of the scoring function[1]:

![](https://static.igem.wiki/teams/4814/wiki/modelingz2.png)

##### Test 1

---

The results of our attempt are represented in the table below:

| Receptor | Ligand | ZDock Score |
| --- | --- | --- |
| ATRIP | EGFP | 25.02 |
| RPA | mCherry | 19.74 |
| ATRIP | ECFP | 27.54 |
| RPA | EYFP | 17.62 |

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/atripegfpzdock.png)   Figure 1. ZDOCK results of ATRIP-EGFP | ![](https://static.igem.wiki/teams/4814/wiki/rpamcherryzdock.png)   Figure 2. ZDOCK results of RPA-mCherry | | |
| ![](https://static.igem.wiki/teams/4814/wiki/atripecfpzdock.png)   Figure 3. ZDOCK results of ATRIP-ECFP | ![](https://static.igem.wiki/teams/4814/wiki/rpaeyfpzdock.png)   Figure 4. ZDOCK results of RPA-EYFP | | |

We then docked RPA-1 with ATR-ATRIP, intending to combine the result with the fluorophore-receptor docking result for further analysis:

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/atratriprpazdock.png) | ![](https://static.igem.wiki/teams/4814/wiki/atratriprpazdock-1.png) | | |

Figure 5, 6. Left: ZDock results of ATR-ATRIP complex-RPA. Right: Graph of cluster, density, and, ZDock score, where each point represents a proposed structural configuration generated by ZDock.

In this approach, six structures from PDB were used:

1. 5YZ0: ATR-ATRIP complex
2. 2B29: RPA
3. 4EUL: EGFP
4. 4ZIO: mCherry
5. 5OX8: ECFP
6. 1YFP: YFP

Results were yielded consisting of clusters, cluster sizes, and ZDock scores. ZDock scores are generated from a scoring algorithm that ranks each pose based on the scoring function mentioned above; the higher the score, the more feasible the pose. In the results of RPA1-ATRATRIP, there is a general downward trend in the number of clusters as ZDock scores increase. This correlation could have been more conclusive evidence to prove our system was effective and efficient, however. Also, after extensive reading in the relative field, we recognized flaws in the previous approach: using the whole existing complex to model is not realistic; it does not complement our vector design, which only includes ATRIP, and the fluorescent proteins were not attaching to the sites we had intended them to. Simply docking the PDB files to form our engineered protein, instead of predicting our own proteins’ structures, neglects the possibility of any structural deformation due to these special circumstances, as FRET and fluorophore-tagged proteins are not naturally occurring.

##### Improve 1

---

###### Consultation with Bioinformatics Scientist

After consulting Bioinformatics Scientist Mr. Edison Ong from Moderna, he advised us to apply an alternative approach and use a different set of software tools to achieve our goals. The recommended method included predicting the protein structure, validating it with existing proteins, docking the structures, and measuring the docked molecular separation. Subsequently, we begin with predicting how the peptide chains from our designed sequences will fold into proteins. At the same time, we found out that the PDB file of RPA-1 we used in Cycle 1.0 does not contain the protein-interacting domain(N-terminal) but only the DNA binding domain(dbd-A, dbd-B, dbd-C). After correcting the sequence, we subsequently began by predicting how the peptide chains from our designed sequences would fold into proteins.

##### Design 2

---

###### Preparation: Alphafold protein structure prediction

In order to give an accurate representation of the proteins expressed from our vectors, we obtained the amino acid sequences of the involved proteins from Vectorbuilder, the platform where our vectors were designed. Given the lack of known protein structures for our target proteins, ATRIP and RPA1, we employed AlphaFold[2][3] for the prediction of their 3D models. To initiate the process, we utilized the modified amino acid sequence of each protein and subjected it to AlphaFold's simulation.

The sequences used for this prediction are listed below in the format of RefSeq Transcript ID-linker-Marker:

* EGFP-ATRIP: (NM\_130384.3)-3xGGGGS-EGFP
* mCherry-RPA1:(NM\_002945.5)-3xGGGGS-mCherry

![](https://static.igem.wiki/teams/4814/wiki/alphafold1.png)

Figure 7. Pre-Residue Count

![](https://static.igem.wiki/teams/4814/wiki/alphafold2.png)

Figure 8. Confidence Score

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/alphafold3spin.gif) | ![](https://static.igem.wiki/teams/4814/wiki/alphafold4spin.gif) | | |

Figure 9, 10. AlphaFold predictions of RPA1-mCherry(left), ATRIP-EGFP(right)

###### Verification: Using PyMOL to verify AlphaFold's outcomes

To ensure the predicted structure’s accuracy, we aligned AlphaFold’s prediction with existing structures from PDB and calculated the RMSD (Root Mean Square Deviation) of each part of the protein compared to the known structures, using PyMOL. The align function superimposes the provided predicted structure with existing structures and identifies their similarity. In simple terms, a large RMSD indicates an unstable and deviating predicted protein structure and a RMSD below 5 is considered ideal.

![](https://static.igem.wiki/teams/4814/wiki/rmsd.png)

Figure 11. PyMOL's analysis of our predicted structure of RPA-mCherry from AlphaFold and existing structures of RPA and mCherry.

Below are the RMSD values of RPA1-mCherry with two structures of RPA70 and mCherry. Codes in parantheses indicate the structure obtained from PDB.

|  | RPA1-mCherry |
| --- | --- |
| RPA70 N-term (2b29) | 0.369 |
| RPA70 dbd-A, dbd-B, dbd-C (4gnx-C sequence: dbd-A:181-290, dbd-B:301-422, dbd-C:436-616) | 0.494, 0.662, 0.958 |
| mCherry (4zio) | 2.581 |

Below are the RMSD values of ATRIP-EGFP with ATRIP and EGFP. Codes in parantheses indicate the structure obtained from PDB.

|  | ATRIP-EGFP |
| --- | --- |
| ATRIP(5yx0-C) | **43.318** |
| EGFP(6b7t-A) | 0.298 |

However, in PyMOL’s calculation of the RMSD of the predicted ATRIP-EGFP to ATRIP, the RMSD value was 43.318, which was extraordinarily high. Still, this is expected, as the reference structure of ATRIP is from an ATR-ATRIP complex not only ATRIP. However, our vector only contains ATRIP as the size of ATR-ATRIP would be too great to put into a single lentiviral vector. Therefore, The ATRIP’s structure will be slightly different from the one in the complex.

##### Build 2.1

---

###### Approach 2: ClusPro

We used ClusPro 2.0[4][5][6][7] next, utilizing its CPU, to perform molecular docking simulations. The docking calculations were carried out with hydrophobic-favored coefficients to enhance the accuracy of the results.

E = 0.40Erep +− 0.40Eatt + 600Eelec +2.00EDARS

A total of 30 models were generated through the docking simulations. Subsequently, we focused our analysis on the top 10 models with the highest scores. Remarkably, all of these models exhibited a range of FRET effectiveness within the distance range of 10-100Å, indicating that our designed bio-reporter system is feasible in terms of configuration and proximity.

##### Build 2.2

---

###### Cross-Examination with Approach 3: HDOCK

---

In addition to ClusPro, we opted for HDOCK to dock our predicted structures in hopes that the results from these simulations would be able to co-validate each other. In the case of any disparities in the outcomes of these two algorithms, it would be meaningful to compare the different scores, parameters and configurations that they may provide.

Each HDOCK model comes with two scores, a docking score and a confidence score: The docking scores are calculated by a knowledge-based iterative scoring function. More negative docking scores indicate more likely binding models. However, since the score has not been calibrated to experimental data, it should not be interpreted as the actual binding affinity of two molecules. The confidence score is determined based on the docking score and is designed to indicate the likelihood of binding between the protein-protein/RNA/DNA complexes. Generally, when the confidence score is above 0.7, the two molecules would be very likely to bind in this pose.[8] The calculation of the confidence score is defined as follows:

Confidence\_score = 1.0/[1.0+e0.02\*(Docking\_Score+150)]

##### Test 2

---

###### Using PyMOL to measure the result distances

Based on the literature review, FRET can be an accurate measurement of molecular proximity within the range of angstrom distances (10–100 Å)[9]. Using PyMOL, we analyzed the results of both ClusPro and HDOCK by calculating the angstrom distance between the fluorophores attached to ATRIP and RPA in the poses generated by these algorithms.

###### Results from Approach 2: ClusPro

---

After results were obtained, we focused our analysis on the top 10 models with the highest scores using PyMol. Remarkably, all of these models exhibited a range of FRET effectiveness within the distance range of 10-100Å, indicating that our designed bio-reporter system is feasible in terms of configuration and proximity.

![](https://static.igem.wiki/teams/4814/wiki/cp6.png)

Figure 12. ClusPro hydrophobic-favored model no.6, with an angstrom distance of 56.5

![](https://static.igem.wiki/teams/4814/wiki/cp7.png)

Figure 13. ClusPro hydrophobic-favored model no.7, with an angstrom distance of 97.4

###### Results from Cross-Examination with Approach 3: HDOCK

---

Below are the docking scores and confidence scores for the top ten models, which are ranked by their docking scores:

| Rank | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Docking Score | -252.39 | -239.71 | -231.76 | -228.53 | -221.35 | -217.78 | -217.14 | -215.96 | -214.32 | -213.43 |
| Confidence Score | 0.8857 | 0.8574 | 0.8369 | 0.8279 | 0.8064 | 0.7950 | 0.7930 | 0.7890 | 0.7835 | 0.7805 |

![](https://static.igem.wiki/teams/4814/wiki/h10.png)

Figure 14. The pose with the 1st highest score generated by HDOCK, with an angstrom distance od 132.5Å.

![](https://static.igem.wiki/teams/4814/wiki/h1.png)

Figure 15. The pose with the 10th highest score generated by HDOCK, with an angstrom distance of 12.7Å.

In comparison to ClusPro, the results in HDOCK displayed a wider spectrum, ranging from 12.7Å to 132.5Å.

It is important to emphasize that the scores given by ClusPro and HDOCK are not directly correlated with the distances determined by PyMOL; poses with higher scores do not necessarily indicate a larger or smaller degree of separation. However, these highly-ranked poses are more likely to form, so analyzing their distance is relatively meaningful as it encompasses a substantial portion of the poses generated.

##### Improve 2

---

In this investigation, we obtained results from ZDock, AlphaFold, then PyMOL, ClusPro, HDOCK, and finally PyMOL again. The results were generally in favor of our system design despite a few discrepancies, such as a high RMSD between ATRIP-EGFP and ATRIP(5yx0-C), and a large range of distances between EGFP and mCherry in the poses generated by HDOCK. After analyzing the results, we believe that further validation can be completed experimentally through western blotting, which can confirm the proper folding of the expressed proteins.

## References

---

[1] Chen, R., Li, L., & Weng, Z. (2003). ZDOCK: an initial-stage protein-docking algorithm. Proteins, 52(1), 80–87. <https://doi.org/10.1002/prot.10389>

[2] Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: Making protein folding accessible to all. Nature Methods, 19(6), 679-682. <https://doi.org/10.1038/s41592-022-01488-1>

[3] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A., Ballard, A. J., Cowie, A., Nikolov, S., Jain, R., Adler, J., Back, T., . . . Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589. <https://doi.org/10.1038/s41586-021-03819-2>

[4] Desta IT, Porter KA, Xia B, Kozakov D, Vajda S. Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure. 2020 Sep; 28 (9):1071-1081. <https://doi.org/10.1016/j.str.2020.06.006>

[5] Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE, Xia B, Hall DR, Kozakov D. New additions to the ClusPro server motivated by CAPRI. Proteins: Structure, Function, and Bioinformatics. 2017 Mar; 85(3):435-444. <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5313348/pdf/nihms834822.pdf>

[6] Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S. The ClusPro web server for protein-protein docking. Nature Protocols. 2017 Feb;12(2):255-278. <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540229/pdf/nihms883869.pdf>

[7] Kozakov D, Beglov D, Bohnuud T, Mottarella S, Xia B, Hall DR, Vajda, S. How good is automated protein docking? Proteins: Structure, Function, and Bioinformatics. 2013 Dec; 81(12):2159-66. <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934018/pdf/nihms556382.pdf>

[8]Yan, Y., Tao, H., He, J., & Huang, S. (2020). The HDOCK server for integrated protein–protein docking. Nature Protocols, 15(5), 1829-1852. <https://doi.org/10.1038/s41596-020-0312-x>

[9] Sekar, R. B., & Periasamy, A. (2003). Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations. The Journal of cell biology, 160(5), 629–633. <https://doi.org/10.1083/jcb.200210140>

{% endblock %}

{% 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?

---

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

---

* [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)

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results{% 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 %}
{% block cover\_content %}{% endblock %}
{% block page\_content %}

Here are the results of all parts:

* RecA(BBa\_K629001)-EGFP and RecA(BBa\_K3020001)-EGFP
* RBS test (Strong, B0032, and B0034)
* Hha biofilm reducer
* ATRIP-EGFP UV treatment
* ATRIP RPA1 FRET

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

---

RecA(BBa\_K3020001)-EGFP and RecA(BBa\_K3020001)-EGFP
RBS test (Strong, B0032, B0034)
Hha biofilm reducer
ATRIP-EGFP UV treatment
ATRIP RPA1 FRET

## RecA(BBa\_K3020001)-EGFP and RecA(BBa\_K629001)-EGFP

---

[RecA(BBa\_K629001)-EGFP (BBa\_K4814014)](https://parts.igem.org/Part%3ABBa_K4814014)

[RecA(BBa\_K3020001)-EGFP (BBa\_K4814015)](https://parts.igem.org/Part%3ABBa_K4814015)

To compare the EGFP signal among different groups and analyze the fluorescence relative to cell growth (OD600), we utilized the fluorescence over OD600 (FL over OD) ratio. Following the methodology described by team BIT 2019 (https://2019.igem.org/Team:BIT/Bio), we used specific fluorescence units (SFU = RFU/OD600) to quantify fluorescence. After comparing the data from three independent experiments, we observed a direct correlation between SFU and the intensity/concentration of the carcinogens, specifically UV and H2O2. In the aspartame group, both BBa\_K3020001 and BBa\_K629001 exhibit a marginal increase in specific fluorescence units (SFU). This phenomenon can be attributed to the complex and indirect mechanism by which aspartame may potentially contribute to cancer development. Moreover, the performance of the BBa\_K3020001 promoter was superior to that of the BBa\_K629001 promoter, indicating that the optimized BBa\_K3020001 promoter was able to reduce background noise.

The data demonstrates the RecA(BBa\_K629001)-EGFP construct’s inferior performance in reporting DNA damage compared to RecA(BBa\_K3020001)-EGFP across all treatment groups (see Fig. 1-2). Notably, in the aspartame group, the SFU of RecA(BBa\_K3020001)-EGFP is over three times higher than that of RecA(BBa\_K629001)-EGFP.

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/floverod-uvb-v2.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/floverod-h2o2-v2.png) | | |

Figure 1. The fluorescence of RecA(BBa\_K3020001)-EGFP, RecA(BBa\_K629001)-EGFP with different intensity of UVB and concentrations of H2O2. Data is collected three hours after the treatment.

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/floverod-ap-v2.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/floverod-na-v2.png) | | |

Figure 2. The fluorescence of RecA(BBa\_K3020001)-EGFP, RecA(BBa\_K629001)-EGFP with different concentrations of Aspartame and Nalidixic acid. Data is collected three hours after the treatment.

We calculated the standard error (SE), with n=3:

![](https://static.igem.wiki/teams/4814/wiki/lab/se.png)

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

As depicted in Figure 3, the fluorescence intensity of RecA(BBa\_K3020001)-EGFP bacteria exhibits an upward trend with increasing duration of UVB exposure. This observation suggests that UVB radiation induces the SOS response in bacteria. A similar pattern is observed in the other treatment groups, including H2O2, nalidixic acid, and aspartame. Through this experiment, we have successfully validated the efficacy of utilizing the RecA promoter and EGFP fluorescent protein combination for evaluating genotoxicity based on the extent of DNA damage.

![](https://static.igem.wiki/teams/4814/wiki/lab/k3-analyzed-group-1.png)

Figure 3. Images were taken with a confocal microscope using EGFP channel with an exposure time of 80.024 ms. (a) The graph demonstrates the fluorescent units of bacteria treated with UVB at intensity 20 for 6, 12, and 18 minutes.
All photos were taken 2-3 hours after the treatment. (b) Fluorescence of cells treated with H2O2 at 0.05, 0.5, 5 mM. (c) Fluorescence of cells treated with nalidixic acid at 1, 10, and 100 μg/ml concentration. (d) Fluorescence of cells treated with Aspartame at 0.25, 0.5, and 1 μg/ml concentration.

The imaging results (Fig. 4) of the BBa\_K629001 group (E. coli with RecA(BBa\_K629001)-EGFP) indicate a lack of significant dosage-dependent response. This can be attributed to the enhanced performance of RecA(BBa\_K3020001)-EGFP, which has undergone sequence optimization. The fluorescence intensity in the UVB exposure groups ranging from 6 to 18 minutes remained relatively unchanged. Similarly, in the aspartame and nalidixic acid groups, the fluorescence exhibited minimal variations. Conversely, the H2O2 group displayed a slight decrease, but the overall changes were not significant.

![](https://static.igem.wiki/teams/4814/wiki/lab/k6-analyzed-group-2.png)

Figure 4. RecA(BBa\_K629001)-EGFP group: The images were taken with a confocal microscope using an EGFP channel, exposure time of 80.024 ms. (a) The graph demonstrates the fluorescent units of cells treated with UVB at intensity 20 for 6, 12, and 18 minutes.
All photos were taken 2-3 hours after the treatment. (b) Fluorescence of cells treated with H2O2 at 0.05, 0.5, 5 mM. (c) Fluorescence of cells treated with nalidixic acid at 1, 10, and 100 ug/ml concentration. (d) Fluorescence of cells treated with Aspartame at 0.25, 0.5, and 1 g/ml concentration.

A dot = the mean fluorescence value of a bacteria. Independent sample number = 3; at least 30 cells in each sample.

All figures above have one-way ANOVA significance p < 0.001, with significance comparison (with No Treatment) indicated by: ns = no significance; \* = p < 0.05; \*\* = p < 0.01; \*\*\* = p < 0.001; \*\*\*\* = p < 0.0001.

## RBS test of B0032 (BBa\_K4814002), Strong (BBa\_K4814013), and B0034 (BBa\_K4814015)

---

Confirming the excellence of BBa\_K3020001 reporter, we aimed to further improve the reporter by replacing RBS B0034 (our original design) with RBS B0032 and new RBS strong.

---

The following charts are the SFU (significant fluorescence units. fl over OD600) of DNA damage reporter
with different RBS (Strong [BBa\_K4814013](http://parts.igem.org/Part%3ABBa_K4814013), B0032 [BBa\_K4814002](http://parts.igem.org/Part%3ABBa_K4814002), and B0034 [BBa\_K4814015](http://parts.igem.org/Part%3ABBa_K4814015)). The RecA-EGFP design with B0034 RBS is the original design (BBa\_K3020001). We replaced B0034 with B0032 and a new RBS generated by machine learning results by Zhang, M. et al (2022). This experiment aims to select the best design by comparing the fluorescence over OD600 when exposed to genotoxic agents (UV, H2O2, Aspartame, and Nalixidic acid).

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/rbs-uvb.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/rbs-h2o2.png) | | |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/rbs-ap-1.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/rbs-na-1.png) | | |

Figure 1. The graphs of different RBS designs treated with UV, H2O2, Aspartame, and Nalidixic acid.

We calculated the Standard Error (SE) with n = 3:

![](https://static.igem.wiki/teams/4814/wiki/lab/se.png)

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

Overall, the SFU of all designs has an upward trend in UV and H2O2 groups. The results illustrate that while in both UV and H2O2 groups, the SFU of B0032 (BBa\_K4814002) is three times of B0034 (BBa\_K4814015), the Strong (BBa\_K4814013) is only half of B0034.

However, when it comes to Aspartame, the SFU of all engineered bacteria decreases when the concentration of Aspartame increases. On the other hand, the SFU of Strong and B0034 rises from 0 to 10 ug/ml of Nalidixic acid, followed by a steep drop between 10 and 100.

## Hha biofilm reducer

---

The hha protein [BBa\_K4814001](https://parts.igem.org/Part%3ABBa_K4814001) is encoded to reduce biofilm formation and is designed to target and kill bacteria upon activation of the RecA promoter in response to DNA damage. In this study, our objective was to examine the impact of increasing concentrations of carcinogens on the optical density (OD) of a bacterial culture.

Initially, we hypothesized that as the concentration of carcinogens increased, the OD would decrease due to the bactericidal effects of the activated hha protein. However, the experimental results revealed that despite the escalating level of carcinogen treatment, the OD did not exhibit a significant decrease compared to the control group (top10 or E. coli), nor did it demonstrate a discernible trend. This could be attributed to adaptive responses to stress or incomplete protein folding of the hha protein.

As illustrated in Figure 1, the OD of the TOP10 control group decreased slightly from 0.28 to 0.26, and then increased to 0.3 after 6, 12, and 18 minutes of UVB exposure. In contrast, starting at 0.24, the OD of the hha group showed a slight increase and then decreased to 0.22 after 18 minutes of UVB treatment. In the graph representing the H2O2 group (Figure 2), both the hha and TOP10 groups exhibited a decreasing trend in OD, consistent with our initial hypothesis.

To further investigate this phenomenon, future studies can assess the expression level of the hha protein using techniques such as Western Blot or qPCR. These analyses will provide insights into the protein's expression patterns and help elucidate the underlying mechanisms contributing to the observed OD changes.

|  |  |  |  |
| --- | --- | --- | --- |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/hha-uvb.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/hha-h2o2.png) | | |

Figure 1 & 2. The graph of the OD600 of RecA(BBa\_K629001)-hha after being treated with UVB (left) and H2O2 (right).

| ![](https://static.igem.wiki/teams/4814/wiki/lab/hha-na.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/hha-ap.png) | | |

Figure 3 & 4. The graph of the OD600 of RecA(BBa\_K629001)-hha after being treated with Nalidixic acid (left) and Aspartame (right).

We calculated the standard error (SE), with n=3.

![](https://static.igem.wiki/teams/4814/wiki/lab/se.png)

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

## ATRIP-EGFP UV treatment

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ATRIP and RPA1 are crucial proteins involved in DNA damage signaling pathways within human cells. Once the RPA protein binds to single-stranded DNA, it recruits the ATR complex by interacting with the ATRIP protein. To investigate this interaction and detect DNA damage events in living human cells, we employed a fluorescence resonance energy transfer (FRET) approach by fusing two fluorescent proteins, namely EGFP & mCherry and ECFP & EYFP. This allowed us to measure the rate of energy transfer and determine the ATRIP-RPA1 interaction.

The following designs were utilized in our study:

* ATRIP-EGFP [(BBa\_K4814006)](http://parts.igem.org/Part%3ABBa_K4814006)
* RPA1-mCherry [(BBa\_K4814007)](http://parts.igem.org/Part%3ABBa_K4814007)
* ATRIP-ECFP [(BBa\_K4814008)](http://parts.igem.org/Part%3ABBa_K4814008)
* RPA1-EYFP [(BBa\_K4814009)](http://parts.igem.org/Part%3ABBa_K4814009)

### Aggregation after UV treatment

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After exposing the cells to a UVB dosage of 100 J/m^2, we observed aggregation of the EGFP signal (Fig. 1 and 2). Interestingly, fluorescence was detected in both the Green and Red channels. It is important to note that the emission of GFP is dependent on its fluorescence spectra, as mentioned in studies by Sattarzadeh, A. et al. (2015) and Licea-Rodriguez, J. (2019). This fluorescence could potentially be attributed to GFP emitting at around 560 nm.

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| ![](https://static.igem.wiki/teams/4814/wiki/lab/human/atrip-gfp-uv-treatment-2-20230926.png) | ![](https://static.igem.wiki/teams/4814/wiki/lab/human/atrip-gfp-uv-treatment-20231004.png) | | |

Figure 1 (top) & 2 (bottom). The image of ATRIP-EGFP after UVB 100 J/m^2 exposure. Both tests showed clusters and aggregation of signal in green channel and red channel. (488 nm excitation)

## FRET (ATRIP-EGFP + RPA1-mCherry)

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After subjecting the cells to UVB treatment at a dosage of 100 J/m^2, we observed a change in the density of both EGFP and mCherry signals. When excited at 488 nm, we noticed that the EGFP signal became weaker following exposure to UVB. However, in contrast, the red fluorescence emitted by mCherry (with an emission range of 570-620 nm) intensified.

![](https://static.igem.wiki/teams/4814/wiki/lab/human/g-m-uv-treatement-20231004.png)

Figure 1. The image of ATRIP-EGFP (excited at 488 nm) + RPA1-mCherry (RPA1-mCherry transfected twice) (excited at 561 nm).

To enhance the accuracy and reliability of our data analysis, we utilized ImageJ software to precisely outline the cell nuclei present in the Green Channel (excited at 488 nm). This step ensured that we specifically selected cells that were transfected with EGFP, as depicted in Figure . We focused on GFP-emitting cells because we observed that the image captured in the 488 nm excited green channel did not completely overlap with the image in the 488 nm excited red channel. However, it is important to note that the 488 nm red channel fluorescence should correspond to GFP emission at approximately 560 nm. Therefore, the shape of the cells in the red channel should be identical to that in the green channel (as shown in Figure .). By choosing GFP-emitting cells, we aimed to reduce background noise and focus our analysis specifically on the G+M cells (cells expressing both ATRIP-EGFP and RPA1-mCherry), excluding cells expressing only RPA1-mCherry.

![](https://static.igem.wiki/teams/4814/wiki/lab/human/g-m-fret-image-treatment.png)

Figure 2. The image of green channel (left) and circled merged channel (green and red) of G+M excited at 488 nm.

### Statistical Analysis

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We calculated the ratio of FRET using the Red over Green (Channel 3 over Channel 2) ratio. When the ratio is bigger than 1, there is more red fluorescence in the cell. We can compare the ratio before and after UV treatment to determine whether FRET occurs.

To handle the non-linear nature of the data, we took the logarithm of the values with a base of 2, which brings the values onto a comparable scale.

In Figure 3, the data points in the UV- graph are divided into two groups, with a separation occurring at 0.1. We set this value as the cutoff point, indicating the presence of FRET when the data point exceeds 0.1.

In the UV+ graph, there is a noticeable distinction in the proportion of data points indicating FRET.

![](https://static.igem.wiki/teams/4814/wiki/lab/human/log2-red-green-ratio-distribution.png)

Figure 3. Distribution of the Log base 2 Red/Green data before and after UV.

The Red over Green ratio (Log\_2) showed an increase of more than threefold after UVB light treatment. This substantial increase indicates that there is energy transfer from GFP to mCherry, resulting in the emission of red fluorescence when exposed to UV light. This confirms the occurrence of FRET energy transfer.

To assess the significance of the relationship between the two categorical variables, we employed Fisher's exact test. This statistical test is suitable when dealing with small cell counts. When the two-sided p-value is less than 0.01, it suggests a significant association between the two groups. (MedCalc Software Ltd. Fisher, 2023)

The result of Fisher's exact test revealed a strong significance between the two groups (p-value = 0.00122178, p-value < 0.01), indicating a stastical significance.

The calculator we used:

<https://www.medcalc.org/calc/fisher.php>

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| ![](https://static.igem.wiki/teams/4814/wiki/lab/human/g-m-log-2-ratio.png)  Figure 15. Mean value (with error bar, technical sample number = 4, about 30 data points in each sample) of logarithms of the data with base 2 before and after UV. |
| ![](https://static.igem.wiki/teams/4814/wiki/lab/human/log2-fret-fishertest.png)  Figure 16. Fisher's exact test result: p value < 0.05. |

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