Engineering Success


Production of tumor antigen attached OMV through E.coli Top10 strain

The design of the outer membrane vesicle (OMV) protein outlined in figure 1 contains three major structural domains: vesicle surface protein C terminus domain ClyA, cancer cell antigen Adpgk domain, and mouse IgG Fc fragment for the purpose of immune-enhancing and presenting antigen.

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Fig1. Design of OMV protein producing part
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Fig2. Plasmid pET-28b-ClyA-Adpgk-mFC

In order to verify the expression of our OMV protein, a plasmid based on pET28b vector was constructed and used to transform to E.coli BL21 DE3. Plasmid was extracted from successful transformants and sequenced to check for fidelity. Different concentrations of IPTG were tested to determine the concentration required for induction and SDS-PAGE was used to detect the presence of OMV protein in supernatant of culture media, bacteria lysate and pellet. (experiment detail on: supplementary material: protocol 7,8,12).

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Fig3. SDS-PAGE gel

Repeated experiments were unable to detect the presence of the OMV through SDS-PAGE as shown in figure 3. Figure 3c shows weak band around 95kDa was observed in pellet of the bacterial lysate. This initially promising signal at the correct molecule mass. However, this signal was not reproducible and so insufficient.

Discussion with Prof Huang led to the development of an alternative detection technique with higher sensitivity. The plasmid was redesigned to include a HA (hemagglutinin) tag for Western Blotting and, an enhanced GPR reporter gene. The design of the redesigned plasmid is shown in figure 4.

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Fig4. OMV protein with HA tag and report gene and plasmid pET-28b ClyA-Adpgk-mFC-eGFP

E.coli BL21 DE3 were transformed with the redesigned pET28b vector and transformants were induced with 0mM - 2mM IPTG and screened for GFP expression. (experiment detail on: supplementary material: protocol 12).

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Fig5. Detection of OMV through eGFP (model result)

After induction at 37℃ for 4h a significant fluorescence signal was detected and is show in figure 5. The optimum IPTG concentration for expression was determined to be in the range of 0.4mM to 0.6mM. Figure 6 displays images of E.coli transformants expression enhance GFP.

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Fig6. Enhanced GFP expression of E.coli under fluorescence microscope

The signal of GFP florescent protein is indicative of OMV protein expression. It was decided to perform a western blot to confirm the weak band noted in figure 3c.

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Fig7. Western blot result using anti-HA tag antibody, after 37℃ 4h with 0.5mM IPTG (loading 20ul of each sample)

Figure 7 shows the western blot image confirming the band on Fig3c is from the OMV. It was determined that the whole system was working. (experiment detail on: supplementary material: protocol 9).


Control of OMV release through UV light

To overcome the immune tolerance caused by long-term antigenic stimulation, OMV released was controlled using the SulAp promoter (BBa_K518010), an optogenetic regulatory element that was previously used by our team in 2020.

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Fig1. Design of light control OMV release
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Fig.2 UV control OMV producing vector pSB1C3_sulAp_ClyA-Adpgk-eGFP

After transforming the vector into E.coli Top10 the bacteria were exposed to UV radiation (15mW/CM2) for 1min and tested for change of the florescence. (experiment detail on: supplementary material: protocol 13)

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Fig3. Change in Florescence of bacteria culture

The florescence of bacteria culture increases significantly after exposure to UV. It was decided to perform further testing through western blot. (experiment detail on: supplementary material: protocol 9)

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Fig4. Western blot image of bacteria after UV radiation

Redesign of part sulAp and reduce leaking expression

The high leaking expression of SulAp make us failed to reach the goal of constructing a UV induced bacteria lysis system in 2020( By redesigning the part sulAp, we significant reduce the leaking expression without a large affect on the promoting strength.

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Structure of dLexburner, consists of two LBS, using the same promoter element as sulAp
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Improvement of SulAp by our team this year

Detail information on:


The Deep Nero Network

The Nero Network was made and trained as a classification model, inputting the image (marked as X) and outputting the possibility distribution of the classes. More information regarding the code and the architecture is outlined on the modelling page and the Software tool page. The results are outlined below. The textbox below shows the classes arranged in this order

upper-gi-tract\pathological-findings\esophagitis-b-d 0
lower-gi-tract\therapeutic-interventions\dyed-lifted-polyps 1
upper-gi-tract\anatomical-landmarks\z-line 2
lower-gi-tract\pathological-findings\ulcerative-colitis-grade-2-3 3
lower-gi-tract\pathological-findings\ulcerative-colitis-grade-1-2 4
lower-gi-tract\pathological-findings\ulcerative-colitis-grade-0-1 5
upper-gi-tract\pathological-findings\barretts 6
upper-gi-tract\anatomical-landmarks\pylorus 7
upper-gi-tract\anatomical-landmarks\retroflex-stomach 8
upper-gi-tract\pathological-findings\barretts-short-segment 9
lower-gi-tract\pathological-findings\ulcerative-colitis-grade-1 10
lower-gi-tract\pathological-findings\ulcerative-colitis-grade-2 11
lower-gi-tract\anatomical-landmarks\ileum 12
lower-gi-tract\anatomical-landmarks\retroflex-rectum 13
lower-gi-tract\quality-of-mucosal-views\impacted-stool 14
lower-gi-tract\pathological-findings\polyps 15
lower-gi-tract\anatomical-landmarks\cecum 16
upper-gi-tract\pathological-findings\esophagitis-a 17
lower-gi-tract\pathological-findings\hemorrhoids 18
lower-gi-tract\therapeutic-interventions\dyed-resection-margins 19
lower-gi-tract\pathological-findings\ulcerative-colitis-grade-3 20
lower-gi-tract\quality-of-mucosal-views\bbps-0-1 21
lower-gi-tract\quality-of-mucosal-views\bbps-2-3 22

e.g. class 18 is the "lower-gi-tract\pathological-findings\hemorrhoids" class


Figure 1 shows the lightness of the pixel with index of [Row = r, Column = c] means the number of the sample been classified as a c-th class and it's acctually the r-th class According to the diagram, class 23 has a lot of wrong data, because class 23 is the class for unpredictables

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Fig.1 Heat map showing how the prediction is related

Figure 2 shows the same thing but less sophisticated. In this image, the lightness of the pixel with index of [Row = r, Column = c] therfore P({ {X \in Set(c) }|{\hat{y}\in Set(r)}} ) Where X is a sample from the sample space and \hat{y} is the prediction from the network

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Fig.2 Heat map showing how the prediction is related to classes in posibilities

To compare with other networks or the same network with different hyperparameters, or just curiosity, here are the metrics

accuracy_score accuracy_score recall_score f1_score
The model 0.7297880322641156 0.7297880322641156 0.976408583260133 0.8352745424292845

The results for this model are really decent and as a medical-purposed model. Because the higher the recall is, the safer it is, and therefore the 0.97 recall (which is very high and very good) is so satisfying.

To deploy the model in production, we have to add the corresponding timing information( which we wont have the data until we do the animal test, because endoscopys don't move the way capsules move) to it with RNNs or transformers. But it's clear enough to say the data will just be better because better data is fed in.