iGEM 2023 — Team thrace


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## See our engineering success !

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## Introduction

Engineering success lies in the main principle of starting to build a synthetic biological system, a dry lab model or other analysis, a hardware or software project. It discloses the fundamental requirements that should be met in order for the engineering process to be more valuable, effectively implemented and foster interdisciplinary collaboration.

The Design, Built, Test, Learn cycle (DBTL) is a fundamental concept in engineering, particularly in the field of synthetic biology and bioengineering. This iterative process guides engineers and scientists in developing novel biological systems.

![](https://static.igem.wiki/teams/4989/wiki/athanasia-dbtl-2.png)

Ιn the "Design" phase, researchers conceptualize and plan their engineered biological systems, specifying the genetic components and functions required. The "Build" phase involves physically constructing the designed genetic constructs in the laboratory. In the "Test" phase, the functionality and performance of the engineered system are assessed, often through experimental testing. Finally, the "Learn" phase interprets the results, identifies areas for improvement, and refines the design for the next cycle.

The DBTL cycle is a powerful approach that not only accelerates the development of new biological solutions but also facilitates continuous learning and optimization, making it an indispensable tool in the field of bioengineering.

As a team from our very early steps in Igem’s synthetic biology project, we aimed to combine the engineering circle and integrated human practices in order to configure our project. Engineering success was accomplished in different aspects of our project such as the wet lab, dry lab, and our software tool. In this way, we made 3 interactive engineering circles that the progress of the one influences and potentially changes the progress of the other.

### WET LAB ASPECT

### DRY LAB ASPECT

### SOFTWARE ASPECT

### WET LAB

For implementing the DBTL cycle on our wet lab projects we separated our tasks into two different categories, always based on the gold medal criterion of creating a new improved part:

+ TESTING THE OLD PART BBa\_K1618021
+ TESTING THE NEW PART BBa\_K4989008

### **1ST CYCLE----Testing the old part**

### **DESIGN**

After we concluded with the conduction of bibliographical research about the butyrate-producing pathway we searched in the registry part about a part that can produce butyrate. Simultaneously, we designed our target ideal gene cluster and we compared it with the part that we found in the registry (BBA\_K1618021). We realized that it needed certain changes in order to comply with our needs for producing butyrate. Thus, we proceeded with an in-silico analysis of the sequence. Since it is a testing for the function of this sequence we did not make any changes.

Since we wanted the sequence to be synthesized and cloned into a vector, we deposited the sequence’s part to IDT as it is in order for it to be cloned into a GoldenGate vector. Then we would transform with this vector 3 Lactobacillus species and E.coli K-12. We didn’t design a different vector since we only aimed to test the part.

Of course, we had designed our experiment guidelines and steps and collected all of our reagents. Also, we recorded the results that we expected to see and the reasons behind them.

### CROSSROAD

This type of sequence was too big in size in order for it to be synthesized. Thus we needed to re-design it in gBlocks and add restriction sites on each of its edges to perform later a Golden Gate assembly.

### 1st RE-DESIGN

We performed an in-sillico analysis again and we “optimized” our sequence in order to not include any unwanted restriction sites of the endonucleases that we wanted to use. Also, in our sequences, we designed and added the target restriction sites in each of the two edges of the gBlocks. Then we performed an in-sillico-based digestion in order to see that everything was designed as wanted. Also, we needed to find a new vector for cloning.

### CROSSROAD

Since the vector Pmg36e was not available for utilization we decided to proceed with the pNZ8149 plasmid. This vector also gave a solution for cloning and testing the old part.

### **BUILD**

After IDT synthesized our gBlocks, we performed GoldenGate assembly in order to assemble the parts together. Then we proceeded to ligate this part with the pNZ vector and with this we transformed the competent cells of Lactobacillus and E.coli strains. This is how we built our microbial chassis in order for them to potentially express butyrate.

### **TEST**

We chose those clones that included the insert part, and cultured them in order to give them time to produce the corresponding portions of butyrate. When we measured, through HPLC, the concentrations of butyrate, we found out that there wasn’t any level of butyrate production in Lactobacillus and the average butyrate production in E.coli.

### **LEARN**

We analyzed those results and we discussed what we have learned if they verified our recorded predictions before conducting the experiment, and what we should do in order to produce butyrate.

Our 1st DBTL circle was performed and concluded successfully. We received very promising results since the first part wasn’t functional and we got the chance to re-design it based on the notes we made on why we expected the experiment to not work. Therefore we were reassured of the results that we awaited and we confirmed experimentally that our target host Lactobacillus rhamnosus GG does not produce butyrate with the old part.

### **2ND CYCLE----TESTING THE NEW PART**

### CIRCLE INTERSECTION

Based on the results from the second circle, we began the design of the new improved part.

### **DESIGN**

We wanted to design our new part in order to have some basic characteristics that resembled an old part just so we keep the general structure. We applied all of the changes in the genetic makeup and the regulatory characteristics and we ran the sequence through in-sillico analyses. Our goal was to synthesize this part inside the Pmg36e plasmid. So we included the Biobrick scars amongst the genes and the RBSs as Igem requires. Also, we had all the enzymes, the reagents, and the protocols ready for use.

### CROSSROAD

Since the vector Pmg36e was not available for utilization we decided to synthesize our sequence from IDT in a GoldenGate plasmid.

However the delay of the part delivery was prohibitive to order it, so we decided to re-design the sequence in different gBlocks.

### **1st RE-DESIGN**

We redesigned our part in sillico and we separated the parts safely with the appropriate type IIS restriction sites on the edges, for GoldenGate assembly. However, there were some technical issues with the sequence and due to the terminator and other parts of the sequences unfortunately we could not synthesize our part from IDT.

### CIRCLE INTERSECTION

The Pnz8149 vector was finally available from our lab so we had the ability to do the cloning on our own. We approached Twist and ordered our gBlock sequences which passed the technical control.

Unfortunately due to the big delay with the pMG36e plasmid synthesis and the difficulty that came up with our part’s order, we could not test eventually the new improved part. This was due to the fact that the parts from the Twist would be delivered just for days after the Wiki Freeze. So besides the design level, we could not go anywhere further in the DBTL cycle.

However, we represent below the rest of the DBTL cycle that could possibly take place.

### **BUILD**

After the obtainment of the parts, we performed the corresponding digestions for the GoldenGate assembly of the parts. Then the whole insert is cloned into the pNZ vector and with this we transformed the Lactobacillus species and the E.coli K-12 strain in order to create one chassis for the proof of our concept and 3 other chassis for our part’s characterization.

### **TEST**

Since we added the GFP molecule in our insert we could isolate the colonies with the recombinant plasmid that has our part, and culture it again in order to give time to the bacteria to grow and produce butyrate.

Then, we measured with HPLC the levels of butyrate and acetate, since the latter is explicitly related to the butyrate production.

### **LEARN**

At the learning level we could have two possible scenarios:

+ If the butyrate levels are high in comparison with our controls and the acetate levels are relatively low since it has been utilized, then our new improved part is working and our DBTL cycle is successful!
+ If we do not detect any butyrate levels or they are very low in comparison with our controls then our part did not function as we expected. Then a new DBTL cycle would start again with the design part stage and correct any possible omissions or mistakes. Besides the design of the part faults, the inefficient production of our part could be attributed to the built stage. Our strain “hosts” a new metabolic pathway that could unbalance the overall metabolism. Thus, it could be necessary for our organisms that would accept this plasmid to be genetically engineered directly to their genetic makeup in order to “redirect” some crucial pathways of the metabolism. This could be done with the deletion of a gene that encodes for an enzyme that “opens” up a new pathway from the utilization of acetyl-CoA.

We us a team decided to put all our efforts in, to test and characterize our new improved part, because we have a strong belief in its Design level. So we aiming to produce our results, build our chassis and evaluate our results, after the Wiki Freeze, in order to present them to in our Q&A session to the judges. Be sure that we would come up with some very exciting results!

### DRY LAB

To excel on every project on the dry lab level, it is equally important to apply the DBTL principles. The changes that arose at each level, came from experienced scientists and analyses in the field of bioinformatics.

### **DESIGN**

After reviewing available metabolic modeling software, we decided to use MICOM. MICOM is a Python package for the metabolic modeling of microbial communities. In this way, we wanted to design an in sillico microbial community that could resemble those of the gut flora and then potentially test any changes that could bring abnormalities to the microbial synthesis.

### **BUILD**

The construct of a community model is conducted from a list of input COBRA models and manages exchange fluxes between individuals and individuals with the environment.

### CROSSROAD

After discussing this software with the experienced bioinformaticist Haris Zafeiropoulos, PhD, he advised us not to use MICOM, as it does not fulfill our needs of observing the flux changes of SCFA's in gut microbiome through time. He advised us to design a bioinformatics workflow, to in-silico reconstruct the L.Rhamnosus GG genome and see the change in SCFA fluxes.

However, we did not have sufficient time to complete such a workflow. We decided to design another workflow, for the identification of bacteria species and enzymes differentially enriched with significant statistical differences. This way, we could propose possible enzymes affecting butyrate production to our wet lab, as candidates to be expressed in our potential vector system.

As the first engineering cycle wasn’t successfully fulfilled we proceeded with a new one, which was very important for the experimental designs of the wet lab.

### **DESIGN**

At first, our wet lab team was keen on expressing butyrate kinase through a vector system, as this enzyme is crucial to butyrate production and we found absence of it in the L. Rhamnosus GG genome through genome annotation.

### **BUILD**

We built a bioinformatics workflow, from bacteria cohort creation to genome annotation. Thus, we tested if there are other possible genes, with a potential role in the butyrate pathway.

### **TEST**

We found other enzymes, contributing to butyrate synthesis, way more enriched than butyrate kinase such as butyryl-CoA:acetyl-CoA transferase or butyryl-CoA Dehydrogenase.

### **LEARN**

Through bibliography, we studied the role of transferase and a possible in-vivo reconstruction on L.Rhamnosus GG with a sequential catalyzed protein reaction.

We compared the pros and cons of adding a kinase and a transferase. First, transferase is found to be more decreased in the decreased bacteria cohort from people with depression . It also utilizes the acetate that is already produced in high quantities from the Lactobacillus rhamnosus GG. This makes it the ideal pathway to add to our butyrate-producing metabolic pathway.

### SOFTWARE

Even in the process of software development, the DBTL cycle was fundamental, and the feedback that changed the cycles multiple times in order to have our ideal result came from our integrated human practices work.

### **DESIGN**

Our initial thought was to develop an app that could combine therapeutics with diagnostics. Thus, we decided to create a chatbot that could be available for the patient to answer questions and evaluate if the answers are pruned to be categorized as depressive. Then, if this is the case the chatbot will advise the user to visit a therapist for a psychological evaluation/consultation.

The tools used for this project are based on AI. More specifically its fundaments lie to neural networks that can classify the texts as depressive or non-depressive.

### **BUILD**

The whole code section was built in Flutter environment. The neural architecture was designed and trained using tensorflow.

### **TEST**

After the creation of the app we tested it and the results were very promising. We tried key phrases that we knew were classified as depressive and indeed the chatbot advised us to visit a therapist.

### **LEARN**

Since the results were those that we expected the app functioned as we planned. The only concerns were about its enrichment and the further development of the graphics.

### HUMAN PRACTICES INTERSECTION

### **1st RE-DESIGN**

After a presentation of our app to Assistant Professor of Nutrition and Dietetics Dr.Anastasiou he advised us to link the app with our project since the app provided only prompts for the patient to consult a therapist.

We included in our app the aspect of the diet. More specifically, we added the characteristic of tracking the user’s dietary habits and linked those with the concentration of the SCFAs. Simultaneously, we got valuable advice and a detailed dietary guide on food rich in SCFAs from our experienced Nutritionist, Elpida Papanikolaou.

### **1st RE-BUILD**

We developed a local database in order to keep track of the user’s dietary habits. This was implemented with sqllite.

### **1st RE-TEST**

We tested again our app, ensuring that both databases where functional (text and diet database).

### **1st RE-LEARN**

Since the application was effective we didn’t have to change and re-design any part of our application. However, after a meeting with the Group of Psychiatrists of the Society of Social Psychiatry P. Sakellaropoulos, new enhancements that should be introduced to the app came up, opening a new DBTL cycle.

### HUMAN PRACTICES INTERSECTION

### **2st RE-DESIGN**

Based on the meeting with the Group of Psychiatrists of the Society of Social Psychiatry P. Sakellaropoulos, we made some fundamental changes on the basis of the application. First of all we needed to change the source of the questionnaire that the chatbot uses for answering the texts given by the user. We included the QIDS-SR (Quick Inventory of Depressive Symptomatology) that is widely used in the field of depression detection and therapy.

Secondly, they recommended us to adjust the app in order to recommend nearby mental health specialists, provided that the text is classified as depressive. Also, we included the feature of the storage of previous conversation between the user and the chatbot. In this way the user will pick a date and the corresponding conversation will be shown.

### **2st RE-BUILD**

We redesigned and implemented the UI of the application. We changed the list of strings that where stored into the database as questions and we use the google API for maps in order to recommend nearby experts

### **2st RE-TEST**

After including those features we tested the app to see if the are incorporated into it. We tested the google api separately while we also printed multiple times the contents of the database to ensure that the new questions are stored.

### **2st RE-LEARN**

Since the application was effective we didn’t have to change and re-design any part of our application.

Lastly, after creating a questionnaire for the public on their opinion about our application, there were many concerns about the accuracy of the chatbox on discriminating the depressive from the non-depressive texts.

### HUMAN PRACTICES INTERSECTION

### **3st RE-DESIGN**

We collected a very large dataset with depressive or non-depressive texts from multiple scientific sources. In this way we tried to train our model more effectively in order for it to be more accurate.

### **3st RE-BUILD**

We redesigned the dense part of our neural architecture. We increased the dropout as the change on the training data resulted in some overfitting.

### **3st RE-TEST**

We checked the efficiency of our model using AUC and logloss metrics. We got better results. AUC was around 0.8 while logloss was around 0.3

### **3st RE-LEARN**

Since the application was effective enough we didn’t have to change and re-design the neural architecture.

In this way, we concluded with the development of DEPRETECT and we accomplished to excel every nuance of the application.

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## Experiments

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## Introduction

Our project mainly consisted of 2 experimental parts:

+ Finding the best SCFA
+ Increasing the concentration of this SCFA through a probiotic

We will describe below some basic information about our experiments and our results, the exact protocols we used can be found in the Lab Book.

## How did we choose to work with butyric acid?

After our bibliographical research, we found out that the two most important SCFAs for the gut-brain communication are acetate and butyrate.

Read more about the SCFAs in our Background Knowledge.

In order to identify which one of the two fatty acids would be the best fit for the needs of our own project, we performed our first experiment. As we couldn’t use animal models, we decided to make the compromise to use an enteric cell line, believing that this would provide a simulation of the gut. More specifically, our first experiment was based on the ability of some enteric cells to produce serotonin, as a response to the existence of SCFAs in their environment. This way, we thought that we could have some first data showing that the SCFAs increase the serotonin production (and as a result alleviate the depressive symptoms, as explained in the Project Description).

Initially, we planned to use the R1N14B cell line, a cell line exhibiting epithelial morphology that was isolated from the pancreas of a male rat. R1N14B cells are enterochromaffin cells that synthesise ∼95% of the body's total serotonin (5-HT). However, as our lab’s faculty was trained to use the Caco-2 cell line, after studying the corresponding bibliography and research recent papers, we adapted our project into using the Caco-2 cell line.

After successfully culturing a Caco-2 cell line, we added the two SCFAs in the following concentrations:

| First Component | Second Component |
| --- | --- |
| Acetate Low (AL): 10mM | Acetate Medium (AM): 20mM |
| Acetate High (AH): 30mM | - |
| Butyrate Low (BL): 0,5mM | Butyrate Medium (BM): 1mM |
| Butyrate High (BH): 1,5mM | Butyrate Extreme (BE): 2,5mM |
| ALBL: 10mM + 0,5mM | ALBM: 10mM + 1mM |
| ALBH: 10mM + 1,5mM | - |
| AMBL: 20mM + 0,5mM | AMBM: 20mM + 1mM |
| AMBH: 20mM + 1,5mM | - |
| AHBL: 30mM + 0,5mM | AHBM: 30mM + 1mM |
| AHBH: 30mM +1,5mM | - |

Our goal was to investigate whether butyrate, acetate, or their combination would be the most efficient for serotonin production. We measured the serotonin produced using a serotonin ELISA Kit (https://www.immusmol.com/shop/serotonin-elisa-kit-any-sample/). We ran the same experiment twice. However, the results coming only from the second experiment are presented below. In the first experiment, we used extremely high concentrations of SCFAs, and, as a result, the kit didn’t work properly.

#### Results:

![](https://static.igem.wiki/teams/4989/wiki/experiments-elisa.jpg)

The results from the serotonin ELISA

The analysis of our results indicated that among the two primary SCFAs we examined, acetate appears to play a slightly more significant role in influencing final serotonin levels. Notably, the maximum serotonin concentration is achieved when both acetate and butyrate are present at their highest concentrations, as shown in the plot below. More about the methods we used to analyze our results can be found in the Model.

![](https://static.igem.wiki/teams/4989/wiki/experiments-plot.png)

![](https://static.igem.wiki/teams/4989/wiki/plot2.png)

Barplots of produced serotonin for each SCFA combination. Color groups bars with the same butyrate dosages. Barplot plots mean value with sd error and assigns significance stars based on a t.test with the reference group (control) . Significance stars indications: (p value > 0.05) , \* (p value <= 0.05) , \*\* (p value <=0.01) , \*\*\* (p value <= 0.001) , \*\*\*\* (p value <= 0.0001). Note: B15A30 stands for butyrate concentration=1,5mM, acetate concentration=30mM, and stands for the same thing as AHBH. The same applies for all the abbreviations.

From the barplot above we can observe that:

+ Butyrate when administered with no acetate, causes less serotonin production than the control (and than acetate administrations).
+ For a standard butyrate concentration, increasing acetate tends to increase serotonin production. In some cases (B00 and B10) , a decrease is observed from A20 to A30.
+ The most efficient combination on serotonin production is B10\_A20
+ The B15\_A10 combination had almost the same serotonin production as the control.

## How do we plan to increase the SCFAs’ concentration?

As mentioned above, we found that the maximum serotonin concentration is achieved when both acetate and butyrate are present at their highest concentrations.

We chose to work with Lactobacillus rhamnosus gg, a commonly used probiotic. Lactobacillus rhamnosus gg normally produces acetate. So, our team’s idea was that, if we could manage to make this strain produce butyrate as well, we would achieve the maximum serotonin production when administering the probiotic. In order to achieve this, we genetically modified Lactobacillus rhamnosus gg, by inserting a plasmid containing the necessary genes.

This part of our experiments took place in the Laboratory of Biotechnology and Applied Microbiology.

We intended to run two separate experiments for the genetic modification of the Lactobacillus strain. In the first experiment, we would transform the cells using the old iGEM part (BBa\_K1618021), while in the second we would use our own new improved part (BBa\_K4989008). In this section there is no information about the parts, but you can read more about them in the New Improved Part section.

Although Lactobacillus rhamnosus gg is our probiotic strain, we also used E.Coli K12 to better characterize our parts.

We started off by measuring the concentration of the SCFAs normally produced by the two strains in a 24h culture, to use these measurements as a control. These values were determined by HPLC, using a Shimadzu chromatography system.

Afterwards, we proceeded to transform both of the bacteria strains. We cloned our insert in the pNZ8149 food-graded plasmid, using Golden Gate methods and enzymes. We also used IPTG to induce the expression of the old part, as it was controlled by a lac-promoter. After succesfully transforming both E.Coli K12 and L.rhamnosus gg with the plasmid containing the old part (conducted colony PCR to check the transformation) and cultured them for 24h, we measured their production of SCFAs as well, using HPLC.

Sadly, we faced many difficulties ordering the new part. We ordered it from two different suppliers multiple times, but we experienced many delays because of its length and complexity. As a result, the new part didn’t arrive on time and we are still working on this experiment. Although we won’t be able to have these results by the Freeze Day to upload them on our Wiki, we will continue our experiments and we will for sure share our updates with the judges in the judge Q&A during the Grand Jamboree!

We understand that our New Improved Part insert is considerably long, which is one of our major concerns. However, we have very carefully designed every part of our insert, so we are optimistic that it should work and we expect to observe an increase in the production of butyrate. In order to identify whether the strains have eventually been transformed with the plasmid, we have included a GFP protein sequence to our part.

#### Results:

![](https://static.igem.wiki/teams/4989/wiki/transformation1.png)

The results obtained from the HPLC of the bacteria strains that weren’t transformed.

We can see that before the transformation, as expected, there is production of acetate and lactate. E.Coli also produces a very low quantity of butyrate.

![](https://static.igem.wiki/teams/4989/wiki/transformation2.png)

The results obtained from the HPLC of the bacteria strains that were transformed with the plasmid containing the old part.

We can observe that there is no big difference when inserting the old part. This aligns with previous teams’ research, as the part didn’t work for them as well.

As we mentioned above, we still don’t have the results from the transformation with the plasmid containing our new improved part. However, what we are expecting to observe is green fluorescent cultures, producing high concentrations of lactate and acetate , along with a higher concentration of butyrate. We will be happy to discuss with the judges our results in the judge Q&A during the Grand Jamboree!

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## What should this page contain?

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* Clearly and objectively describe the results of your work.
* Future plans for the project.
* Considerations for replicating the experiments.

## Describe what your results mean

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* Interpretation of the results obtained during your project. Don't just show a plot/figure/graph/other, tell us what you think the data means. This is an important part of your project that the judges will look for.
* Show data, but remember **all measurement and characterization data must also be on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).** Otherwise these data will not be in consideration for any medals or part awards!
* Consider including an analysis summary section to discuss what your results mean. Judges like to read what you think your data means, beyond all the data you have acquired during your project.

## Project Achievements

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You can also include a list of bullet points (and links) of the successes and failures you have had over your summer. It is a quick reference page for the judges to see what you achieved during your summer.

* A list of linked bullet points of the successful results during your project
* A list of linked bullet points of the unsuccessful results during your project. This is about being scientifically honest. If you worked on an area for a long time with no success, tell us so we know where you put your effort.

## Inspirations

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* [2019 Newcastle](https://2019.igem.org/Team%3ANewcastle/Results)
* [2019 Munich](https://2019.igem.org/Team%3AMunich/Results)
* [2019 Tec Chihuahua](https://2019.igem.org/Team%3ATec-Chihuahua/Results)
* [2020 Aalto Helsinki](https://2020.igem.org/Team%3AAalto-Helsinki/Results)
* [2020 GreatBay SCIE](https://2020.igem.org/Team%3AGreatBay_SCIE/Results)
* [2020 Queens Canada](https://2020.igem.org/Team%3AQueens_Canada/Results)

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