iGEM 2023 — Team nus-singapore

# Notebook

# Engineering

# Overview

We started with the aim of creating a high throughput workflow that can produce variational data and evaluate diametrically opposing characteristics.
We achieved this goal through our several DBTL Cycles. Using STARs as a case study, we successfully integrated the target and switch into a single-plasmid system that can generate a library of different sense-antisense pairs.
We also successfully designed a diametric selection marker that can screen cells for the mutually exclusive ON and OFF configurations of their RNA switch, thus capturing ON/OFF information.

# RNA Switch Single-Plasmid System

Our objective was to engineer a one-plasmid system where both the RNA switch and the target are linked on a single strand. This would ensure:
1. 1:1 balance in a single cell for an easier control of expression
2. Physical linkage of the RNA switch and target sequences to enable future creation of variance needed for data generation.
At this stage, we started by exploring the Toehold switch system and STAR system.

## Developing the Toehold Switch Single-Plasmid System

## Design

The single-plasmid system required the toehold switch fused to GFP (under the control of an aTc-inducible pTet promoter) to be joined with the target (under the control of an IPTG-inducible pLac promoter) on a single plasmid. Toehold switches were chosen for their high levels of orthogonality, modularity and specificity.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

The toehold switch and target were **successfully** integrated into a 300bp stretch of DNA on a single plasmid.

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

The **efficacy** of the system was quantified by measuring the expression of downstream GFP when both the target and switch were expressed. Results were compared against GFP expression under the pTet promoter, without the switch.
For further details on our Build and Test steps, [Click here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

However, it was determined that the toehold single-plasmid system **did not exhibit significant levels** of GFP expression. We learnt that toehold switches might not be best suited to be integrated into a single-plasmid system, and that the change of promoters could have detrimentally affected system functionality. Hence, we decided to shift to using STAR RNA switches.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

## Developing the STAR Single-Plasmid System

## Design

The single-plasmid system required the STAR antisense strand fused to GFP to be joined with the sense strand on a single plasmid. Learning from the previous DBTL cycle, we cloned the target and switch under the original constitutive promoters. STAR switches were chosen as they were **well-characterized**.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

The antisense and sense sequences were successfully integrated into a stretch of DNA on a single plasmid.

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

The cells containing the single-plasmid system had a high GFP output, but the level of fluorescence was only around half that of the two-plasmid system. We learnt that the single-plasmid system was successful, but that it could be  **further optimised**  to reach higher levels of expression.
For further details on our Build and Test steps, [Click here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

It was determined that our system activated a significant level of GFP expression when both the sense and antisense strand were expressed. This demonstrates that the STAR single-plasmid system works successfully, as we have designed. We will focus on STAR in our next cycle which involves the creation of oligo library and developing the one plasmid system for Golden Gate assembly.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

# Oligo Library Generation and developing the Single-plasmid System for Golden Gate Assembly

In the next cycle, we aimed to engineer a one-plasmid system where an oligo pool of unique sense-antisense sequences could be inserted into the plasmid.
This would enable the one-plasmid system to generate a library of different sense-antisense pairs, linking it to data generation.

## Developing the STAR Oligo Single-Plasmid System

### Next, we needed to see how the single-plasmid system fared when unique oligos were integrated into the plasmid.

## Design

After **functionality** of the single-plasmid system was confirmed, we created a **computational model** to design our oligo pool through random generation. The STAR oligo single-plasmid system required the oligo (consisting of the sense and antisense pairs) to be joined with the backbone on a single plasmid via Golden Gate Assembly. This system would be able to generate strand-wise variation.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

The oligo and the backbone sequences were successfully integrated into a single plasmid by our novel Golden Gate Assembly protocol.

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

The functionality of the system was tested by measuring the expression of downstream GFP when both the sense and antisense strand were expressed. Results were compared against GFP expression under the pTet promoter, without the antisense strand.
For further details on our Build and Test steps, [Click here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

It was determined that our system activated a significant level of GFP expression when both the switch and target were induced. This demonstrates that the unique oligo was successfully integrated into the single-plasmid system, as per our novel plasmid design and new Golden Gate protocol. It also showed that our system would be able to generate strand-wise variation.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

# Developing the Diametric Selection Marker

Our objective was to engineer a diametric marker that could directly screen for the ON/OFF characteristic in cells. This would enable us to capture ON/OFF information, despite the ON and OFF states being mutually exclusive.

## Characterizing SacB

### Firstly, we aim to characterize a negative selection marker.

## Design

The SacB gene was chosen as it is a well characterized negative selection marker.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

The sacB gene was fused to a protein linker which was fused to the C-terminal of GFP, and the fusion protein was placed under the control of the **IPTG inducible pLac promoter** .

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

SacB was characterized by measuring the growth of the cells and the expression of downstream GFP in differing conditions of **0%, 0.5%, 1%, 2.5%, 5%** of sucrose and **0, 25, 50, 100μM**  of IPTG over 6 hours.
For further details on our Build and Test steps, [Click here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

The negative selection marker component of our diametric marker is meant to screen against **leaky** variants by killing them in culture. We now have found the ideal concentration of sucrose to use, 0.5%, which would allow us to tune the level of 'acceptable' leakiness.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

## Testing Killing Mechanism of sacB

### Next, we tested the effect that intracellular sacB could have on a non-sacB producing population of cells.

## Design

Since the mechanism of sacB is not well understood, we aimed to find out if sucrose converted to levan in one cell affects the survivability of other cells. Cells expressing RFP were taken from our lab.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

Cells expressing GFP-sacB were co-cultured with cells expressing RFP.

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

Several cocultures were made, with different starting proportions of the 2 cell populations. All populations were exposed to either 100uM IPTG only, 1% sucrose only, both conditions or neither, and their growth tracked over 10 hours.
For further details on our Build and Test steps, [Click here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

It was determined that sucrose has a negative effect on cell growth, independent of sacB expression. We learnt that not only does sacB not negatively affect the growth of surrounding cells, it provides a growth advantage to non-sacB producing cells, which assists in our use of the marker to distinguish leaky and non-leaky RNA switches.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

## Testing SacB Functionality

### We aimed to test our screening workflow independently of the RNA switch, using the negative selection marker alone.

## Design

Three different plasmids were designed to give the permanently off sacB, permanently on sacB and inducible sacB characteristic. The sacB gene was chosen as it is a well characterized negative selection marker.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

The GFP-sacB inducible plasmid required sacB gene fused to a protein linker which was fused to the C-terminal of GFP, and the fusion protein was placed under the control of the IPTG inducible pLac promoter.

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

A mixture of cells containing the three plasmids were grown under differing conditions, in order to test if sacB would be able to negatively select for the appropriate plasmid.
For further details on our Build and Test steps, [Click here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

It was determined that the cells containing SacB were killed **the presence of sucrose**. This demonstrates that the SacB negative selection works successfully, as we have designed.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

## Testing LacI Mutagenesis

### We aimed to test our screening workflow independently of the RNA switch, using the pLac promoter as an example.

## Design

Our workflow for screening ON/OFF strength of the RNA switch should in theory be applicable to any switch that controls protein expression. Hence we focused our efforts on the pLac promoter, a well characterized and commonly used promoter in E.coli.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

Error Prone PCR was used to mutate the LacI gene and simulate a variety of switches with different **ON/OFF ratios** and **leakiness**

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

The effect of mutagenesis was tested by measuring the expression of GFP under the control of IPTG-inducible pLac promoter in the cells with mutated LacI. Results were compared against GFP expression of cells containing normal LacI.
For further details on our Build and Test steps, [Click here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D).

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

Different variants were shown to have different responses to IPTG, ranging from increased expression to increased leakiness or insensitivity to IPTG. Thus, we established that error-prone PCR could produce a variety of phenotypes to check our screening against. This also demonstrated that we could generate pair-wise variation.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

# Deep learning modelling

### Deep learning model has 2 directions. We looked into predictive and generative models.

### The first step was to look at Wasserstein Generative Adversarial Networks as it was suggested from human practices.

## Design

The generator's primary goal is to produce RNA switch sequences corresponding to a given target sequence. Our initial approach is to explore WGANs (Wasserstein Generative Adversarial Networks) and Transformers as the starting point for our model building process.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

The input dataset, sourced from 'A Deep Learning Approach to Programmable RNA Switches,' was processed, including filtering out null entries. A GAN was created with a generator and discriminator.

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

WGAN implemented with **gradient penalty failed to compile**. The technical issue prevented us from going forward with this model architecture.

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

Due to time and knowledge constraints, we shifted to a simpler Transformer architecture for RNA switch sequence generation. The Transformer model successfully generated accurate sequences, achieving **impressive error rates** of 0.06 for characters and 0.05 for words.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

## Concurrently, we looked into creating a Generalised Predictive Model for RNA interactions

## Design

In order to capture the RNA-RNA interactions directly, we took inspiration from Siamese-Bert, which creates identical copies of a language model that analyses each sequence. While its used for sequence similarity tasks, We adapted it for interaction modelling.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

A large dataset of 9 million RNA-RNA interaction was gathered from the RNAInter database(Kang et al., 2021) and a Siamese Language architecture was designed to analyse the sequences to predict binding activity. 80% of the data set is used for training and 20% is used for testing.

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

We tested using the data and using traditional metrics for regression tasks such as, **Pearson R and Mean Squared Error**.

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

**The model was a success at predicting binding!** It achieved a Pearson R value of 0.937 showing strong correlation and a Mean Squared Error of approximately 0.01.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

## Next on the predictive side, the model was finetuned specific RNA technologies

## Design

Given our generalised model worked, we then aimed to test it on specific downstream tasks. We used the Toehold On/Off ratio data.

![design](https://static.igem.wiki/teams/4850/wiki/engineering/design.png)

## Build

The model architecture remained the same but was used as a starting point for fine-tuning.

![build](https://static.igem.wiki/teams/4850/wiki/engineering/build.png)

## Test

The model was evaluated using the evaluation metrics used to assess the original papers. For the Toehold efficiency model, Pearson R and Mean Squared Error were the metrics.

![test](https://static.igem.wiki/teams/4850/wiki/engineering/test.png)

## Learn

Our results improved with a pearson r of 0.56 and a mean squared error of 0.056. From this, we validated our hypothesis that language data allowed for **finer prediction**  of RNA properties.

![learn](https://static.igem.wiki/teams/4850/wiki/engineering/learn.png)

# Experiments

Mouse over and click on the links!

### [Overlap PCR](https://dx.doi.org/10.17504/protocols.io.bp2l6x8zrlqe/v1)

### [DNA Cloning](https://dx.doi.org/10.17504/protocols.io.8epv5xnddg1b/v2)

### [Golden Gate Assembly](https://dx.doi.org/10.17504/protocols.io.kqdg3xknqg25/v1)

### [PCR Clean-Up](https://dx.doi.org/10.17504/protocols.io.ewov1q212gr2/v1)

### [Preparation of Agarose Gel](https://dx.doi.org/10.17504/protocols.io.5qpvo3o7xv4o/v1)

### [Preparation of Component Cells (10Beta E.Coli Strain)](https://dx.doi.org/10.17504/protocols.io.q26g7p5w9gwz/v1)

### [Plasmid Extraction (Plasmid Isolation)](https://dx.doi.org/10.17504/protocols.io.yxmvm3yq6l3p/v1)

### [DNA Isolation (Gel Clean-up)](https://dx.doi.org/10.17504/protocols.io.81wgbxnk3lpk/v2)

### [Polymerase Chain Reaction (PCR)](https://dx.doi.org/10.17504/protocols.io.261gedrndv47/v1)

### [Gel Electrophoresis](https://dx.doi.org/10.17504/protocols.io.n2bvj3dp5lk5/v1)

### [Colony PCR](https://dx.doi.org/10.17504/protocols.io.5jyl8pex8g2w/v1)

### [Error-prone PCR (Random Mutagenesis)](https://dx.doi.org/10.17504/protocols.io.rm7vzx57xgx1/v1)

### [Oligo Pool Resuspension and PCR](https://dx.doi.org/10.17504/protocols.io.8epv5xe3ng1b/v1)

### [GFP-sacB Characterization](https://www.protocols.io/view/gfp-sacb-characterization-261gedwxwv47/v1)

# Results

### **Objective of wet lab**

The objective of wet lab will be to generate large amounts of data characterizing the binding affinity of various pairs of RNA sequences.
The most straightforward way to quantify the binding affinity would be to build RNA switches.

![](https://static.igem.wiki/teams/4850/wiki/resullt/rna-switches.png)

RNA switches are characterized by the binding of a target RNA to the secondary structure of a switch RNA, altering the structure and triggering downstream translation.
Thus, we first had to establish a model RNA switch to work on.

### **Toehold Test**

### Experiment Objective

The first RNA switch we examined was the toehold switch, as it has been shown to be highly modular and specific(Green et al., 2014). As outlined in our description, we plan to use an oligo pool to generate pairwise variants of different complimentary RNA sequences.
This requires the target and the switch RNA to be physically linked on a single strand, whereas the toehold switches in literature have relied on expressing the switch and target from different plasmids.

![](https://static.igem.wiki/teams/4850/wiki/resullt/toehold-switch.png)

Thus, the first experiment carried out was to integrate the switch and target expression cassettes into a <300bp stretch of DNA on a single plasmid.

---

### Experiment Design

To begin, the toehold sequence specified in "Toehold Switches: De-Novo-Designed Regulators of Gene Expression" (Green et al.,2014) along with it's cognate target was designed as a single plasmid. Expression cassette for the target was placed under the control of an IPTG-inducible pLac promoter, while the toehold switch was placed under the control of an ATC-inducible ptet promoter.
These expression cassettes were placed back to back such that the target to the toehold spanned <300 bases, allowing different pairs of toeholds and targets to be ordered as a single oligo in future. The whole target and toehold switch sequences were ordered and synthesized by IDT and TWIST respectively and they were then assembled into a plasmid called TH01 as shown in the figure below.

![](https://static.igem.wiki/teams/4850/wiki/resullt/circuit-toehold.png)

It was expected that only after inducing both promoters would the downstream GFP be expressed, thus cells were cultured for 6 hours in selective LB media with no inducers, only IPTG, only ATC, or with the addition of both inducers.

---

### Experiment Result

![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/figure-1-cap.png)

Results show the cells exhibited similar levels of GFP under all four conditions. Since the sequences of the toehold switch and the target are identical to Green et al.(2014) , some other aspect of the 1-Plasmid system must have caused the expression to fail.
Green et al.(2014)used a constitutive promoter to express their target and switch. RNA switches may be highly sensitive to their secondary structure, it could be possible that swapping out the promoter altered the transcription start site and the 5' end of the RNA changed their secondary structure and prevented the target and switch from interacting.

---

### Conclusion

The experiment was unsuccessful in showing that a single-plasmid system would work for toehold switches. Hence, we decided to pivot to STAR as it was well characterised.
In the meantime, we focused on characterising the SacB negative selection marker, as we wanted a selection marker to select for the successful RNA switches.

---

### **STAR Single-Plasmid System Test**

### Experiment Objective

To try a different RNA switch, we examined the STAR system (Chappell et al., 2015).

![](https://static.igem.wiki/teams/4850/wiki/resullt/star-ill.png)

Similar to the toehold switch, STAR had only been tested on a 2-plasmid system. Thus, we tried a similar approach to integrate the switch and target into a single plasmid.

---

### Experiment Design

We obtained the two separate target and switch plasmids from the Addgene. As plasmid copy number and selection markers can greatly affect expression, we strove to stay as close as possible to the system outlined in Chappell et al. (2015). Thus, the target expression cassette was cloned into the plasmid harbouring the switch.
Learning from the previous experiment with the toehold switch, we cloned the target and switch under the constitutive promoters originally found in the paper. Cells were cultured with our new one plasmid system for 6 hours. As a negative control, strains either only containing the switch(plasmid S5) or the target(plasmid A5) were tested, and as a positive control the original 2 plasmid system(S5+A5) was used.

![](https://static.igem.wiki/teams/4850/wiki/newresult/1.png)

---

### Experiment Results

![](https://static.igem.wiki/teams/4850/wiki/newresult/replace/figure-2-replace.png)

Lack of expression observed from the individual switch and target was expected. Our 1-Plasmid System had shown increased expression compared to the negative controls, however, it was not as high as the original 2-Plasmid System, likely due to an altered ratio between the target and switch.

---

### Conclusion

We can conclude that the 1-Plasmid System was successful, but that it was also important to consider the ratio between the target and switch. Due to a lack of time, we did not focus on optimisation. Instead, we next moved on to the STAR Oligo 1-Plasmid System test.

---

### **STAR Oligo Single-Plasmid System Test**

### Experiment Objective

Now that the STAR system has been consolidated to a single plasmid, we wanted to design a plasmid that could generate a library of different sense-antisense pairs by accepting an oligo pool of various target and switch sequences.
The library generating system consists of two parts.

![](https://static.igem.wiki/teams/4850/wiki/newresult/variance-highlight.png)

Part 1 would be the oligo pool carrying all the variable regions. A python program was written to randomly generate pairs of complimentary switches and targets along with a unique barcode. This oligo pool was flanked with standardized BsaI golden gate restriction sites to allow for easy assembly.
Part 2 was a vector with restriction sites that could accept the BsaI digested oligo strands, such that a golden gate reaction could assemble the generated oligo pool into a library

---

### Experiment Design

Before ordering an oligo pool with pairwise variation, we first constructed the vector and a single instance of the designed oligo. Because a larger oligo would take longer to order, in the interest of time we tested it a scaled down version of the oligo library and vector, that would use a constant switch but with variable target sequences.

![](https://static.igem.wiki/teams/4850/wiki/resullt/star-1-plasmid-oligo-scale-down.png)

To test the assembly, we produced a PCR construct with a single target sequence but with the barcoding and restriction sites of our designed oligo pool. Golden gate assembly was carried out between our modified target sequence and the vector and trasnformed into E.coli.

---

### Experiment Results

![](https://static.igem.wiki/teams/4850/wiki/newresult/replace/figure-3-replacereplace.png)

After golden gate assembly and transformation, GFP of resulting oligo insert was shown to be not significantly different from the original 1 plasmid STAR system(checked using a t-test).

---

### Conclusion

This experiments shows that the addition of the golden gate sites and barcoding, the GFP expression is not affected. This affirms that the oligo pool can be ordered with the same design and assembly protocol.

---

### **STAR Oligo Pool Single-Plasmid System Test**

### Experiment Objective

Now that it had been established that the designed assembly was functional, we wanted to test whether a library could be created with an oligo pool of target sequences, and characterize how well preserved the variation of sequences would be in liquid culture. This is crucial as our deep learning model relies on data from a large amount of sequences, and a liquid, heterogenous population of cells is required for our proposed ON/OFF diamteric selection with sucrose and antibiotics.

---

### Experiment Design

An oligo pool of various target sequences was ordered according to our design. This library would be made to test **strandwise variation**, and thus a variety of sequences were generated by taking the cognate target of our current RNA switch, and introducing mismatches in varying amounts and position[insert list of variants with mismatches highlighted here, need a figure]
This oligo pool amplified with PCR and the same golden gate experiment was carried out and transformed into E.coli. After transformation, instead of spreading on an agar plate, transformants were inovulated into a 5mL liquid culture of selective LB broth.
A PCR reaction amplifying the oligo region was carried out after 1 hour, 2 hours, 3 hours and 24 hours. A FLO-FLG114 Flongle Flow Cells (R10.4.1) kindly sponsored by Oxford Nanopore was then used to do high-throughput sequencing and assess if the culture still maintained a large variety of sequences from the oligo library.

---

### Experiment Results

![](https://static.igem.wiki/teams/4850/wiki/resullt/nanopore-result-1.png)
![](https://static.igem.wiki/teams/4850/wiki/resullt/nanopore-result-2-replace.png)

Nanopore sequencing showed a variation of target sequences, even after several hours of culturing. This was important in demonstrating that the assembly preserved the different variants in the oligo pool, such that there would be a sufficient population of different phenotypes to undergo screening and sorting into different bins.

---

### Conclusion

Oligo pool assembly would produce a heterogenous culture, which would then provide a variety of sequences to link to GFP outputs of RNA-RNA interactions. This would allow us to produce a large amount of data to train the model on.

---

### **Inducible STAR system test**

### Experiment Objective

Now that it had been established that the oligo pools could be assembled into our vector to produce a variable library of different switch-target pairs in one culture, we wanted to swap out the constitutive promoter controlling the target RNA for an inducible one. This is key to being able to measure an ON/OFF ratio, which would be the main classifying metric for our model.

---

### Experiment Design

A new plasmid (IndP1) was cloned with the target placed under the control of the pTet promoter, thus we expected that the target would only be transcribed in the presence of ATC, unfolding the switch and producing GFP.

![](https://static.igem.wiki/teams/4850/wiki/resullt/indp.png)

---

### Experiment Results

![](https://static.igem.wiki/teams/4850/wiki/newresult/replace/figure-5a-replace.png)

Results showed that no expression was observed with or witout ATC compared to the system containing constitutive promoters. To perform a diagnostic, the plasmid was recloned without the the STAR terminator.
From the result, we could see that without the STAR harpin, GFP was expressed properly, indicating the specific change in the constitutive promoter controlling the target to the pTet promoter caused the switch to be insensitive towards the target.
A further test was conducted on the pTet promoter. It was cloned upstream of the GFP gene, and tested with and without aTc.

![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/figure-5b-with-cap.png)

As can be seen from the graph, GFP expression was successfully induced in the presence of ATC.

---

### Conclusion

We were unable to turn the STAR system to an inducible system for the measurement of ON/OFF ratio. We suspect that this may result from the same issue as the toehold switch, where changes in promoters change the overall transcript of the target, affecting it's interaction with the switch. This demonstrates that the system may not be as modular as we initially thought, and that the RNA target cannot be viewed separately from the promoter. More importantly, this further highlights the need for better tools to fully understand RNA-RNA interactions, as small changes such as transcription start site can cause criticial downstream failure for RNA tools if not properly characterized.

---

### **SacB Characterisation**

### Experiment Objective

As part of our comprehensive workflow to capture ON/OFF information, we developed the idea of a diametric marker that could directly screen for that characteristic despite ON and OFF states being mutually exclusive.
This marker would consist of a fusion of a negative selection marker, a positive selection marker and GFP.
To develop this tool, we first wanted to characterize a negative selection marker fused to GFP. We chose the sacB gene as a well characterized negative selection marker. In the presence of sucrose, sacB converts sucrose into levan, which is toxic to E.coli.

![](https://static.igem.wiki/teams/4850/wiki/resullt/sacb-mechanism.png)

---

### Experiment Design

The sacB gene was fused to the C-terminal of GFP, and the fusion protein was placed under the control of the IPTG inducible pLac promoter. We expected that varying levels of expression through different IPTG concentrations would give different responses to scaling sucrose concentrations.

![](https://static.igem.wiki/teams/4850/wiki/resullt/corrected-wet-lab-result-pic.png)

Cells were cultivated in 0%, 0.5%, 1%, 2.5%, 5% of sucrose and 0, 25, 50, 100μM of IPTG, and their OD600 as well as GFP fluorescence taken over a period of 6 hours to track cell growth.

---

### Experiment Results

![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/0iptg-hs-with-cap.png)
![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/25iptg-hs-with-cap.png)
![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/50iptg-hs-with-cap.png)
![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/100iptg-hs-with-cap.png)

GFP expression levels across different concentrations of IPTG without sucrose demonstrated that we would expect IPTG and marker expression to be positively correlated. GFP cannot be used as an accurate indicator in the presence of expressed marker as cell death will influence protein production.
As can be seen from Figure 6a, even at 0μM IPTG, growth curve of the cell deteriorates with increasing concentrations of sucrose. This can indicate
1) that there is significant leakiness of the pLac promoter, expressing enough marker to cause cell death even in the absence of the inducer; or
2) sucrose has a negative effect on cell growth independent of sacB.
In the presence of any concentration of IPTG, concentration of sucrose 1% and above caused immediate stunting of growth.
However, for 0.5% sucrose, all curves had a distinct peak shape, where cells grew for a period of time before entering a death phase with decreasing OD600. Importantly, the speed at which the population entered death phase was linearly correlated with the IPTG concentration.

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### Conclusion

The negative selection marker component of our diametric marker is meant to screen against leaky variants by killing them in culture. We now have found the ideal concentration of sucrose to use, 0.5%, which would allow us to tune the level of 'acceptable' leakiness. This acceptable leakiness can be quantified using the IPTG concentrations as a proxy.
For example, based on the graphs, allowing cells to grow in 0.5% sucrose for 2 hours would guarantee that surviving cells had constructs that had leakiness equivalent to or less than a pLac promoter's expression at 25uM IPTG, while growing it for only 1 hour would loosen the upper limit of leakiness, allowing any cell that had leakiness up to the equivalent of a pLac promoter's expression at 100uM IPTG to survive in the media.

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### **SacB Coculture Test**

### Experiment Objective

In order for the selection marker to be able to select for individual variants in a culture, it must directly link the surviviability of the cell to the variant. As the mechanism of sacB is not well understood, it is important that sucrose converted to levan in one cell does not affect the surviviability of other cells, i.e by levan crossing the cell membrane/leaked by lysed cells and killing cells that do not have leaky RNA switches. Thus, we conducted an experiment to verify the effect that intracellular sacB could have on a non-sacB producing population of cells.

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### Experiment Design

Cells expressing our sacB-GFP fusion protein under an IPTG inducible promoter were cocultured with cells producing RFP. Several cocultures were made, with different starting proportions of the 2 cell populations. All populations were exposed to either 100uM IPTG(to induce sacB-GFP production) only, 1% sucrose only, both conditions or neither, and their growth tracked over 10 hours.

![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/well-plate-design.png)

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### Experiment Results

![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/coculture-with-cap.png)
![](https://static.igem.wiki/teams/4850/wiki/newresult/replace/figure-8-hr.png)
![](https://static.igem.wiki/teams/4850/wiki/newresult/replace/figure-9-hr.png)

Figure 8 and 9 show the expression of RFP over time in each of the different conditions. As the OD600 cannot be used to directly differentiate the cells expressing RFP and the cells expresssing SacB-GFP, RFP was assumed to be directly proportionate with the amount of RFP-expressing cells.
From the condition without any additives, we can see that the amount of RFP produced over time is correlated with the seed proportion of RFP-expressing cells. From the condition where 1% Sucrose and 100uM IPTG are added, it can be seen that there is significantly more RFP expression independent of the seed proportion.

![](https://static.igem.wiki/teams/4850/wiki/newresult/large-figure-10.png)
![](https://static.igem.wiki/teams/4850/wiki/newresult/large-caption.png)

Figure 10 and 11 show the overall OD600 of each coculture seed proportion in each condition over 6 hours.  Across all conditons the addition of sucrose caused the OD to drop slightly, even for cultures that did not have any sacB-GFP producing cells.
As expected, the addition of sucrose and IPTG to cultures that were fully sacB-GFP expressing cells led to a collapse in OD600, similar to what was observed in the sacB-GFP characterization. However, for cocultures, the addition of both IPTG and sucrose saw a restoration of normal growth compared to the addition of sucrose only, except for cultures that were fully RFP-expressing cells.

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### Conclusion

As RFP-expressing cells continue to grow in 1% sucrose and IPTG, conditions that, from our data, evidently produce sufficient levan to kill the sacB-GFP producing cells, we can conclude that levan only negatively affects cells that sacB-GFP was produced in.
In addition, it seems that sucrose has a negative effect on cell growth, independent of sacB expression. This effect is relieved in the presence of IPTG and sacB-GFP producing cells. Our hypothesis is that sacB-GFP producing cells metabolize the sucrose in the media into levan, which is evidently kept intracellular. Even though this kills the sacB-GFP producing cells, it removes the sucrose from the media, alleviating the negative effect on other cells.
Thus, not only does sacB not negatively affect the growth of surrounding cells, it provides a growth advantage to non-sacB producing cells, which assists in our use of the marker to distinguish leaky and non-leaky RNA switches.

---

### **LacI Mutagenesis Test**

### Experiment Objective

Our workflow for screening ON/OFF strength of the RNA switch should in theory be applicable to any switch that controls protein expression. In order to save time, we developed our workflow in parallel to our attempts to create a library generation system for the STAR RNA switch.
To test our workflow independently of the RNA switch, we focused our efforts on the pLac promoter, a well characterized and commonly used promoter in E.coli.

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### Experiment Design

To simulate a variety of switches with different ON/OFF ratios and leakiness levels, we decided to conduct random mutagenesis on the lacI gene in the plasmid expressing IPTG inducible sacB-GFP, followed by gibson assembly back into the sacB-GFP expression vector. The resulting transformants were inoculated into liquid culture, and it was expected that the culture would contain a heterogeneous population of different lacI variants with different properties which could be screened by the sacB-GFP marker.

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### Experiment Results

Firstly, the result of the error-prone PCR was sequenced with FLO-FLG114 Flongle Flow Cells (R10.4.1) to verify that variants were being created.

![](https://static.igem.wiki/teams/4850/wiki/newresult/with-caption/figure-12-with-cap.png)

Gibson assembly was then carried out with the mixture of mutants and transformed into E.coli. Transformation was plated, and 10 colonies cultured and plasmid extracted. After sequencing, it was verified that transformations of this protocol all had different mutations in the lacI gene, and thus all 10 plasmids were cultured in varying concentrations of IPTG to judge their response.

![](https://static.igem.wiki/teams/4850/wiki/newresult/big-figure-13.png)

Different variants were shown to have different responses to IPTG, ranging from increased expression to increased leakiness or insensitivity to IPTG. Thus we established that error-prone PCR could produce a variety of phenotypes to check our screening against.

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### Conclusion

Experiment demonstrates that mutagenesis produces different phenotypes for the lacI protein, and they all have different responses to IPTG. This would allow us to use the various mutants of lacI as a base pool to test the capability of our diameteric selection marker to screen out the leaky variants of switches.

---

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## References

[1] Chappell, J., Takahashi, M. K., & Lucks, J. B. (2015). Creating small transcription activating RNAs. Nature Chemical Biology, 11(3), 214–220. https://doi.org/10.1038/nchembio.1737
​

[2] Green, A. A., Silver, P. A., Collins, J. J., & Yin, P. (2014). Toehold Switches: De-Novo-Designed Regulators of Gene Expression. Cell, 159(4), 925–939. https://doi.org/10.1016/j.cell.2014.10.002