iGEM 2025 — Team barcelona-ub

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{% block title %}Notebook{% endblock %}
{% block lead %}This serves as a chronological record of your team's progress throughout the season. It documents your daily activities, experiments, discussions, and decisions.{% endblock %}
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* [Notebook & Lab Protocols](#basic)

# Notebook & Lab Protocols

Documents to facilitate reproducibility

These documents contain the various protocols we have followed to develop our project as well as day-to-day descriptions of the experiments followed in the wet lab. They are detailed records of our activity that will help you master readthrough elements.

### LAB NOTEBOOK

Click to see the whole document

### LAB PROTOCOLS

Click to see the whole document

✕

✕

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# Engineering

Our engineering approach follows the iterative DBTL (Design-Build-Test-Learn) process to optimize synthetic biology solutions through systematic refinement and validation.

# Overview

Our engineering approach focuses on three key components that work together to create a robust and efficient system. Through multiple cycles of refinement, we've developed predictive models and experimental validations that enhance the performance and reliability of our biological constructs.

The DBTL process allows us to systematically improve our designs based on empirical data and computational predictions, leading to innovative solutions for real-world challenges. Each component undergoes rigorous testing and optimization to ensure maximum efficiency and stability.

# Engineering Components

### SCR-D Structure Prediction

Computational modeling of protein structures with limited sequence information

Explore Cycles

### Aptamer-Controlled SCR

Genetic regulation modulated by aptamer binding

Explore Cycles

### Development of Tadpole

Software tool for automatised semi-rational design

Explore Cycles

## Determine SCR-D's Structure - Cycles

### Cycle 1: SCR-D Structure Prediction

Structure Prediction with Bioinformatics Tools and Coevolutionary Analysis

Start DBTL Process

### Cycle 2: Coevolutionary Analysis of SCR

Manual Conservation Analysis, Automation, and Key Element Identification

Start DBTL Process

## Creation of an aptamer-controlled SCR - Cycles

### Cycle 1: SCR element

SCR-D as Switch Candidate, Selecting New SCR Element, and Choosing DIO2 SECIS

Start DBTL Process

### Cycle 2: Aptamer

Selecting Theophylline Aptamer and Choosing Theo-ON-5 Variant

Start DBTL Process

### Cycle 3: SCR riboswitch

Joining all the parts together to design a switch, using the model and the software.

Start DBTL Process

## Development of Tadpole - Cycles

### Cycle 1: Algorithm Refinement

Brute-Force Search, Genetic Algorithm (GA), and Clustering

Start DBTL Process

### Cycle 2: Deployment and Accessibility

Python Scripts, Streamlit Interface, Render Deployment, and Docker

Start DBTL Process

# DBTL Visualization

Scroll down to see the iterative DBTL process or use the navigation arrows

# References

1. Wang, X., Yu, S., Lou, E., Tan, Y.-L., & Tan, Z.-J. (2023). RNA 3D structure prediction: Progress and perspective. *Molecules, 28*, 5532. <https://doi.org/10.3390/molecules28145532>
2. Washietl, S., Pedersen, J. S., Korbel, J. O., Gruber, A. R., & F. P. A. (2005). RNAz: A program to detect functionally important RNA secondary structures in multiple alignments. *Nucleic Acids Research, 33*(10), 3209–3218. <https://doi.org/10.1093/nar/gki633>
3. Mariotti, M., Lobanov, A. V., Manta, B., Santes, M. E., Morales, P. E., & Gladyshev, V. N. (2016). Lokiarchaeota marks the transition between the archaeal and eukaryotic selenocysteine encoding systems. *Molecular Biology and Evolution, 33*(9), 2441–2453. <https://doi.org/10.1093/molbev/msw122>
4. Lorenz, R., Bernhart, S. H., zu Siederdissen, C. H., et al. (2011). Viennarna package 2.0. *Algorithms for Molecular Biology, 6*(1), 26. <https://doi.org/10.1186/1748-7188-6-26>
5. Mishler, D. M., & Gallivan, J. P. (2014). A family of synthetic riboswitches adopts a kinetic trapping mechanism. *Nucleic Acids Research, 42*(10), 6753–6761. <https://doi.org/10.1093/nar/gku262>
6. Touat-Hamici, Z., Bulteau, A.-L., Bianga, J., Lobanov, A. V., & Gladyshev, V. N. (2018). Selenium-regulated hierarchy of human selenoproteome in cancerous and immortalized cells lines. *Biochimica et Biophysica Acta (BBA) - General Subjects, 1862*(11), 2493–2505. <https://doi.org/10.1016/j.bbagen.2018.04.012>
7. Jenison, R. D., Gill, S. C., Pardi, A., & Polisky, B. (1994). High-resolution molecular discrimination by rna. *Science, 263*(5152), 1425–1429. <https://doi.org/10.1126/science.7510417>
8. Human Metabolome Database. (2025, June 8). Hmdb0001889: Theophylline. <https://www.hmdb.ca/metabolites/HMDB0001889>
9. Tang, W., Hu, J. H., & Liu, D. R. (2017). Aptazyme-embedded guide rnas enable ligandresponsive genome editing and transcriptional activation. *Nature Communications, 8*(1), 15939. <https://doi.org/10.1038/ncomms15939>
10. Lynch, S. A., Desai, S. K., Sajja, H. K., & Gallivan, J. P. (2007). A high-throughput screen for synthetic riboswitches reveals mechanistic insights into their function. *Chemistry & Biology, 14*(2), 173–184. <https://doi.org/10.1016/j.chembiol.2006.12.008>
11. Anzalone, A. V., Lin, A. J., Zairis, S., Rabadan, R., & Cornish, V. W. (2016). Reprogramming eukaryotic translation with ligand-responsive synthetic rna switches. *Nature Methods, 13*(10), 989–995. <https://doi.org/10.1038/nmeth.3807>
12. Menichelli, E., Lam, B. J., Wang, Y., et al. (2022). Discovery of small molecules that target a tertiary-structured rna. *Proceedings of the National Academy of Sciences, 119*(48), e2213117119. <https://www.pnas.org/doi/10.1073/pnas.2213117119>
13. Anzalone, A. V., et al. (2006). Engineered RNA reporters for gene expression in living cells. *Chemistry & Biology, 13*(12), 1259-1267. <https://doi.org/10.1016/j.chembiol.2006.12.008>
14. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in python. *Journal of Machine Learning Research, 12*, 2825–2830.
15. Streamlit Inc. (2024). *Streamlit: Turn data scripts into shareable web apps* (Version 1.35). <https://streamlit.io>

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Iteration 1 of 3

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{% block lead %}Cell-based assays in HEK293T: A complete weekly workflow{% endblock %}
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* [Organization](#organisation)
* [Experimental workflow](#experim-w)
  + [Common part](#common)
  + [Cell-based assays](#cell)
  + [Cell-free system assays](#cell-free)
  + [Other Procedures](#procedures)
* [Constructs used in the lab](#constructs)
  + [SCR-D](#scr-d)
  + [SECIS](#switch)

### Functional RNA Element (FRE)

RNA sequences that perform specific biological functions when folded, such as frameshift elements, ribozymes, or Stop Codon Readthrough[3] sequences.

### Semi-rational design

Semi-rational design uses rational guidance to narrow down the possibilities,
even if the exact behaviour cannot be exactly predicted, while still relying on experimental
testing of a manageable set of candidates.

### Conformational RNA Element (CRE)

RNA structures that can change conformation in response to stimuli, including Aptamer[4]s, miniRNAs, and temperature-sensitive motifs.

### SECIS Element

A highly conserved RNA hairpin structure that directs the ribosome to bypass UGA stop codons and incorporate selenocysteine into proteins.

### Aptamer

A short RNA or DNA molecule that binds specifically to a target molecule (similar to an antibody in function).

### Riboswitches

Structured RNA segments that directly bind small molecules and regulate gene expression without requiring proteins. They change conformation upon ligand binding.

### Model Section

Explanation of the workflow from Engineering design to Wet Lab experiments.

### HP CARD

At the **Discover-Glo conference** by **Promega**, we had the chance to talk to
**Irena Montiel** and **Eric Rovira**, who suggested using the
**dual-luciferase assay** to test our construct.
See [HP](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%20%7D%7D)
and [Engineering](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D)

# Experimental Overview

# Organization

The experimental work of our project aims to complete the various cycles described in the Engineering section. In the table below, each wet lab branch is associated with the engineering cycle it seeks to complete and provide experimental validation for.

| Engineering | Wet Lab |
| **Branch:** SCR-D structure prediction    **Cycle 2:** Identification of key structural elements      - Complete iteration 3 | **Branch 1:** SCR-D element |
| **Branch:** Creation of an aptamer-controlled SCR    **Cycle 1:** SCR element      - Complete iteration 3 | **Branch 2:** SECIS  **Branch 5:** SECIS Cell-free |
| **Branch:** Creation of an aptamer-controlled SCR    **Cycle 2:** Aptamer      - Complete iteration 2 | **Branch 3:** Aptamer  **Branch 4:** Aptamer Cell-free |
| **Branch:** Creation of an aptamer-controlled SCR    **Cycle 3:** Riboswitch      - Complete iteration 1 | **Branch 6:** Switch |

A detailed explanation of the workflow Engineering → Wet Lab can be found in the [Model Page](https://2025.igem.wiki/barcelona-ub/model).

# Experimental workflow

![General scheme of experimental workflow](https://static.igem.wiki/teams/5622/images/wetlab/generalscheme.webp)

General scheme of experimental workflow

In the laboratory, we mainly carried out two types of assays: **cell-based assays** and **cell-free system assays**. Both assays share a **common preliminary part**.

## Common part

The constructs arrived from the supplier in plasmid form. In order to amplify the plasmids, we performed a standard amplification procedure using **E. coli DH5α**:

1. **Transformation:** We carried out a bacterial transformation by heat shock of **E. coli DH5α** with the plasmids of interest, and plated the bacteria.
2. **Incubation:** The following day, we collected colonies from the plates and incubated them in tubes with **LB medium**.
3. **Plasmid purification:** One day later, we purified the plasmids of interest, obtaining the desired concentration.
4. **Verification:** Finally, to verify the efficiency of the procedure, we measured the concentration of the purified plasmids using a **Nanodrop**.

All plasmids underwent this process before being used in the assays.

## Cell-based assays in HEK293T

Starting from the purified plasmids, we performed the assays in HEK293T cells, with plasmids of SCR-D and SECIS. A complete assay takes one week, so we always began on Monday and finished over the weekend. The process is as follows:

## Weekly Experimental Timeline

From cell seeding to data analysis - a complete 7-day protocol

1

Monday

Cell Seeding

We seeded the cells at the desired density in 48-well plates. The cells were then incubated for 24 hours.

2

Tuesday

Transfection

We transfected the plasmids of interest (SCR-D and SECIS) into the cells using a lipofectamine-based system, followed by a 24-hour incubation.

3

Wednesday

Treatment

After preparing selenium and theophylline solutions, we treated the cells with selenium and/or theophylline and incubated them for 48 hours.

4

Thursday

Observation

We observed the cells under an optical microscope, solely to check that the process was proceeding correctly and that cell growth was taking place as expected.

5

Friday

Luciferase Assay

We performed cell lysis and dual-luciferase assay 💡(Dual-Luciferase® Reporter Assay System from Promega®, catalog #E1910), obtaining raw data on gene expression.

📊

Weekend

Data Analysis & Presentation

We dedicated this time to plotting and analyzing the results in RStudio, and prepared a presentation of the findings and conclusions to share with our PI and instructors.

This procedure shows variations across the different rounds carried out, since we have conducted an optimization process of the various variables and time points that influence the experiment. For more details about this process, please refer to the [Pivotal changes and milestones Page](https://2025.igem.wiki/barcelona-ub/pivotal-changes).

## Cell-free system assays

Starting from the purified plasmids, we performed the **cell-free system assays** (*TnT® Coupled Reticulocyte Lysate Systems* from Promega®, catalog #L1170), but in this case only **SECIS plasmids** and **SECIS-related plasmids** were tested. A complete assay takes one or two days. The process is as follows:

1. **Starting Material:** The starting point can be either a purified plasmid or a PCR product. We incorporated **Paul Copeland’s plasmid** in purified plasmid form. However, we performed a PCR on the SECIS plasmids to incorporate our construct as a PCR product, since in this form it was more stable. Therefore, the first step was to run a **PCR** and an **agarose gel electrophoresis**.
2. **Assay Setup:** Once we had the PCR product, we proceeded with the cell-free system assay. The different samples were treated with **selenium** and/or **theophylline**, which were added prior to the incubation period during which **transcription and translation** occurred simultaneously.
3. **Measurement:** Gene expression was measured through a **dual-luciferase assay**. Finally, we plotted and analyzed the results in **RStudio** and prepared a presentation to share with our PI and instructors.

## Other Procedures

* **Split:** This routine procedure in the team is used to replace the medium of **HEK293T cells** with trypsin, thereby ensuring their survival over time.
* **Toxicity assay:** This procedure involves seeding cells at different densities and treating them with increasing concentrations of **theophylline**, in order to establish toxicity thresholds.
* **Sanger sequencing verification:** To confirm the identity and integrity of the purified constructs, plasmid samples are sent for Sanger sequencing. The results are compared to the expected construct sequences to verify that they match the intended design.

More information about lab procedures and protocols can be found at the [Notebook section](https://2025.igem.wiki/barcelona-ub/notebook).

# [Constructs](https://2025.igem.wiki/barcelona-ub/parts) used in the lab

The constructs used in our laboratory are divided into two main branches, according to the SCR with which they are associated. Thus, we have constructs that were used for the SCR-D experiments and constructs that were used for the SECIS experiments.

![General Constructs](https://static.igem.wiki/teams/5622/images/wetlab/constructs.webp)

Classification of the constructs that we used in the lab

## SCR-D

#### A: Normal SCR-D (TGA)

This construct is the complete SCR-D along with the STOP
codon on which it must act to produce readthrough, allowing the
ribosome to bypass it. This way, Firefly luciferase will be expressed.

![Normal SCR-D (TGA)](https://static.igem.wiki/teams/5622/images/wetlab/tganormal.webp)

Normal SCR-D (TGA) construct and predicted structure

#### B: SCR-D controls

**1- CGA:** This construct has the first nucleotide of the STOP codon mutated
from T to C, so the codon is no longer a STOP codon but instead encodes an arginine.
In other words, each reading of the construct’s mRNA gives rise to both luciferase proteins,
Renilla and Firefly. This serves to normalize the activity of the two luciferases and to generate
cleaner and more reliable graphs.

![CGA construct](https://static.igem.wiki/teams/5622/images/wetlab/cga.webp)

CGA construct, a control for normalizing the activity of both luciferases

**2- TAAA:** This construct serves as a negative control for the
Dual-Luciferase Assay. In this case, the STOP codon was modified by changing
its central nucleotide (G → A) and adding one extra nucleotide immediately after
it, introducing a frameshift. As a result, even if the ribosome continues
translation beyond this point, the downstream Firefly luciferase gene will
be out of frame and cannot be correctly translated. Therefore, no Firefly
signal should be detected. This control ensures that any measured Firefly
activity in experimental constructs truly results from readthrough events
rather than from frameshifting or reinitiation.

![TAAA construct](https://static.igem.wiki/teams/5622/images/wetlab/taaa.webp)

TAAA construct, useful for ruling out reinitiation and other modes of Firefly
luciferase expression that are independent of SECIS-mediated readthrough.

**3- TGA postATG mutated:** This construct was designed to control for ribosomal
reinitiation. In the natural SCR-D sequence, there is an internal ATG start codon located
downstream of the stop codon. This ATG could allow the ribosome to detach and then
reinitiate translation, leading to Firefly luciferase expression that is not due to
true readthrough. To prevent this, the internal ATG was mutated in a way that preserved
the SCR-D RNA structure while eliminating the reinitiation site. As a result, any Firefly
luciferase produced from this construct must come exclusively from readthrough events.
By comparing this construct to the normal SCR-D (TGA) construct, we can quantify and
subtract the contribution of reinitiation.

![TGA postATG mutated construct](https://static.igem.wiki/teams/5622/images/wetlab/atgmutated.webp)

TGA postATG mutated construct, useful for ruling out reinitiation,
a mode of Firefly luciferase expression that is independent of SECIS-mediated readthrough.

#### C: SCR-D parts

**1- Only pre-diapin:** This construct contains only the
Pre-diapin region of SCR-D, and it is used to test whether this isolated
part is capable of producing readthrough on its own. It allows us to quantify
the percentage contribution of Pre-diapin to readthrough, if it is indeed able to induce it.

![SCR-D pre-diapin construct](https://static.igem.wiki/teams/5622/images/wetlab/onlyprediapin.webp)

SCR-D construct containing only the pre-diapin element

**2- No hypopin:** This construct contains all parts of SCR-D except
for the Hypopin. It is used to determine whether this region is essential for readthrough
to occur, or if SCR-D can bypass the STOP codon without it. It allows us to quantify
the percentage contribution of Hypopin to readthrough.

![SCR-D no hypopin construct](https://static.igem.wiki/teams/5622/images/wetlab/nohypopin.webp)

SCR-D construct not containing hypopin element

**3- Mutated Diapin:** This construct is nearly identical to the normal
SCR-D (TGA) element, except that the nucleotide sequence of the Diapin region was altered
while maintaining its predicted base pairing and secondary structure. It was designed to
determine whether the Diapin’s specific sequence is essential for promoting readthrough,
or whether its structural configuration alone is sufficient for SCR-D function.
This distinction is particularly important for our project, as we are exploring
structure-dependent mechanisms to build a riboswitch.

![SCR-D mutated diapin construct](https://static.igem.wiki/teams/5622/images/wetlab/mutateddiapin.webp)

SCR-D construct containing a mutated diapin element

## SECIS

#### A: Controls

**1- Activity normalization:** The construct used to normalize luciferase
activity in the SECIS experiments was the same as the one used in SCR-D. Since both SCR-D
and SECIS employ the same luciferases to measure gene expression, we were able to use the
SCR-D luciferase normalization construct for normalization in the SECIS experiments.

![CGA construct](https://static.igem.wiki/teams/5622/images/wetlab/cga.webp)

CGA construct, a control for normalizing the activity of both luciferases

**2- Negative control / Bad SECIS:**
This construct does not contain any element that enables readthrough,
and is therefore used to determine the amount of Firefly luciferase produced as a
result of spontaneous ribosomal readthrough, experimental background, reinitiation,
or other possible experimental artifacts. Thus, in constructs with SECIS, we can determine
what percentage of Firefly luciferase corresponds to this background noise and what percentage
actually corresponds to the readthrough driven by SECIS.

![Bad SECIS construct](https://static.igem.wiki/teams/5622/images/wetlab/negative.webp)

Bad SECIS allows us to rule out Firefly luciferase expression that is independent of SECIS-mediated readthrough.

**3- SECIS control:** This is the construct we used to verify that the SCR element of
our riboswitch, namely SECIS, functions on its own. This construct is very important because, before
testing our riboswitch, it allowed us to confirm that the SECIS element works and is in the proper
conditions to carry out readthrough.

![SECIS control](https://static.igem.wiki/teams/5622/images/wetlab/seciscontrol.webp)

SECIS control confirmed that SECIS system works in the experimental conditions we are exposing it to

**4- Aptamer control:**
This construct allows us to verify the aptamer’s structural responsiveness
to the ligand under our experimental conditions before testing it within the riboswitch context.
In this design, the presence or absence of theophylline influences the formation of a **pseudoknot structure**
that can induce ribosomal frameshifting. If no theophylline is present, the pseudoknot that induces
ribosomal frameshift does not fold correctly. Conversely, when theophylline is added, the pseudoknot
is released, allowing the ribosome to undergo frameshift and avoid a STOP codon, placing Firefly
luciferase in the correct ORF and resulting in its expression. This sequence and design is extracted
from Anzalone et al., *Reprogramming eukaryotic translation with ligand-responsive synthetic RNAb
switches, Nature Methods (2016)*. https://doi.org/10.1038/nmeth.3807

![Aptamer control](https://static.igem.wiki/teams/5622/images/wetlab/fsapt.webp)

Dr Anzalone’s construct is crucial to make sure the aptamer is working in the experimental
conditions we are exposing it to.

#### B: Switches

The following constructs represent different designs of the SECIS-based riboswitch,
coupled with the theophylline aptamer. In their names, the letter L stands for “Linker,”
and the accompanying number indicates the number of nucleotides inserted between the SECIS
element and the aptamer. The L5 construct has a slightly modified version of the SECIS sequence.

In the absence of theophylline, the aptamer base-pairs with part of the SECIS sequence, keeping
the two regions bound together and preventing SECIS from folding into its **natural structure**.
The aptamer also base-pairs with the linker when there is no theophylline. When theophylline binds to
the aptamer, this interaction is disrupted, changing the conformation of the SECIS region and
therefore altering Firefly luciferase expression and acting as a riboswitch for readthrough rate.

**1- L5:** Structure of Linker 5 construct.

![Linker 5 construct](https://static.igem.wiki/teams/5622/images/wetlab/l5.webp)

Structure of Linker 5 construct

**2- L7:** Structure of Linker 7 construct.

![Linker 7 construct](https://static.igem.wiki/teams/5622/images/wetlab/l7.webp)

Structure of Linker 7 construct

**3- L8:** Structure of Linker 8 construct.

![Linker 8 construct](https://static.igem.wiki/teams/5622/images/wetlab/l8.webp)

Structure of Linker 8 construct

**4- L9:** Structure of Linker 9 construct.

![Linker 9 construct](https://static.igem.wiki/teams/5622/images/wetlab/l9.webp)

Structure of Linker 9 construct

It is important to consider that these four riboswitches were designed using our model and the TADPOLE software. These two tools operate based on structural predictions provided by RNAfold and focus specifically on the energetic requirements that allow the aptamer to fluctuate between two states: bound to theophylline or unbound. The linkers were designed based on an ON-ON switch structural prediction with RNAfold. This means that a functional (non-disrupted) SECIS element is expected in the presence of theophylline, as illustrated in the riboswitch structures.

However, RNAfold has known limitations that prevent its structural predictions from being fully accurate, and these limitations appear to affect the non-canonical interactions within the functional core of SECIS. Therefore, although the in silico prediction suggested an ON-ON switch, an OFF-ON switch may be observed experimentally.

#### C: Dr. Copeland’s plasmid

This plasmid was used in the cell-free assays as a supplement for SECIS. The SBP2 protein is essential for SECIS to induce readthrough, so its presence is required to achieve higher readthrough efficiency.

![Dr. Copeland's SBP2 plasmid](https://static.igem.wiki/teams/5622/images/wetlab/pc.webp)

Simplified version of Dr. Paul Copeland’s SBP2 gene context.

↑

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{% block title %}Results{% endblock %}
{% block lead %}Interactive exploration of our experimental findings through our results tree{% endblock %}
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# Results

Explore the results obtained on the Wet Lab.

The results obtained follow a progressive design logic, illustrated by a **branching structure**. We have been working with **stop-codon readthrough elements**, with the aim to create a library of different readthrough levels. Starting with SCR-D, we characterized a novel readthrough element in **HEK293T cells** to understand its parts, function, and determinants of readthrough efficiency. As a pivotal step, we explored and optimized a well-characterized readthrough element: a Selenocysteine Insertion Sequence (**SECIS**).

To go even further, we explored the possibility of creating an inducible SCR element by creating a riboswitch. Thus, we characterized a sensing element as well: a **theophylline aptamer**, in **HEK293T cells** too.
Another pivotal step was needed, and we transitioned to **cell-free systems**, to validate the **SECIS and Aptamer Elements**: our top candidates for **building the riboswitch**.
With the help of our **Software** and **Model** tools, we designed the **riboswitch constructs** fusing these two elements. And our final step was testing them in the lab.

## Results Overview:

* We achieved the first experimental validation of **SCR-D** in **HEK293T cells**,
  reaching up to **9% readthrough**.
  Its modular domains (PreDiapin, Diapin, Hypopin) can be tuned to achieve different expression levels,
  making it a valuable tool for **controlled dual-protein expression**. However, as its activity mainly depends
  on a short sequence immediately after the stop codon, we determined it is **not a suitable element for
  riboswitch applications**, which rely on structural modulation.
* In HEK293T cells, **SECIS DIO2** consistently achieved **8–10% readthrough**, an exceptionally high rate
  for mammalian systems. These results validate it as a **structurally driven and highly reliable
  element** for riboswitch construction. In the cell-free system, SECIS maintained functionality,
  reaching up to **14% readthrough** when supplemented with 300 ng/μL SBP2, confirming its strong structural
  integrity across experimental contexts.
* The **Theo-ON-5 aptamer** is a promising and well-characterized sensing element in many systems,
  but it does not perform consistently in HEK293T cells. It does, however, exhibit **strong and
  reproducible ligand responsiveness in the cell free system**. This makes it a reliable sensing
  component for in vitro riboswitch development and characterization.
* Binding **SECIS DIO2** with the **Theo-ON-5 aptamer** with computationally designed linkers using our own
  developed tools produced **ligand-responsive riboswitches**. Upon theophylline binding,
  readthrough
  decreased consistently across variants, demonstrating a **reproducible OFF-switch behavior**. Linker
  length modulated both baseline activity and switching amplitude, highlighting the role of RNA
  architecture in functional tuning.
* Using **TADPOLE** and our **energetic modeling pipeline**, we successfully predicted a transition between
  two different energetic states responsive to the presence of theophylline, and designed linkers that
  enabled **switching readthrough rates between structural conformations**. Experimental results confirm the
  utility of our tools for designing and parametrizing riboswitches, and provide valuable feedback to
  further refine linker optimization and RNA energetic modeling tools.

#### Let’s take a look at what our results revealed!

1-SCR-D
2-SECIS
3-Aptamer
4-Aptamer CF
5-SECIS CF
6-Switch
HEK Setup
Future

Buttons with a lighter colour are results in HEK cells, and buttons with a darker colour are results in cell-free systems (CF)

# BRANCH 1: Characterizing the SCR-D Element in HEK Cells

## Background

SCR-D is a newly identified stop codon readthrough (SCR) element that can stimulate substantial levels of translational readthrough, but its functional architecture remained uncharacterized until now.
To address this, we performed an evolutionary and structural analysis of the element, detailed in Engineering.

Explore the predicted structure of the SCR-D element

From this analysis, we identified three structural regions that could contribute to readthrough and potentially allow modular control over translational efficiency:

### PreDiapin

A short stretch of highly conserved nucleotides upstream of the main hairpin.
We hypothesized that these nucleotides are critical for stimulating readthrough.

See sequences

### Diapin

A stem-loop structure where base pairing is conserved, but individual nucleotides vary.
We hypothesized that its structure, rather than its sequence, is important.

See sequences

### Hypopin

A downstream region with low sequence conservation and variable predicted structures.
We hypothesized that it contributes little to readthrough.

See sequences

To test these hypotheses, we designed the following constructs for comparison with the natural SCR-D element:

* Only PreDiapin
* Mutated Diapin (sequence altered but structure preserved)
* No Hypopin
* Natural SCR-D Element

Reporter constructs were built using a Dual-Luciferase Assay design,
with Renilla luciferase preceding the stop codon and Firefly luciferase following it.
Constructs were transfected into HEK293 cells, and readthrough was quantified as the Firefly/Renilla activity ratio.

Normalisation was performed using the following controls:

* CGA (positive control): stop codon removed for maximal dual expression.
* TAAA (frameshift negative control): no Firefly expected.
* TGA postATG mutated: control for reinitiation events.

See chosen constructs

## Results

The normalized readthrough rates are summarized in Figure 1.

![SCR-D readthrough results](https://static.igem.wiki/teams/5622/images/wetlab/results/1r-scr-d.webp)

Fig. 1. Normalized readthrough levels for the different constructs tested in the lab.

We can observe that the natural SCR-D element achieved almost
9% readthrough in HEK cells, a remarkably high percentage in mammalian systems..
The construct containing only PreDiapin reached around 6% readthrough, supporting our hypothesis that these few nucleotides play a major role despite their small size.
Similarly, the construct lacking the Hypopin produced ~5% readthrough, suggesting that this region contributes to the overall effect, despite its low sequence conservation.
Finally, the mutated Diapin construct, in which the sequence was altered but the structure preserved, maintained high readthrough at ~8%.
This indicates that the specific nucleotide sequence of the Diapin is not essential for activity; it just contributes to it.

## Discussion

These results provide clear insights into the contributions of different SCR-D regions:

* PreDiapin drives most of the readthrough activity.
  Even in isolation, the short PreDiapin sequence stimulates ~6% readthrough, confirming its major role despite consisting of only a few nucleotides.
* The Diapin and Hypopin appear to modulate this effect together rather than independently.
  When the Diapin sequence is mutated but its structure is preserved, readthrough remains similar (~8%) to the natural sequence (~9%).
  Compared to No Hypopin (~5%) and Only PreDiapin (~6%), this suggests the Diapin alone does not substantially enhance readthrough.
  Instead, its function seems to depend on the presence of the Hypopin.
  The Hypopin, although weakly conserved, may help stabilize structural interactions with the Diapin that facilitate readthrough.

Taken together, these results indicate that SCR-D’s high readthrough is driven mainly by PreDiapin,
while the rest of the structure together provides a lesser contribution.

## Implications

Although SCR-D achieves exceptionally high readthrough levels in mammalian cells (up to 9%), its activity depends mainly on a short conserved sequence (PreDiapin) rather than on the disruption of a structured element.
Because riboswitch design requires elements whose function depends on structure, we can conclude that targeting SCR-D for a riboswitch is not the most suitable strategy.

Nevertheless, our work provides the first functional dissection of SCR-D. The distinct contributions of PreDiapin, Diapin, and Hypopin show that it can be modularly tuned to achieve different readthrough rates.
While not used as the basis for our riboswitch, SCR-D shows great value as a novel part, which expands the library of genetic tools for dual-protein expression and controlled co-expression in mammalian systems.

# Branch 2: Characterizing the SECIS Element in HEK293T Cells

## Background

The SECIS (Selenocysteine Insertion Sequence) element was chosen as a potential riboswitch platform because it is a well-characterized structural motif with strong and reproducible readthrough activity.
Unlike the SCR-D element, SECIS has been extensively studied and its function depends primarily on
RNA structure, making it a fitting choice for structure-based regulation.
SECIS DIO2 is a particularly well-performing element, with documented efficiency
and compatibility in human cells, and it is the element we are using.[4]

See how we chose this particular SCR Element

We first need to characterize SECIS’ performance to:

* Identify the optimal experimental conditions for its use in HEK 293T cells.
* Confirm its suitability as a reliable riboswitch backbone.

The constructs used contained the SECIS element placed after the Renilla and Firefly luciferase genes.
Readthrough efficiency was measured as the ratio of Firefly to Renilla luminescence, normalized to a
CGA construct as a positive control which represents 100% readthrough.

# Constructs

### CGA Activity Normalization Control

Stop codon removed for maximal dual expression.

See constructs

### SECIS Control

Contains the SECIS element after the luciferases.

See constructs

Literature reports indicate that selenium supplementation can enhance SECIS activity in mammalian cells.
To clarify this, we tested SECIS activity under varying concentrations of both theophylline and selenium, in three independent experimental rounds.
Across three rounds of experiments, we progressively refined our cell density, treatment conditions, and timing to ensure reproducibility.
Below is a summary of the experimental parameters:

![SECIS experimental parameters table](https://static.igem.wiki/teams/5622/images/wetlab/results/goodsecis.webp)

Table 1. Experimental parameters used in each round of experiments.

## Results

In the first round, SECIS showed strong readthrough levels of up to 9% under basal conditions (0 mM theophylline) and at 0.5 mM theophylline.
However, at 1 mM and especially 5 mM, readthrough decreased to below 6%, likely due to
theophylline-induced cytotoxicity (as confirmed by our separate toxicity assay, see below).
These results demonstrate that the SECIS element is functional in HEK cells, achieving
readthrough levels consistent with (and mostly higher than) those reported in literature[4,6].

![SECIS first round readthrough results](https://static.igem.wiki/teams/5622/images/wetlab/results/3r-secishek-1.webp)

Figure 2. Raw Renilla (before stop) and Firefly (after stop) Luciferase Values and Normalized Readthrough Rate of the SECIS element in different theophylline concentrations.

In the second round, we tested the combined effects of selenium and theophylline.
While 1 mM theophylline slightly reduced readthrough, selenium supplementation increased it in a dose-dependent manner up to
100 nM, where the highest readthrough rate (10.00%) was observed.
Although replicability across replicates varied slightly, the overall trend was consistent: selenium enhances SECIS efficiency, while high theophylline concentrations are inhibitory.

![SECIS second round results](https://static.igem.wiki/teams/5622/images/wetlab/results/4r-secishek-2.webp)

Figure 3. Raw Renilla (before stop) and Firefly (after stop) Luciferase Values and Normalized Readthrough Rate of the SECIS element in different theophylline and selenium concentrations.

In the third round, we confirmed these observations under optimized conditions (higher cell density and shorter treatment time).
Some of these round’s results have limited replicability, but readthrough levels without selenium remain
consistent with previous rounds (~8–9%).

![SECIS third round results](https://static.igem.wiki/teams/5622/images/wetlab/results/5r-secishek-3.webp)

Figure 4. Raw Renilla (before stop) and Firefly (after stop) Luciferase Values and Normalized Readthrough Rate of the SECIS element in different theophylline concentrations.

As we can see, the highest readthrough rates across the three rounds are achieved with
100 nM of Selenium (10%), even though Selenium does not seem to be required for SECIS activity; it only optimizes it further.
On the other hand, theophylline addition slightly affects readthrough up until 1 mM, and more substantially at 5 mM.

![SECIS combined analysis results](https://static.igem.wiki/teams/5622/images/wetlab/results/6r-secishek-combined.webp)

Figure 5. Normalized Readthrough Rate of the SECIS element across the three rounds of experiments, in different theophylline and Selenium concentrations.

## Discussion and Implications

Together, these results confirm that:

* The SECIS element is functional and reliable in HEK 293T cells, consistently performing
  8–10% readthrough across different experimental conditions. These values are high compared to other eukaryotic readthrough elements reported in literature, demonstrating the robustness of SECIS as a translational regulator.
* Readthrough efficiency remained consistent despite variations in cell number (30,000–100,000 cells/well) and timing (24–48 h incubation), which highlights the reproducibility of the system.
* Selenium supplementation improved performance slightly, suggesting a role in optimizing the SECIS conformation, but it is not essential for activity. In future experiments, we could try further optimization with higher selenium concentrations, alongside a selenium toxicity assay.
* The working range of theophylline was defined as 0–1 mM, ensuring minimal cytotoxicity while maintaining SECIS functionality.

Final Implication

Our work provides a characterization of the SECIS DIO2 element in HEK293T Cells that reaches consistent
readthrough levels of 8–10% across different experimental conditions: a very high percentage in readthrough
research, especially in mammalian cells.

# Effect of Selenium and Theophylline on Controls

### A: Effect on Normalization Control

#### Background

Both selenium and theophylline
were necessary reagents for our SECIS and aptamer experiments. Since these compounds can directly affect
cellular metabolism and translation efficiency, it was essential to evaluate how they influenced the
normalization control construct used across all samples.

To ensure accurate normalization, we included replicates of the control under every tested condition
(each theophylline and selenium concentration) in all experiments. In all results, each construct was
normalized only to a control exposed to the same experimental conditions.
This approach ensured that any observed differences in luciferase activity were attributable
to the experimental element (SECIS or aptamer) rather than to theophylline or selenium effects.

#### Results

As shown in Figure 11, both theophylline and selenium influenced the CGA control construct, highlighting the need for condition-specific normalization.

At 0 nM selenium
and 0 mM theophylline,
the baseline Firefly/Renilla (FLuc/RLuc) ratio was around 10–12%,
consistent across replicates. As the theophylline concentration increased, this ratio gradually decreased,
reaching approximately 8% at 5 mM. This consistent trend confirms
that theophylline slightly inhibits luciferase expression,
likely due to general cytotoxic effects at higher concentrations.

In contrast, selenium supplementation affected the control
construct to a greater extent. The FLuc/RLuc ratio increased from about
10% at 0 nM to up to 18%
at 100 nM selenium, although this effect depended on theophylline concentration. Specifically,
at 100 nM selenium, the ratio was around 14% with 0 mM
theophylline, and rose to ~18% with 1 mM theophylline.
These fluctuations with selenium, also observed in the Sensing Element experiments, indicate
that selenium challenges consistency but can also modulate
translational efficiency in HEK cells.

![CGA control under different conditions](https://static.igem.wiki/teams/5622/images/wetlab/results/14r-controls-hek.webp)

Figure 11. Firefly/Renilla luciferase ratios for the CGA positive control across different theophylline and selenium concentrations. Used as reference for normalization in both SECIS and aptamer experiments.

#### Discussion and Implications

These results demonstrate that both theophylline and selenium alter the baseline
luciferase activity of the normalization control, and emphasize the importance of normalizing
every construct against a control treated with the same conditions.

This is why all results
presented in the SECIS and aptamer sections have been normalized
against controls treated with identical reagent concentrations, ensuring that the
reported readthrough and frameshift rates reflect true biological effects rather than other
influences from the compounds themselves.

Moving forward, this reinforces the need to always
include internal controls under each tested condition
to maintain accuracy and reproducibility in luciferase-based assays.

### B: Theophylline Toxicity Assay in HEK293T Cells

#### Background

Theophylline is known to have toxic effects at high concentrations in mammalian cells, primarily through interference with phosphodiesterase activity and cellular metabolism. To ensure the viability of our experimental system and define safe treatment conditions, we performed a toxicity assay in HEK293T cells to establish the concentration range compatible with our constructs.

Cells were seeded at different densities (30,000 and 70,000 cells/well) and treated with increasing
concentrations of theophylline, ranging from 0 to 40 mM. After 24 hours of treatment, cell morphology
and viability were assessed under the microscope. After 48 hours, cells were counted using 10 μL samples
in a Neubauer chamber after homogenization.

This assay allowed us to determine both the cytotoxic threshold
of theophylline and the optimal seeding density for subsequent
experiments.

#### Results

As shown in Figure 11, cell counts showed some variability, likely due to incomplete homogenization during sampling, but the general trend is clear

* Up to 1 mM theophylline, cell numbers and morphology seem mainly unaffected.
* At 5 mM, a significant reduction in viable cells is observed, alongside visible changes in morphology.
* Above 5 mM, cell viability drops abruptly, confirming cytotoxic effects.

These findings are consistent with the luciferase assay results,
in which constructs treated with  5 mM theophylline consistently exhibited
reduced Firefly luciferase activity , suggesting cellular stress or death rather
than a true biological response.

![Quantification of viable cells](https://static.igem.wiki/teams/5622/images/wetlab/results/15r-toxicity-hek.webp)

Figure 11. Quantification of viable HEK293T cells after 48 hours of treatment with increasing theophylline concentrations (0–40 mM), for both 30K and 70K cells seeded.

These results are clearly supported by microscopic observations at 24h (Figures 13 and 14).
At 30,000 cells seeded, cells appear healthy and confluent
at 0–1 mM theophylline, but show clear detachment and reduced density
starting at 5 mM. At 70,000
cells, the same pattern is observed.

In cultures treated with ≥5 mM theophylline,
HEK293T cells show clear morphological signs of stress,
including the formation of clusters and detachment from the plate surface, as well as angular
or A-shaped appearance. This is consistent with the abruptly
reduced viability observed, and supports the cytotoxic effect observed at ≥5
mM theophylline.

![Microscope images 30k cells](https://static.igem.wiki/teams/5622/images/wetlab/results/16r-toxicity-30k-hek.webp)

Figure 13A. Microscope images for 30K seeded cells, 0–40 mM theophylline.

![Microscope images 70k cells](https://static.igem.wiki/teams/5622/images/wetlab/results/17r-toxicity-70k-hek.webp)

Figure 13B. Microscope images for 70K seeded cells, 0–40 mM theophylline.

#### Discussion and Implications

These results established a safe working concentration window
for theophylline in HEK293T cells:

* Concentrations below 1 mM are well tolerated.
* 5 mM and above induce clear cytotoxic effects.

The chosen concentrations of theophylline used in our experiments were based on this toxicity assay, which is why we only performed experiments using theophylline concentrations between 0 and 5 mM. This assay also confirmed that 30,000 cells/well provide sufficient density for reliable results.

# Branch 3: Validating the Sensing Element in HEK293T Cells

## Background

The theophylline aptamer was selected as our ligand-responsive element
because of its exceptional
specificity for theophylline [7],
its absence in mammalian cells [8], and its proven biocompatibility
in HEK cells [9].
Moreover, this aptamer has shown cross-system functionality
[10,11] and possesses a
well-characterized secondary structure, making it an excellent
candidate for riboswitch engineering[11].

Among the various versions available, we chose the Theo-ON-5 variant [11, 12], as it displayed the largest ON/OFF ratio in frameshift efficiency.

See how we chose the aptamer

Before incorporating it into our riboswitch, we aimed to:

* Validate its responsiveness to theophylline.
* Identify the optimal experimental conditions for reliable use in HEK 293T cells.

# Constructs

### CGA Activity Normalization Control

Stop codon removed for maximal dual expression.

See the constructs

### Aptamer Control

This construct contains the theophylline aptamer with a frameshift-inducing pseudoknot,
between the Renilla and Firefly luciferase genes. In this system, the aptamer’s folding state determines
whether a ribosomal frameshift occurs, which in turn determines if Firefly Luciferase is expressed or not.

see the constructs

Depending on the presence of the ligand:

* Without theophylline, the pseudoknot structure does not fold correctly, preventing frameshifting and blocking Firefly luciferase expression.
* With theophylline, the aptamer binds the ligand and releases the pseudoknot, allowing ribosomal frameshifting and placing the Firefly luciferase gene in-frame, enabling its expression.

This setup allows a direct measure of aptamer responsiveness by quantifying
Firefly/Renilla luminescence, normalized to the
CGA control (representing 100% translation readthrough).

View constructs

Because theophylline was required for this assay, and selenium had been tested in parallel for SECIS constructs,
we evaluated both factors together to ensure compatibility.

Four experimental rounds were carried out in HEK 293T cells, progressively refining cell density, treatment timing, and ligand concentrations,
following the same approach used for SECIS characterization with a further refining week, as detailed in Table 2.

![Experimental parameters for Frameshift and Aptamer Construct](https://static.igem.wiki/teams/5622/images/wetlab/results/goodaptamer.webp)

Table 2. Experimental parameters used in each round of experiments for the Frameshift and Aptamer Construct.

## Results

This first round shows promising and consistent results.
The aptamer seems to respond to theophylline, with frameshift rates increasing from
3–4% at 0 mM to
6–8% at 0.5–1 mM theophylline.
At 5 mM, readthrough drops below 5%, likely due to
cytotoxicity (see toxicity assay in Controls ).
These results suggest that the aptamer is being responsive to theophylline, even though the ratio is not increasing very much.

![Frameshift Aptamer HEK 1 results](https://static.igem.wiki/teams/5622/images/wetlab/results/8r-fsapt-hek1.webp)

Figure 6. Raw Renilla (before frameshift pseudoknot) and Firefly (after frameshift pseudoknot) Luciferase Values and Normalized FLuc/RLuc rate in different theophylline concentrations.

The second round introduces selenium to test its potential interference, and keeps evaluating responsiveness to
theophylline. In this case, the results were already expected as less reliable, and we can see they are less reproducible.

Frameshift rates appear to be between 2–4%, with some outlier values (~7%) only in the presence of selenium.
This low replicability prevented clear conclusions, and the addition of selenium seemed to reduce experimental consistency,
possibly by destabilizing the construct or interacting with the translation machinery.
Consequently, further refinement of the experimental conditions was required, and we considered stopping the use of selenium,
since it was not essential for SECIS function and seemed to hinder reproducibility.

![Frameshift Aptamer HEK 2 results](https://static.igem.wiki/teams/5622/images/wetlab/results/9r-fsapt-hek2.webp)

Figure 7. Raw Renilla (before frameshift pseudoknot) and Firefly (after frameshift pseudoknot) Luciferase Values and Normalized FLuc/RLuc rate in different theophylline and selenium concentrations. Second round of experiments.

In the third round, despite improved replicability, we observed no clear responsiveness to theophylline.
Frameshift rates remained stable around 3–4%, regardless of ligand concentration.
This suggested that, under the tested conditions, the aptamer was not reliably functional in HEK cells.

![Frameshift Aptamer HEK 3 results a](https://static.igem.wiki/teams/5622/images/wetlab/results/10r-fsapt-hek3.webp)

Figure 8a. Raw Renilla and Firefly Luciferase Values and Normalized FLuc/RLuc rate in different theophylline concentrations.

![Frameshift Aptamer HEK 3 results b](https://static.igem.wiki/teams/5622/images/wetlab/results/11r-fsapt-hek3.webp)

Figure 8b. Normalized rate for better visualization.

To test whether the promising results from the first round could be reproduced, we did a final round replicating the first round’s conditions (30,000 cells/well, 48 h incubation), as detailed in Table 2.
The results show a similar trend: frameshift ratios slightly increase in response to theophylline again (from ~4% to ~6%), but not with sufficient difference to confirm consistent responsiveness.
At 5 mM, the ratio decreases below 4% again due to toxicity, as we can see in Figure 9.

![Frameshift Aptamer HEK 4 results](https://static.igem.wiki/teams/5622/images/wetlab/results/12r-fsapt-hek4.webp)

Figure 9. Raw Renilla and Firefly Luciferase Values and Normalized FLuc/RLuc rate in different theophylline concentrations. Fourth round of experiments.

We also confirmed that the issue lies in the sensing element rather than in theophylline itself.
This conclusion comes from analyzing the CGA positive control in each experimental round, which behaved consistently and as expected: higher theophylline concentrations led to a gradual decrease in overall luminescence due to cytotoxicity (see the Positive Control in Figure 10).
This demonstrates that the ligand is active and functional, but that the aptamer construct does not respond as intended.

![Frameshift Aptamer Positive Control](https://static.igem.wiki/teams/5622/images/wetlab/results/13r-fsapt-hek4.webp)

Figure 10. Renilla and Firefly Luciferase Values and Normalized FLuc/RLuc rate in different theophylline concentrations, for both the positive control (CGA construct) and the Frameshift Element + Aptamer Construct.

## Discussion and Implications

Across all experimental rounds, the theophylline aptamer construct
failed to show consistent or reproducible responsiveness in HEK 293T cells.
Although some data hints at the expected trend, the differences are minor and not as large as those
reported in literature (even though they are tested in other systems).[11, 12, 13].

Our team hypothesized that this could be due to temperature-related instability, as the aptamer was originally characterized at 30 °C.
Using it under HEK293T culture conditions (37 °C) might cause partial denaturation of its structure, impairing proper folding and ligand binding.

### HP CARD: Anzalone

Anzalone, who developed and characterized the Theo-ON-5 aptamer we have used, confirmed that he had observed
the same inconsistent results that we did in HEK293T, which is why he never published them.
He recommended that we tried it in a cell-free system.

To further investigate this, we contacted Dr. Anzalone💡,
who developed and characterized the Theo-ON-5 aptamer used in our study.
He confirmed that his team had observed similar inconsistencies when testing their frameshifting riboswitches in HEK293T cells, which is why there were no papers published on the aptamer in this system.
He also noted that both the temperature difference and the
intrinsic inefficiency of ribosomal frameshifting he had observed in HEK cells [14] likely contributed to these results.

Based on his advice, and with additional input from Dr. Paul Copeland and our team discussions,
we decided to test the construct in a cell-free system (rabbit reticulocyte lysate), which operates at 30 °C:
a more suitable temperature for this aptamer. This platform provides tighter control of experimental conditions and has already been shown by Anzalone’s group to improve reproducibility[6, 17, 18].
We already had it in mind as our second option, so it was time to use it.

Final Conclusion

In conclusion, while the **theophylline aptamer** remains a promising and well-characterized sensing element
in many systems, our results indicate that it **performs inconsistently in HEK293T cells**.
Future experiments in **cell-free systems** will allow us to evaluate its switching behavior more accurately
and validate its use in combination with the SECIS element for riboswitch building.

# Branch 4: Aptamer’s Validity in the Cell-Free System

## Background

After observing inconsistent responsiveness of the theophylline aptamer in HEK293T cells, we transitioned to a **cell-free expression system** to test its functionality under more controlled conditions (TnT® Coupled Reticulocyte Lysate Systems from Promega®). Literature reports have shown that the used theophylline aptamer performs reliably in this system, where RNA folding is less affected by cellular complexity.[11, 12]

In this setup, we could test the aptamer’s responsiveness directly, without cellular variability. Due to solubility limitations of **theophylline** in the cell-free mixture, the **maximum concentration** we could reach was **2.4 mM**, added in small volumes before the incubation step.

See Limitations in Theophylline Solubility

The cell-free reaction can be initiated from either purified plasmids or PCR-amplified DNA templates. Here, we used **PCR products** derived from the plasmid containing the aptamer construct: a **theophylline-binding aptamer coupled to a frameshifting pseudoknot**, as well as a **CGA positive control** for normalization.

# Constructs

### CGA Activity Normalization Control

Stop codon removed for maximal dual expression.

See the constructs

### Aptamer Control

This construct contains the theophylline aptamer with a **frameshift-inducing pseudoknot** between the Renilla and Firefly luciferase genes. In this system, the aptamer’s folding state determines whether a ribosomal frameshift occurs, which in turn determines if Firefly Luciferase is expressed or not.

See the constructs

Depending on the presence of the ligand:

* **Without theophylline**, the pseudoknot structure does not fold correctly, preventing frameshifting and blocking Firefly luciferase expression.
* **With theophylline**, the aptamer binds the ligand and releases the pseudoknot, allowing ribosomal frameshifting and placing the Firefly luciferase gene in-frame, enabling its expression.

During the assay, transcription and translation occur simultaneously.[15] Theophylline was added prior to incubation at concentrations of **0 mM, 0.5 mM, 1 mM, and 2.4 mM**, and gene expression was quantified through a **dual-luciferase assay**, measuring both Renilla (upstream) and Firefly (downstream) activities.

## Results

As shown in **Figure 14**, the cell-free assay demonstrated a clear **ligand-dependent increase** in frameshifting efficiency. Normalized to the CGA control, the **FLuc/RLuc ratio increased from 7 % (0 mM)** to approximately **15 % (2.4 mM)**, showing a near-linear response to increasing theophylline concentrations.

Importantly, **the positive control remained unaffected by theophylline**, confirming that the ligand itself does not interfere with the translation process in this system, unlike in HEK cells, where minor cytotoxic effects were observed.

![FLuc/RLuc Ratio in different theophylline concentrations](https://static.igem.wiki/teams/5622/images/wetlab/results/18r-fs-cellfree.webp)

**Figure 14.** FLuc/RLuc Ratio in different theophylline concentrations to evaluate the Aptamer’s responsiveness to the ligand.

## Discussion and Implications

These results confirm that **the theophylline aptamer is fully functional in the cell-free expression system**, exhibiting a reproducible and quantitative response to ligand concentration. This validates the aptamer’s folding and switching capacity at 30 °C, supporting the hypothesis that its performance issues in HEK293T cells were related to the higher incubation temperature and intracellular conditions.

Final Implication

With this validation, the aptamer becomes a **strong candidate for integration into the final riboswitch design**, in combination with the SECIS element.

# Branch 5: SECIS Characterization in the Cell-Free System

## Background

To further validate the functionality of the **SECIS DIO2** element under controlled conditions, we tested it in a **cell-free expression system** (TnT® Coupled Reticulocyte Lysate System, Promega® #L1170). This was an essential step before integrating SECIS into the final riboswitch construct, as we needed to confirm that it retained its readthrough-promoting activity.

In this system, **selenium supplementation is not required**, as the commercial lysate already contains the necessary cofactors. However, **SBP2 (SECIS-Binding Protein 2)** is essential for SECIS activity, as it specifically recognizes the SECIS hairpin and recruits the selenocysteine insertion machinery.[20] To supply this factor, we incorporated **Dr. Copeland’s SBP2 plasmid**
💡
(under a T7 promoter) directly into the reactions.

### HP CARD

Paul Copeland has been a leading contributor in uncovering the mechanisms of **selenocysteine incorporation** at UGA stop codons. He played a key role in the identification of the eukaryotic **SECIS-binding protein 2 (SBP2)** and in establishing the mechanistic model in which **SECIS** works.
He is an expert on SECIS elements and advised us that it would not be possible to have good results without the presence of **SBP2** in the **cell-free system**. This is why he sent us his plasmids so that we could purify them and use **SBP2**. From his experience, he also confirmed that **Selenium supplementation** would not be necessary in the cell-free system.

We performed the assay starting from **PCR products** of the SECIS constructs (which were more stable and efficient in this format), while the SBP2 plasmid was used in **purified form**. Reactions were carried out with three SBP2 concentrations: **0, 100, and 300 ng/μL**. The SBP2 plasmid was added before incubation, allowing simultaneous **transcription and translation**. Gene expression was quantified using a **dual-luciferase assay**, and results were normalized to a **CGA positive control** for maximal activity and to a **negative control** (containing no readthrough element) to account for background signal.

# Constructs

### CGA Activity Normalization Control

Stop codon removed for maximal dual luciferase expression (used for ratio normalization).

See the constructs

### Negative control

Renilla luciferase followed by a STOP codon and Firefly luciferase; measures background readthrough or reinitiation.

### SECIS control

Contains the SECIS element downstream of the STOP codon.

### SBP2 Plasmid

Provides the SECIS-binding protein necessary for efficient readthrough activation.

## Results

As shown in **Figure 15**, the **negative control** exhibits a small but consistent increase in Firefly luminescence with higher SBP2 concentrations: from **~5% at 0 ng/μL** to **~12% at 300 ng/μL**. This effect was unexpected, but it shows that SBP2 slightly influences general translation efficiency, reinforcing the importance of including controls for each condition in experiments.

In contrast, the **SECIS construct** showed a much stronger and linear increase in readthrough efficiency. When normalized against both the CGA control and the negative control (**Figure 16**), readthrough rose from nearly **0% at 0 ng/μL SBP2** (as expected, since SBP2 is required for function) up to **~12–14% at 300 ng/μL SBP2**.

This demonstrates that the SECIS element is **active and responsive to SBP2 concentration**, achieving high readthrough efficiency in the cell-free context.

![Normalized readthrough SECIS vs negative control](https://static.igem.wiki/teams/5622/images/wetlab/results/19r-secis-cellfree.webp)

**Figure 15.** Normalized readthrough between the SECIS control construct and the negative control, normalized only to the positive control to see how SBP2 affects it. Tested at three different SBP2 concentrations: 0, 100, and 300 ng/μL.

![Normalized readthrough SECIS all controls](https://static.igem.wiki/teams/5622/images/wetlab/results/20r-secis-cellfree.webp)

**Figure 16.** Normalized readthrough to all controls for the SECIS construct in three concentrations of SBP2: 0, 100, and 300 ng/μL.

## Discussion and Implications

These results confirm that **SECIS DIO2 is fully functional in the cell-free system**, producing **up to 14% readthrough efficiency** when supplemented with sufficient SBP2. The consistent, SBP2-dependent trend demonstrates that this element behaves predictably in a controlled in vitro environment.

A concentration of **300 ng/μL SBP2** was established as optimal for achieving strong and reliable readthrough, although exploring higher concentrations (e.g., 500 ng/μL) could be further explored for optimization.

Final Implication

Together with the successful validation of the theophylline aptamer, these results show **an efficient readthrough element** that is ready to be assembled and tested as part of the final riboswitch constructs!

# Branch 6: Riboswitch Building in the Cell-Free System

## Background

After validating both the **SECIS DIO2** and the **Theo-ON-5 aptamer** in the cell-free system, optimizing **SBP2 concentration to 300 ng/μL** and **theophylline concentrations between 0 and 2.4 mM**, we proceeded to combine them into complete **riboswitch constructs**. [21, 22]

The objective was to test whether coupling the sensing element (aptamer) to the readthrough element (SECIS) could produce a ligand-responsive regulatory switch.

# Constructs

### CGA Activity Normalization Control

Stop codon removed for maximal dual luciferase expression (used for ratio normalization).

See the constructs

### Negative control

Renilla luciferase followed by a STOP codon and Firefly luciferase; measures background readthrough or reinitiation.

### SECIS control

Contains the SECIS element downstream of the STOP codon.

### SBP2 Plasmid

Provides the SECIS-binding protein necessary for efficient readthrough activation.

### L5 Construct

First test riboswitch, with five nucleotides inserted between the SECIS element and the Aptamer.

### L7 Construct

Second test riboswitch, with seven nucleotides inserted between the SECIS element and the Aptamer.

### L8 Construct

Third test riboswitch, with eight nucleotides inserted between the SECIS element and the Aptamer.

### L9 Construct

Fourth test riboswitch, with nine nucleotides inserted between the SECIS element and the Aptamer.

We also tried further controls with  **SBP2 concentrations up to 500 ng/uL** , just to explore further potential optimization.

## Results

In **Figure 17**, normalized to all controls, the **SECIS-only construct** displayed consistent readthrough around **6%**, below previous results (~12–14%). This variation may reflect batch-to-batch differences in lysate activity or fluctuations in reaction efficiency that should be further explored.

When the **aptamer and linker** are added, a clear switching behavior is observed. Even without theophylline, the addition of the linker and aptamer **partially disrupts SECIS function**, reducing readthrough from 6% to below 4% for L5, 3% for L7 and L8, and 2% for L9. When theophylline is added, the SECIS element is further disrupted, with its readthrough decreasing to **~1% across all variants**, demonstrating a **consistent OFF-switch effect**.

Interestingly, the magnitude of switching depends on linker length: **shorter linkers (L5)** initially preserve SECIS structure better, resulting in smaller absolute decreases but proportionally stronger relative switching. This pattern highlights how **linker architecture directly influences functional coupling** between the aptamer and readthrough domains, and is an interesting aspect to note for future discussion on improving linker prediction in our software and model tools.

![Linkers results - Figure 17](https://static.igem.wiki/teams/5622/images/wetlab/results/21r-linkers-cellfree.webp)

**Figure 17.** RLuc (before stop), FLuc (after stop), and normalized SCR rate across the four different riboswitch constructs, compared to only SECIS. All conditions are tested at 300 ng/uL SBP2 and in the absence (0 mM) and presence (2.4 mM) of theophylline, which corresponds to the off and on-state of the aptamer, respectively.

In **Figure 18**, we further examined control interactions. Increasing SBP2 concentration up to **300 ng/μL** enhanced SECIS readthrough, but **500 ng/μL decreased it**, likely due to competition of SBP2 with the translation machinery. Interestingly, the negative control shows even higher ratios with 500 ng/μL, emphasizing once more the importance of condition-matched normalization, but raising concerns on how to normalize samples to controls affected differently by the same reagent.

![Controls for linkers - Figure 18](https://static.igem.wiki/teams/5622/images/wetlab/results/22r-controlsforlinkers-cellfree.webp)

**Figure 18.** Negative control (right) and only-SECIS (left, not normalized to negative control) at two different theophylline and SBP2 conditions.

## Discussion and Implications

These experiments mark the successful construction and testing of our **first functional riboswitch prototypes**. All four designs, differing only in linker length, exhibited a **consistent ligand-dependent switching effect**, confirming that coupling an aptamer to a readthrough element can dynamically modulate translation.

As these riboswitches were designed and predicted using our **model and TADPOLE software**, these results give us important conclusions to evaluate them as well. These tools operate based on structural predictions provided by RNAfold and focus on the energetic requirements that allow the aptamer to fluctuate between two states: bound to theophylline or unbound. The linkers were designed based on an ON-ON switch structural prediction with RNAfold. This means that a functional (non-disrupted) SECIS element is expected in the presence of theophylline, as illustrated in the riboswitch structures.

However, RNAfold has known limitations that prevent its structural predictions from being fully accurate, and these limitations appear to affect the non-canonical interactions within the functional core of SECIS. Therefore, although the in silico prediction suggested an ON-ON switch, **an OFF-ON switch has been observed experimentally**. The activation of the aptamer has resulted in further disruption of the SECIS element, consistent across all linkers.

Hence, the model-guided approach **successfully generated constructs that switch between two structural and functional states**, validating the design pipeline. The data also provide valuable feedback for **refining linker prediction algorithms** and improving energetic modeling of complex RNA architectures.

Final Implication

Experiments have demonstrated that both **SECIS DIO2** and **Theo-ON-5 aptamer** are functional in a cell-free system, that their combination forms a **ligand-responsive regulatory unit**, and that linker length modulates baseline activity and switching amplitude.

# 🚀 Future Steps After Wiki Freeze

## Future Steps in the Lab

Our planned next steps and future directions for the project after the wiki freeze, including optimization strategies, potential applications, and continued development.

🚀

### Future Directions

Add your future experimental plans and project roadmap here.

# Results: Future Outlook

## What We Have Achieved

* We have **validated key RNA elements** for dual-protein expression.
* Integrating these elements with a sensing aptamer has enabled the creation of a **functional, ligand-responsive SECIS switch**.
* This work establishes **a modular and versatile toolkit** in which each component can be tuned or replaced independently, providing a **foundational platform for customizable riboswitches** in research applications.

## What Are the Next Steps?

* While our results demonstrate robust qualitative trends, **further replicates and quantitative statistical analyses** will strengthen and validate our conclusions.
* Future work will also address **computational limitations** identified during the project by integrating experimental feedback into prediction algorithms. For example, since non-canonical base pairs are not well captured by RNAfold, exploring tools such as **RNAstructure**, **Rosetta FARFAR2**, or **deep learning–based RNA folding models** can refine energetic predictions.
* Initiate **testing of the riboswitch constructs** across different cell lines and complementary in vitro systems to evaluate transferability and dynamic range.

## Looking Towards the Future

The system developed here forms the basis of a **versatile synthetic biology toolkit** capable of:

* **Precise coexpression control**: achieving defined protein ratios (e.g., 20:1 for Prot1:Prot2) from a single transcript.
* **Ligand-inducible regulation**: enabling circuits that respond dynamically to specific metabolites or environmental signals.
* **Compact vector design**: minimizing genetic footprint while maintaining high control over expression.

The tight control this platform offers makes it ideal for designing **robust genetic circuits**. Its modularity and adaptability to various ligands, proteins, and host systems position it as a foundational tool for synthetic biology, with transformative potential in **medical, agricultural, and industrial applications**.

![Future riboswitch toolkit diagram](https://static.igem.wiki/teams/5622/images/wetlab/results/captura-de-pantalla-2025-10-06-a-las-21-23-10.webp)

**Figure.** Overview of the modular SECIS-based riboswitch platform, highlighting future applications and scalability.

Learn more about the future of this technology!

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