iGEM 2025 — Team stanford

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block lead %}Research Notebook and Documentation{% endblock %}
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Notebook

Lab Records

### Included in this section:

* **Lab Notebook PDF** - Complete experimental records and documentation
* **Computational Tools Used in the Notebook** - The code and tools that supported our experimental work

Click to open in fullscreen

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[Click here to view the PDF in a new tab](https://static.igem.wiki/teams/5802/notebookfinal/wiki-writepup-wet-lab-4.pdf)

### Computational Tools Used in the Notebook

Here are the computational tools that were mentioned and used throughout our lab notebook:

#### Program 5: Plasmid Design & Sequence Modification Tool

This Google Colab notebook was used to modify NCBI sequences (like GPC3 from NM\_004484.3) to meet our RADAR sensor design criteria. The tool performs automated sequence cleaning, codon-level analysis, and smart editing to generate functional sensor constructs.

**Key Features:**

* NCBI sequence processing for gene bank sequences
* RADAR sensor optimization and codon editing
* Plasmid-ready output for synthetic biology applications

**Usage:** Input NCBI gene sequences → Output RADAR sensor-optimized sequences

dna\_sensor\_generator.py

```
def main():
    # Input DNA sequence below
    dna = ""  # the sequence you want to be made into a sensor seq

    dna = clean_sequence(dna)
    codons = to_codons(dna)
    sequence = make_sequence(codons)
    print("Spaced sequence:", sequence)

    reverse = reverse_sequence(sequence)
    print("Reversed:", reverse)

    complement = reverse_complement(reverse)
    print("Reverse complement:", complement)

    edited_sequence = sequence_edit(complement)
    print("Edited sequence:", edited_sequence)

def clean_sequence(seq):
    """Remove non-alphabetic characters from DNA sequence"""
    cleaned = ""
    for char in seq:
        if char.isalpha():
            cleaned += char
    return cleaned

def to_codons(seq):
    """Convert DNA sequence to codon triplets"""
    codons = []

    remainder = len(seq) % 3
    print(f"{remainder} letters were excluded from your sequence.")
    length = len(seq) - remainder

    i = 0
    while i + 2 < length:
        codon = seq[i] + seq[i+1] + seq[i+2]
        codons.append(codon)
        i = i + 3
    return codons

def make_sequence(codons):
    """Create spaced sequence from codon list"""
    spaced_sequence = codons[0]
    for i in range(1, len(codons)):
        spaced_sequence = spaced_sequence + " " + codons[i]
    return spaced_sequence

def reverse_sequence(sequence):
    """Reverse the DNA sequence"""
    reverse = ""
    for i in range(len(sequence) - 1, -1, -1):  # start at last index, go to 0
        reverse += sequence[i]
    return reverse

def reverse_complement(reverse):
    """Generate reverse complement of DNA sequence"""
    complement = ""
    for char in reverse:
        if char.lower() == "a":
            complement += "t"
        elif char.lower() == "t":
            complement += "a"
        elif char.lower() == "c":
            complement += "g"
        elif char.lower() == "g":
            complement += "c"
        else:
            complement += char
    return complement

def sequence_edit(complement):
    """Edit sequence to optimize for sensor applications"""
    current_sequence = complement.split(" ")
    new_sequence = current_sequence[:]
    tgg_indices = [i for i, codon in enumerate(current_sequence) if codon.lower() == "tgg"]
    if tgg_indices:
        middle_index = tgg_indices[len(tgg_indices) // 2]
        new_sequence[middle_index] = "uag"
    for i in range(len(new_sequence)):
        codon = new_sequence[i].lower()
        if not (tgg_indices and i == middle_index):
            if codon == "taa":
                new_sequence[i] = "gaa"
            elif codon == "tag":
                new_sequence[i] = "gag"
            elif codon == "tga":
                new_sequence[i] = "gga"
            elif codon == "atg":
                new_sequence[i] = "agg"
            else:
                new_sequence[i] = codon
    edited_sequence = make_sequence(new_sequence)
    return edited_sequence

main()
```

#### Program 6: Flow Cytometry Data Analysis

This Google Colab notebook was used to analyze our flow cytometry data from HBX, AKR1B10, GPC3, and AND-gated experiments. It performs statistical analysis, generates annotated plots, and calculates fold changes for our experimental results.

**Key Features:**

* Statistical analysis with t-tests on replicate datasets
* Annotated plotting for publication-ready visualizations
* Fold change calculation and standardized gating

**Usage:** Input flow cytometry data → Output statistical analysis and annotated plots

flow\_cytometry\_analysis.py

```
# -*- coding: utf-8 -*-
"""RADARoutput_plotting_template.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1lti9eKx7lz_DEt0c3L70LSz1aiHnVvHw
"""

#### IMPORT NECESSARY PYTHON PACKAGES ###

!pip install statannotations

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from IPython.display import display
from statannotations.Annotator import Annotator

"""**Now we need to upload the data file into the colab. Run the code below and then upload the csv file with your data**"""

from google.colab import files

# Upload a file from local computer
uploaded = files.upload()

data = pd.read_csv('sample raw data for plotting - Sheet1.csv')

# print out data to see what we are working with

data.head()

# Calculate the average of the three 'normalized GFP output' columns and create a new column 'Average GFP Output'
data['Average GFP Output'] = data[['normalized GFP output', 'normalized GFP output.1', 'normalized GFP output.2']].mean(axis=1)

# Reshape the data for plotting
data_melted = data.melt(id_vars=['Unnamed: 0'], value_vars=['normalized GFP output', 'normalized GFP output.1', 'normalized GFP output.2'], var_name='Replicate', value_name='GFP Output')

# Create the figure and axes
fig, ax = plt.subplots(figsize=(5, 5))

# Create the swarm plot
sns.swarmplot(x='Unnamed: 0', y='GFP Output', data=data_melted, ax=ax, color='gray')

# Add markers for the average GFP output
sns.pointplot(x='Unnamed: 0', y='Average GFP Output', data=data, ax=ax, color='black', linestyles='', marker='_', markersize=20, markeredgewidth=3)

# Set labels and title
ax.set_xlabel('Sample')
ax.set_ylabel('GFP Output (normalized to background)')

# Rotate x-axis labels for better readability
plt.xticks(rotation=90, ha='right')

# Display the plot
plt.tight_layout()
plt.show()

"""# Task
Create a new column in the dataframe `data` that is the average of columns 2, 3, and 4. Generate a swarm plot using columns 2, 3, and 4, with each replicate plotted across the row. Add a bold dash marker representing the average of the three replicates for each row. Create a second plot using the same data, perform independent t-tests comparing the first and second samples, and the third and fourth samples. Display the statistical test results as stars on the second plot and print the p-values for these comparisons.
"""

# Reshape the data for plotting
data_melted = data.melt(id_vars=['Unnamed: 0'], value_vars=['normalized GFP output', 'normalized GFP output.1', 'normalized GFP output.2'], var_name='Replicate', value_name='GFP Output')

# Confirm the structure of the melted DataFrame by displaying its head
display(data_melted.head())

# Create the figure and axes
fig, ax = plt.subplots(figsize=(5, 5))

# Create the swarm plot
sns.swarmplot(x='Unnamed: 0', y='GFP Output', data=data_melted, ax=ax, color='gray')

# Add markers for the average GFP output
sns.pointplot(x='Unnamed: 0', y='Average GFP Output', data=data, ax=ax, color='black', linestyles='', marker='_', markersize=20, markeredgewidth=3)

# Set labels and title
ax.set_xlabel('Sample')
ax.set_ylabel('GFP Output (normalized to background)')

# Rotate x-axis labels for better readability
plt.xticks(rotation=90, ha='right')

# Adjust layout
plt.tight_layout()

# Display the plot
plt.show()

"""## Define pairs for statistical testing

### Subtask:
Specify the pairs of samples to be compared using independent t-tests.

**Reasoning**:
Create a list of tuples specifying the pairs of samples for independent t-tests based on the 'Unnamed: 0' column in the `data` DataFrame.
"""

# Get the sample names from the 'Unnamed: 0' column
sample_names = data['Unnamed: 0'].tolist()

# Specify the pairs for comparison
pairs = [(sample_names[0], sample_names[1]), (sample_names[2], sample_names[3])]

# Print the pairs to verify
print(pairs)

"""## Add statistical annotations to the plot

### Subtask:
Use `statannotations` to add significance stars to the specified pairs on the plot.

**Reasoning**:
Create an Annotator object, set the statistical test, and apply the annotations to the plot.
"""

# Create an Annotator object
annotator = Annotator(ax, pairs, data=data_melted, x='Unnamed: 0', y='GFP Output')

# Configure the statistical test
annotator.configure(test='t-test_ind', text_format='star', loc='outside')

# Run the test and add annotations
annotator.apply_and_annotate()

# Display the plot with annotations
fig

"""## Perform statistical tests and print p-values

### Subtask:
Manually perform independent t-tests for the specified pairs and print the resulting p-values.

**Reasoning**:
Perform independent t-tests for the specified pairs and print the resulting p-values.
"""

from scipy.stats import ttest_ind

# Filter data for the first pair (complete match -trigger vs complete match +trigger)
complete_match_minus_trigger = data_melted[data_melted['Unnamed: 0'] == 'complete match -trigger']['GFP Output']
complete_match_plus_trigger = data_melted[data_melted['Unnamed: 0'] == 'complete match +trigger']['GFP Output']

# Perform independent t-test for the first pair
ttest_complete = ttest_ind(complete_match_minus_trigger, complete_match_plus_trigger)

# Print the p-value for the first pair
print(f"P-value for 'complete match -trigger' vs 'complete match +trigger': {ttest_complete.pvalue}")

# Filter data for the second pair (partial match -trigger vs partial match +trigger)
partial_match_minus_trigger = data_melted[data_melted['Unnamed: 0'] == 'partial match -trigger']['GFP Output']
partial_match_plus_trigger = data_melted[data_melted['Unnamed: 0'] == 'partial match +trigger']['GFP Output']

# Perform independent t-test for the second pair
ttest_partial = ttest_ind(partial_match_minus_trigger, partial_match_plus_trigger)

# Print the p-value for the second pair
print(f"P-value for 'partial match -trigger' vs 'partial match +trigger': {ttest_partial.pvalue}")

"""## Summary:

### Data Analysis Key Findings

*   The average GFP output for each sample was calculated and added as a new column (`Average GFP Output`) to the `data` DataFrame.
*   A swarm plot was successfully generated displaying individual data points and the average GFP output for each sample, with replicate data plotted across rows.
*   Independent t-tests were performed comparing 'complete match -trigger' with 'complete match +trigger' and 'partial match -trigger' with 'partial match +trigger'.
*   The p-value for the comparison between 'complete match -trigger' and 'complete match +trigger' was approximately 0.0174.
*   The p-value for the comparison between 'partial match -trigger' and 'partial match +trigger' was approximately 0.0185.
*   Significance stars based on the t-test results were successfully added to the plot.

### Insights or Next Steps

*   Both comparisons showed statistically significant differences in GFP output (p < 0.05), suggesting that the "+trigger" condition has a significant impact on GFP expression for both complete and partial matches.
*   Further investigation into the magnitude of the GFP output change and potential biological implications of the observed differences between complete and partial matches under the "+trigger" condition could be valuable.

"""
```

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{% extends "layout.html" %}
{% block title %}Engineering{% endblock %}
{% block lead %}Engineering Design and Development Process{% endblock %}
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Engineering Success

Design, Build, Test, Learn

* [Overview](#overview)
* [Cycle 1: HBx Viral Sensor](#cycle1)
* [Cycle 2: Host RNA Sensor](#cycle2)
* [Cycle 3: Logic Gating](#cycle3)
* [Cycle 4: Split IL-2 Output](#cycle4)

### Engineering Overview

Our Wet Lab Team aimed to design, construct, and validate a modular, programmable RNA-sensing platform using **ADAR editing** in living cells. By coupling programmable RNA sensors to split protein outputs, our system enables the detection of disease-specific RNA signatures and the activation of therapeutic proteins only when multiple targets are simultaneously present.

We focused on *hepatitis B virus–induced hepatocellular carcinoma (HBV–HCC)* as a clinically relevant model, targeting the **HBx** (viral) and **GPC3 / AKR1B10** (host) transcripts. We conducted multiple **Design–Build–Test–Learn cycles** to iteratively refine sensor design, validate logic gating, and prototype therapeutic outputs.

### Cycle 1: Designing and Validating the HBx Viral Sensor

#### Design

Our first goal was to create a **RADAR sensor** that could selectively recognize **HBx mRNA**, a viral transcript critical for HBV replication and oncogenic transformation. Using the Kaseniit et al. (2022) **RADAR algorithm**, we designed sensor-trigger pairs targeting conserved regions of the **HBx transcript**.

We performed a multiple-sequence alignment of *HBV genomes* and identified the region between AUG1 and AUG2 as both highly conserved across HBV genotypes and accessible within the mRNA secondary structure. Two sensor variants were designed:

**HBx\_Broad Sensor:** Targeting conserved regions to detect diverse HBV strains.

![HBx_Broad Sensor Design](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image7.avif)

**HBx\_CellLine Sensor:** Optimized for *Hep3B-specific HBV transcripts*. Each sensor was paired with a complementary trigger RNA, and cloned downstream of a constitutive promoter in a mammalian expression vector.

![HBx_CellLine Sensor Design](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image16.avif)

Each construct contains a **RADAR recognition domain** that forms a short double-stranded RNA with the target transcript, recruiting endogenous **ADAR enzymes** to deaminate a specific adenosine within a synthetic UAG stop codon embedded in the *GFP coding sequence*. Successful editing converts the stop codon to tryptophan, restoring translation and producing *GFP fluorescence* upon **ADAR-mediated editing** and translation reactivation.

We predicted RNA accessibility and folding using RNAfold to identify energetically favorable regions within **HBx** that maximize base pairing while minimizing intramolecular secondary structure.

#### Build

The two sensor variants were synthesized as gBlocks using clonal Twist synthesis and cloned into a mammalian expression vector downstream of a CMV promoter using In-Fusion cloning and transformed them into *E. coli DH5α* for amplification. Each construct contained:

* mCherry reporter
* 3' UTR sensor sequence (complementary to **HBx target**) containing the **RADAR-embedded stop codon**
* GFP reporter output

Plasmids were sequence-verified using Sanger sequencing and transfected into *HEK293T and Hep3B cells* using Lipofectamine 3000.

#### Test

*HEK293T cells* were transfected with either **HBx\_Broad** or **HBx\_Hep3B sensors**, along with their matching or scrambled trigger plasmids, using Lipofectamine 3000. Transfection efficiency was optimized by varying cell confluency (70–90%), DNA:Lipofectamine ratios, and incubation time. Both HBx sensor and trigger plasmids were co-transfected at equimolar ratios.

Specificity testing was performed by introducing a **5% sequence mismatch** in the trigger RNA. Despite this mismatch, both sensors maintained a clear signal with minimal off-target activation, confirming tolerance to viral mutation variability.

**HBx\_Broad Sensor:**

![HBx_Broad Sensor Analysis](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image14.avif)

One A-C editable mismatch between sensor and Broad Trigger designed, as predicted.

**HBx\_CellLine Sensor:**

![HBx_CellLine Sensor Analysis](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image12.avif)

One A-C editable mismatch between the sensor and Broad Trigger is designed in addition to 7 mismatched bases. Used to test functionality and specificity even in the case of a 5% mismatch due to frequent viral sequence mutations and variances, as predicted.

After 48 hours, fluorescence was measured using flow cytometry and fluorescence microscopy. Cells transfected with **HBx\_Broad + trigger** or **HBx\_CellLine + trigger** showed strong *GFP expression* compared to control cells lacking the trigger, validating the specificity of detection. White signals indicate *GFP-positive cells*; red signals represent sensor presence. Even in the edge case of a 5% mismatch between the broad trigger and the cell line sensor, we still observed measurable GFP output, indicating that common viral mutations do not significantly alter sensor performance.

![HBx Sensor Fluorescence Microscopy](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image13.avif)

Flow cytometry analysis of *GFP fluorescence* in *HEK293T cells* transfected with the **HBx\_Broad** and **HBx\_Hep3B sensors** in response to matched and mismatched synthetic triggers indicates a **13-fold and 23-fold**, respectively, increase in *GFP output* relative to the mismatched (scramble) controls. Complete match triggers also produced a **statistically significant increase** in fluorescent intensity compared to partial match triggers (**p=0.0233**). Results confirm both the sensitivity and ability to tolerate limited sequence variation in viral sequences.

![HBx Sensor Flow Cytometry Analysis](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image15.avif)

Single replicate quantitative evaluation of HBx\_Broad vs. HBx3b sensor performance.

#### Learn

From this cycle, we learned that:

1. The RADAR system successfully detects viral RNA in mammalian cells.
2. The Broad sensor design offers superior performance and cross-genotype stability.
3. Moderate sequence mismatches (≤5%) do not abrogate detection, ensuring real-world applicability in diverse HBV variants. These insights established a robust viral sensor for integration into future multi-input circuits.

### Cycle 2: Host RNA Sensor (GPC3 and AKR1B10)

#### Design

As we continued to research the trigger sequences we recognized that low expression of viral transcripts may not allow for **HBx** to be a good input so we looked for endogenous mRNAs associated with *HCC*. To achieve selective recognition of cancerous over healthy hepatocytes, we next designed sensors for **GPC3 (Glypican-3)** and **AKR1B10 (Aldo-Keto Reductase Family 1 Member B10)**, two transcripts consistently upregulated in *HBV–related HCC* relative to healthy liver tissue, to enable combinatorial disease detection.

Both are expected to have a singular editable **A-C mismatch** near the center of the sequence. Trigger plasmids were synthesized using clonal twist synthesis. Each plasmid contained the reverse complementary sequence of the respective sensor sequence under a constitutive promoter (CMV), followed by *mTagBFP* to mark the presence of the trigger in relation to edits made by the **ADAR enzyme** 24 hours following transfection.

**GPC3:**

![GPC3 Sensor Design](https://static.igem.wiki/teams/5802/lastminupload/screenshot-2025-10-09-at-4-25-58-am.avif)

![GPC3 Sensor Sequence](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image2.avif)

**AKR1B1O:**

![AKR1B10 Sensor Design](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image1.avif)

![AKR1B10 Sensor Sequence](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image11.avif)

Using GEPIA2 and DepMap transcriptomic datasets, the Dry Lab Team identified candidate transcripts, filtering by:

* High tumor-to-normal **log₂ fold change (>2)**
* Minimal basal expression in normal liver (**TPM < 1**)
* Presence in the *Hep3B transcriptome*

The **RADAR algorithm** was used to generate sequence-specific sensor regions complementary to accessible mRNA domains predicted via RNAplfold.

![Transcriptomic Analysis](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image9.avif)

#### Build

Each host sensor was cloned using the same **RADAR-GFP backbone** used in Cycle 1 as the viral sensor, enabling direct comparability of performance. Constructs were sequence-verified via Sanger sequencing, and plasmids were co-transfected into *HEK293T and Hep3B cells*, either individually or in combination with viral triggers to test potential crosstalk.

![Host Sensor Constructs](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image10.avif)

#### Test

*HEK293T cells* were co-transfected with each sensor and its corresponding trigger or scrambled control. After 48 hours, *GFP expression* was assessed by microscopy and flow cytometry.

Both **GPC3** and **AKR1B10 sensors** yielded significant activation when co-transfected with their respective triggers compared to the scramble condition, confirming the specificity of our sensor system.

![Host Sensor Fluorescence Microscopy](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image3.avif)

Mean calculations of our flow cytometry values revealed that our **AKR1B10 sensor** exhibited a **20-fold increase** in *GFP fluorescence* in the top 1% of sensor expressing cells (**p = 0.08** following t-test) whereas **GPC3 sensor** exhibited a **15-fold increase** in *GFP fluorescence* (**p = 0.00262** following t-test). The sensor imaging show *mCherry reporter* signalling the presence of the sensor, while the output editing columns show white filling in areas where a sensor-trigger match resulted in a *GFP output*.

![AKR1B10 Sensor Performance](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image4.avif)

Single replicate quantitative evaluation of AKR1B10 sensor performance.

![GPC3 Sensor Performance](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image17.avif)

Single replicate quantitative evaluation of GPC3 sensor performance.

#### Learn

We confirmed that the RADAR system can be modularly extended to detect case-specific host mRNAs, which enabled us to proceed to combinatorial logic-gate design using validated sensors as interchangeable inputs. Each sensor maintained high specificity and orthogonality, validating the platform's capacity for sensing combinatorial inputs. We also observed that GPC3 produced a slightly higher signal-to-background ratio in HCC-related lines, which informed its selection for downstream AND-gate integration.

### Cycle 3: Logic Gating with Split GFP (AND Gate)

#### Design

Having validated both viral and host sensors, we sought to integrate them into a dual-input **AND logic gate**, ensuring signal activation only in cells expressing both **HBx** and **GPC3**- characteristic of *HBV-transformed hepatocytes*. We used a split GFP design, where:

* Sensor 1 (**HBx**) controls the expression of the *GFP1–10 fragment*.
* Sensor 2 (**GPC3**) controls the expression of the *GFP11 fragment*.
* Fluorescence should occur only when both fragments are translated and properly folded, serving as a molecular **AND gate proof of concept**.

#### Build

Each sensor was fused to its respective **split GFP fragment** and cloned into mammalian expression plasmids. Before integration into the **AND-gate system**, the **split GFP halves** were tested under constitutive promoters to confirm proper reconstitution (positive control). Co-transfections were performed in *HEK293T cells* in 48-well format, including:

1. *GFP1–10* alone
2. *GFP11* alone
3. Both halves together (positive control)
4. **AND-gated sensor circuits** with all trigger combinations:

1. Both triggers (dual match)
2. **HBx trigger** only
3. **GPC3 trigger** only
4. Scramble (no trigger-sensor matches)

#### Test

Constitutive expression confirmed **split GFP reconstitution** (fluorescent signal only when both halves were co-expressed). No fluorescence was observed with either half alone, while a measurable *GFP signal* was detected only when both fragments were co-expressed. This confirms proper reassembly of the **split GFP protein** for the **AND-gated sensor** to be tested.

*HEK293T cells* were transfected with **GPC3 + HBx AND-gated sensors** and co-transfected with either both **GPC3 and HBx\_Broad triggers** (dual match), just one of them, or neither of them (scramble). Fluorescence microscopy revealed that *GFP signal* appeared only when both **HBx and GPC3 triggers** were co-expressed. Single-input conditions (only **HBx** or only **GPC3 trigger**) produced minimal signal, confirming logical **AND behavior**.

These results are corroborated by flow cytometry analysis, which indicated that complete match triggers relative to both sensors produced the highest *GFP output* compared to mismatched inputs, confirming the high specificity of our system. However, these are preliminary results and would require more replicates to calculate **statistical significance**. These results show proper **AND logic behavior** with minimal leakiness.

![AND-Gated Sensor Performance](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image6.avif)
![AND-Gated Sensor Performance](https://static.igem.wiki/teams/5802/lastminupload/engineering445/image8.avif)

Quantitative evaluation of AND-Gated sensor performance.

#### Learn

These experiments validated that multiple RNA sensors can be combined into higher-order logic circuits, enabling precise multi-input control of gene expression. It also demonstrated the scalability of the RADAR framework for complex biological computation within mammalian cells. The AND gate ensures high therapeutic expression is confined to cells where both HBx and GPC3 transcripts are present, allowing for disease-specific activation for our application. Although there appeared to still be some GFP expression in the single trigger match cases, the 2-fold increase in output expression in the dual match case proves our system's potential and validity. Leakiness and tunability are to be further optimized in experiments to come.

### Cycle 4: Split IL-2 Output (In Progress)

#### Design

Implementing our proof-of-concept towards a therapeutic application, we designed a **split IL-2**, an immunostimulatory cytokine used in *cancer immunotherapy*, as a therapeutic output to replace the **split GFP**. The goal was to create a system where **IL-2** is secreted only when both viral (**HBx**) and host (**GPC3**) transcripts are detected.

#### Build

We are in the process of cloning the N- and C-terminal fragments of **IL-2** downstream of the **HBx** and **GPC3 sensors**, respectively, preserving the same **stop-codon editing framework**. Each half will be fused to a signal peptide (*IL2sp*) to direct them into the secretory pathway, where reconstitution can occur following entry into the *endoplasmic reticulum (ER)*. All constructs will be sequence-verified, transfected and expressed in *HEK293T cells* in the same way as our **split GFP experiments**.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Our comprehensive experimental approach to developing and validating the RADAR biosensor system for hepatocellular carcinoma detection{% endblock %}
{% block page\_content %}

Experiments

Our comprehensive experimental approach to developing and validating the RADAR biosensor system for hepatocellular carcinoma detection

* [Overview](#overview)
* [Standard Protocols](#standard-protocols)
* [Materials and Methods](#materials-methods)
* [Sequences](#sequences)

### Overview

Our experimental approach focuses on developing the **RADAR** (*RNA-Activated Detection And Response*) **biosensor system** for detecting **hepatocellular carcinoma biomarkers**. This comprehensive methodology combines *molecular cloning*, *cell culture*, *transfection protocols*, and *validation studies* to create an effective detection system based on **ADAR-mediated RNA editing**.

The experimental design is structured around detailed *standard protocols* for reproducibility, comprehensive *materials and methods documentation*, and complete *sequence information* for all genetic components used in the biosensor development.

### Standard Protocols

#### Restriction Cleavage

*Media and materials needed:*

* Thawed reaction buffer
* Thawed restriction enzymes of choice
* Suspended plasmid

1. Assemble the reaction mix on ice in PCR tubes.

|  |  |
| --- | --- |
| **Complete plasmid** | 3 µg |
| **Reaction buffer** | 3 µl |
| **Restriction enzyme #1** | 1 µl |
| **Restriction enzyme #2** | 1 µl |
| **Nuclease-free Water** | to 30 µl total volume |

2. Incubate at 37°C for 3-5 hours (using a PCR machine or heat block).
3. Heat-inactivate enzymes at 65°C for 20 minutes.
4. Store digested DNA at -20°C if not used immediately, or purify the backbone using a gel purification kit.

#### RADAR Transfection

*Media and materials needed:*

* Room Temperature or 37°C Opti-MEM medium
* L3000 and P3000 reagents
* Suspended plasmids

For transfection of the RADAR system, a typical amount of total plasmid DNA (in a volume less than or equal to 10µL) is 500ng (500ng/ml) plasmid DNA per well when using a 24 well format with 200 ng of the sensor plasmid, 250 ng of the trigger sequence, and 50 ng of the ADAR editor plasmid.

1. **Cell confluency:**
   1. One day before transfection, seed cells in appropriate plates so they are 70-90% confluent at the time of transfection.
2. **Preparation of Transfection Mix:**
   1. Dilute the required amount of plasmid DNA in 25µl of Opti-MEM medium, then add 1µl of P3000 reagent.
   2. In a separate tube, dilute 1 µl of L3000 reagent in 25 µl of Opti-MEM medium.
   3. Combine the two tubes and allow the mixture to incubate for 10-15 minutes to enable complex formation.
3. **Transfection:**
   1. Add the DNA-reagent complexes dropwise to the cells in the growth medium (antibiotic-free).
4. **Incubation:**
   1. Return the cells to the incubator (37°C, 5% CO₂) until data collection and analysis

**Notes:**

* Always include a control to monitor transfection efficiency (ex. GFP-only plasmid, untransfected well).
* Change medium 4-6 hours post-transfection if cytotoxicity is observed.

#### Cloning and In-Fusion Assembly

*Media and materials needed:*

* Thawed In-Fusion assembly master mix
* Suspended vector
* Suspended insert
* Nuclease-free H₂O
* SOC recovery medium
* Competent bacterial cells

All optimal amounts of vector and insert to use in the In-Fusion Cloning reaction were calculated via TakaRaBio molar ratio calculator using insert and vector base pair size inputs. Following receiving suggested ng amounts, volumetric amounts were calculated using the insert and vector concentrations.

1. Place all components into an eppendorf tube and mix the reaction components by pipetting up and down 5x.

| Components | Complete | Backbone Only(control) |
| --- | --- | --- |
| **In-Fusion Assembly Master Mix** | 2 µl | 2 µl |
| **Vector** | - | - |
| **Insert** | - | - |
| **Nuclease Free H₂O** | to 10 µl total volume | to 10 µl total volume |

2. Incubate the reaction mix at 50°C for 15 minutes using a heat block.
3. Place the samples on ice for 1-2 minutes.
4. Store samples in -20°C until ready for bacteria transformation to complete the ligation.
5. Bacterial transformation following assembly using Stellar Comp Cells (Takara):
   1. Thaw competent cells on ice. Mix gently to ensure even distribution.
   2. Transfer 50 µl of the competent cells into a Falcon tube.
   3. Add up to 2.5µl of the In-Fusion reaction mixture.
   4. Incubate on ice for 20-30 min.
   5. Heat shock cells at 42°C for 45 sec.
   6. Place tubes on ice for 1-2 min.
   7. Add SOC medium to bring the final volume to 500 µl.
   8. Incubate at 37°C with shaking (160-225 rpm) for 20-60 min depending on antibiotic selection.
   9. Plate 100-170 µl of the transformation mix onto selective LB agar plates and incubate plates overnight at 37°C.

#### Hep3B Cell Line Maintenance

*Materials needed:*

* Warm growth medium, PBS, and trypsin to 37°C using a water bath
* Check cells under a microscope to assess confluency. Cells should be around 70–80% confluent before repassaging

1. Using a sterile pipette, gently aspirate the spent growth medium from the culture dish.
2. Add 5 mL of PBS to wash off residual serum or medium. Swirl gently, then aspirate the PBS.
   1. *Tip:* When adding PBS, touch the pipette tip to the side of the dish to avoid disturbing the cells.
3. Add 1.5 mL of trypsin to the dish and incubate at 37°C for about 5 minutes to allow the cells to detach.
4. Check under a microscope to confirm cell detachment.
5. Add 3.5 mL of complete growth medium to neutralize the trypsin. Pipette up and down to break up any clumps and get a single-cell suspension.
6. Transfer an appropriate volume of this suspension into a new dish containing 10 mL of fresh DMEM complete growth medium.
   1. *Example:* For a 1:5 split, take 1 mL of the suspension and add it to 10 mL fresh medium. For smaller splits, adjust the transferred volume accordingly (ex. 200 µL into 10 mL for a 1:25 split).

**Notes on Hep 3B culture:**

* When thawed, Hep 3B cells were initially cultured in DMEM complete growth medium with 20% FBS.
* After the first passage, cells were maintained in DMEM complete growth medium with 10% FBS.

#### Cell Seeding

*Materials needed:*

* TC-treated cell culture plates
* DMEM Growth Media warmed to room temperature
* Trypan Blue dye

1. Gather all the cells in the original dish in a 10 ml centrifuge tube and spin in a centrifuge at 1200 rpm for about 3 minutes. Following the spin down, you should see a pellet at the bottom of a tube.
2. Aspirate the supernatant containing any medium and trypsin.
3. Rack the tube to facilitate resuspension of the cell pellet.
4. Use about 5ml of DMEM to resuspend the cells.
5. Use the cell counter to inspect the number of live cells:
   1. 10µl cell suspension + same amount of trypan blue into the cell counter slide
6. Calculate the total volume of the cell suspension needed for the wells you are seeding.
7. Move the volume of cells needed for a concentration of 1.5×10⁵ cell/ml to another tube and fill the rest of that tube with the remaining volume of DMEM to make 24 ml of master mix
8. Fill each well with 1 ml of the master mix.
9. Label the transfection plate with the date, your experimental conditions, and which wells will receive the cells.
10. Place the plate in the **37°C incubator** with 5% CO₂ for **16-18 hours** before transfection.

#### Data Collection and Analysis

All data was collected using fluorescent microscopy and flow cytometry. Fluorescent microscopy was used for initial verification of sensor and trigger presence as well as GFP production, while flow cytometry was used to quantify fluorescence intensity values. Fluorescent images were processed using ImageJ for brightness and contrast values, with no major modifications made. For flow cytometry, around 10k cells were recorded per sample with the FSC/SSC gating strategy used to exclude debris and capture only fluorescing cells. For each experiment, at least three replicates were made.

### Materials and Methods

#### Plasmid Construction

All plasmids were constructed using **In-Fusion Assembly reactions** using inserts designed by the team and synthesized by **Twist Bioscience** and vectors digested from previously validated sensor plasmids. To initially clone the **glypican-3 (GPC3)**, **hepatitis B x antigen (HBx)**, and **aldo-keto reductase family 1 member B10 (AKR1B10)** sensor plasmids, the *Kaseniit et al., 2022 sensor plasmid* (plasmid EK0698) was digested to remove the sensor sequence for later replacement with the appropriate synthesized sensor fragment. Reactions were completed in a 30 µl volume consisting of 3µg of the appropriate plasmid, 1 µl of each *restriction enzyme* (**MluI-HF** and **XhoI**), 3µl of 10X rCutSmart Buffer, and the remaining volume of Nuclease-free water. Digests were performed using a *thermocycler*, incubating the samples at 37°C for 5 hours followed by *heat inactivation* at 65°C for 20 minutes. The PCR products were then run on a 0.8% agarose gel in 1X TAE buffer for 45 minutes at 120 volts. Following the gel, appropriate bands respective to the vector backbone length were removed from the gel using a blade and gel purified as per the **Zymoclean Gel DNA Recovery Kit** for later plasmid construction.

Following *sensor validation*, to alter the output, digestions were run on **GPC3** and **HBx sensor plasmids**, enabling isolation and removal of the **GFP output** initially used to test the sensors. *Restriction enzymes* used for the GPC3 and HBx sensor digestions were **ApaI** and **XhoI** to frame the GFP output fragment. This reaction was performed according to the description above using ApaI, XhoI, and 10X FastDigest Buffer.

All **In-Fusion Assembly reactions** were performed in 10 µl volumes at a molar ratio of 2:1 insert:vector and a total DNA mass of 200ng. To assemble the **GPC3** and **AKR1B10 sensor fragments**, around 178 ng of the vector, 24 ng of the insert, 2 µl of **In-Fusion Master Mix**, and the volume to 10µl of Nuclease-free water was used. To assemble *split GFP output fragments*, for the GPC3 sensor around 162 ng of vector and 38 ng of **GFP 1-10 insert** were used with the aforementioned amounts of In-Fusion Master Mix and Nuclease-free water, while the HBx sensor reaction required 191 ng vector and 39 ng **GFP 11 insert** were used with the aforementioned amounts of In-Fusion Master Mix and Nuclease-free water.

#### Cell Culture Conditions

**Hep 3B** and **HEK 293T cells** were maintained at 37°C with 5% CO₂ in **DMEM** supplemented with 10% *fetal bovine serum (FBS)* and 1% *Penicillin–Streptomycin*. Hep 3B cells were thawed into DMEM containing 20% FBS for the first passage and subsequently cultured in 10% FBS medium. Both *cell lines* were passaged at 70–80% confluency using *0.05% trypsin–EDTA*, and split at ratios between 1:3 and 1:10 depending on *growth rate* and experimental needs.

### Sequences

| Component | Sequence |
| --- | --- |
| **Hepatitis B x Antigen (HBx) Sensor Sequence** | ggtcgtccgcaggattcagcgccgacgggacgtaaacaaaggacgtcccgcgcaggatccagtTAGcagcacagcctagcagccatg |
| **Hepatitis B x Antigen (HBx) Trigger Sequence** | Catggctgctaggctgtgctgccaactggatcctgcgcgggacgtcctttgtttacgtcccgtcggcgctgaatcctgcggacgacc |
| **Glypican-3 (GPC3) Sensor Sequence** | tttctttctttgcaaaaggacaatctatatgctaccactaaaatgtatcttcctccaaaagataccattggattttcgaaaacagaacacatggttagtcctctacttcatggcTAGaggaggtatacaggagaacaaaaaaaaaaaaatagaaaaaaagaagaaagtggttccctttatcgaggaagaccacagggtgctgtagggcagcacaggtgctgggcaccaggc |
| **Glypican-3 (GPC3) Trigger Sequence** | Gcctggtgcccagcacatgtgctgccctacagcaccctgtggtcttcctcgataaagggaaccactttcttatttttttctatttttttttttttgttatcctgtatacctcctccagccatgaagtagaggactaaccatgtgttatgttttcgaaaatcaaatggtatcttttggaggaagatacattttagtggtagcatatagattgtccttttgcaaagaaagaaa |
| **Aldo-keto reductase family 1 member B10 (AKR1B10) Sensor Sequence** | gaaatgtctaaaacagcacctcgattctcgtctataacaggactaagttcactgggatctgaaataagcttggctaaaatgggacatgagTAGagggagtcacacttcagcgaagaaagagaatctcctgtagaatctcaccaggagattcaa |
| **Aldo-keto reductase family 1 member B10 (AKR1B10) Trigger Sequence** | Ttgaatctcctggtgagattatacaggagattctctttcttcgctgaagtgtgactacctccactcatgtcccattttagccaagcttatttaagatcacagtgaacttagtcctgttatagacgagaatcgaggtgctgttttagacattta |
| **GFP 1-10** | TCAAAGGGCGAAGAGCTGTTTACCGGCGTCGTGCCCATTCTGGTGGAGCTGGACGGTGACGTTAATGGCCACAAGTTCAGTGTAAGGGGGGAAGGCGAGGGCGATGCTACAATAGGGAAACTCACACTCAAGTTCATCTGTACTACTGGCAAACTTCCTGTGCCCTGGCCCACCCTCGTGACAACTCTCACTTACGGGGTTCAGTGTTTTAGCCGCTACCCTGACCACATGAAGAGACACGACTTCTTTAAATCTGCCATGCCCGAGGGATACGTCCAGGAACGGACGATCAGTTTCAAGGACGACGGAAAATACAAGACACGAGCCGTGGTGAAGTTCGAGGGGGATACATTGGTCAATCGGATTGAGCTGAAGGGGACTGACTTTAAGGAGGATGGGAATATTCTGGGTCACAAATTGGAATACAATTTTAATAGCCACAACGTGTACATAACCGCAGATAAACAAAAAAACGGGATCAAAGCTAATTTCACCGTTCGCCACAACGTGGAGGATGGCAGTGTCCAGCTGGCTGACCATTACCAGCAGAACACACCTATTGGAGATGGCCCGGTCTTGCTGCCTGATAACCACTACCTGAGTACCCAGACCGTGCTGAGCAAAGATCCCAATGAGAAA |
| **GFP 11x3** | ATGCGTGACCACATGGTCCTTCATGAGTATGTAAATGCTGCTGGGATTACAGGTGGCGGCGGAAGTGGAGGTGGAGGCTCGGGTGGAGGCAGTTCGCGAGACCATATGGTTCTCCACGAATACGTAAACGCAGCAGGCATCACTGGCGGTGGCGGAAGTGGCGGAGGAGGTTCGGGCGGAGGCAGTTCGCGTGACCATATGGTCCTCCATGAATATGTAAATGCAGCTGGCATTACT |
| **Neo2A** | CCGAAGAAGAAGATTCAGCTCCACGCCGAACACGCTCTTTACGATGCTCTTATGATACTGAACATCGTCAAGACAAATTCT |
| **Neo2B** | ACGAACAGCCCGCCCGCCGAAGAAAAATTGGAAGACTACGCCTTTAACTTTGAACTTATACTCGAAGAGATTGCTCGGCTGTTTGAGTCCGGTGACCAGAAAGACGAGGCCGAGAAAGCCAAGCGCATGAAAGAGTGGATGAAACGAATCAAGACTACCGCCAGTGAGGATGAGCAGGAAGAAATGGCTAACGCAATCATCACAATTTTGCAGTCCTGGATTTTCTCC |

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{% block title %}Results{% endblock %}
{% block lead %}Experimental validation of LiRA's RNA logic gate system{% endblock %}
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Results

Experimental validation of LiRA's RNA logic gate system

* [Background](#background)
* [Goals and Objectives](#goals-objectives)
* [Results](#results)
* [Discussion](#discussion)
* [References](#references)

### Background

#### Modular, programmable RNA sensing using ADAR editing in living cells

The ability to sense *RNA inside living cells* has the potential to allow diagnostics and therapeutics precise control over cellular behavior. Traditional RNA-based sensing mechanisms have mostly relied on *microRNAs* or engineered *ribozymes and guide RNAs*, but these approaches often suffer from limited programmability or interference with host pathways (Kaseniit et al., 2022). To overcome these limitations, Kaseniit et al. (2023), Jiang et al. (2023), and Qian et al. (2022) developed a modular and programmable system called **RADAR** using the natural activity of **adenosine deaminases acting on RNA (ADARs)** for RNA sensing. **ADARs** convert adenosine to inosine within *double-stranded RNA*, and **RADAR** uses this process to remove **stop codons** that block translation of a downstream protein, thereby linking the presence of a specific RNA sequence directly to a measurable and potentially therapeutic output. **RADAR sensors** have been validated in *human and mouse transcriptomes* and shown to differentiate between disease-relevant mutations and wild-type transcripts.

![A-to-I editing mechanism]({{ url_for('static', filename='assets/illustration/atoiediting.gif') }})

RADAR converts presence of target RNA seq into a functional protein output

#### Hepatocellular Carcinoma development from chronic Hepatitis B Virus conditions

Patients chronically infected with *hepatitis B virus (HBV)* are at high risk of developing *chronic liver disease (CLD)*, which progresses from hepatitis to fibrosis, cirrhosis, and ultimately *hepatocellular carcinoma (HCC)* (Shen et al., 2023). A major contributor to this process is the **hepatitis B x antigen (HBx)**, which not only promotes *HBV gene expression* and replication from *covalently closed circular (ccc) DNA*, but also induces *oxidative stress*, *mitochondrial damage*, and persistent activation of transcription factors, maintaining cycles of inflammation, hepatocellular destruction, and regeneration (Feitelson et al., 2022). Importantly for us, **HBx** often persists in the liver through integration of *HBV DNA* into the host genome, allowing continuous **HBx expression** even in patients with little or no viral replication. This integration causes cancer by disrupting normal host genes and chromosomal stability, leading to uncontrolled cell growth and survival driving tumor development (Levrero & Zucman-Rossi, 2016). In this context, multiple host biomarkers are closely linked with *HBV-related HCC*, including **glypican-3 (GPC3)**, a cell-surface proteoglycan that enhances *Wnt/β-catenin signaling* and serves as a diagnostic marker distinguishing HCC from benign liver disease, and **aldo-keto reductase family 1 member B10 (AKR1B10)**, an enzyme upregulated by *oxidative stress* that removes byproducts of lipid damage, allowing for tumor cell survival and metabolic adaptation (Zhou et al., 2017; Wang et al., 2009).

### Goals and Objectives

Our primary objective is to develop and validate the **synthetic RNA sensing system** that will only selectively detect *HBV-driven hepatocellular carcinoma cells* based on *transcriptomic signatures*. Rather than relying on traditional surface biomarkers, our objective is to build a **programmable intracellular sensor** capable of responding to combinations of *RNA transcripts* present in cancerous liver cells, but absent or minimally expressed in healthy tissue. Our goals specifically include engineering this system to drive expression of an therapeutic output protein only in target cells, optimizing its specificity and reliability in liver cancer-relevant models such as that of HBV transformed HCC. We test the system using artificially induced trigger environments in HEK293T cells and plan to test the system in vitro in engineered liver cell lines that naturally express our desired triggers and more closely resemble the liver cancerous tumor environment and finally in vivo. Ultimately, we aim to create a proof-of-concept for a modular therapeutic platform that can be adapted to other cancers lacking one specific biomarker, but possessing multiple transcriptional profiles instead, with our system even demonstrating the capability of sensing the edge case of a viral indicator.

We aim to successfully develop a proof of concept for an logic-gated RNA-sensing system for the generation of an effective therapeutic response targeted to a specific cancerous area. The platform can be adapted to output diverse molecules, including apoptotic proteins, cytokines, immune checkpoint inhibitors etc. that recruit immune cells to the tumor microenvironment (TME). With this in mind, we aim to demonstrate that our sensors for HBV-induced HCC are reliable and marked by GFP production, which serves as a placeholder for our upcoming experiments including producing IL-2, a cytokine known to enhance immune responses (Jiang et al., 2016). To ensure specificity and functionality even within physiologically relevant transcriptional environments, we test this model in HEK293T cells and are in the process of testing within the Hep3B cell line derived from HBV-induced HCC tumors as well. Future progress includes testing the system in other cancers induced by viral integration or possessing another biomarker specific to a certain stage of progression.

### Results

We first evaluated the performance of the viral **HBx sensor**. In *HEK293T cells* co-transfected with synthetic triggers, the **Broad HBx sensor** exhibited a **23-fold increase** in *GFP fluorescence* relative to the scramble controls (**p = 0.00262** following t-test). A second construct, the **Hep 3B-specific HBx sensor**, demonstrated a **13-fold increase** in *GFP signal* compared to scramble (**p = 0.00297** following t-test). Importantly, the **Hep 3B-specific sensor** maintained detection capacity even when challenged with a synthetic trigger containing up to **5% sequence mismatch**, reflecting resistance to naturally occurring viral sequence variation. Comparison of matched and partially matched triggers confirmed significant differences in *GFP output* (**p = 0.0233**), supporting the simultaneous specificity and universality of the **Broad design** (Figure 1, 2, and 3).

![Figure 1: HBx sensor validation](https://static.igem.wiki/teams/5802/resultsfinal/image8.avif)

**Figure 1. Validation of HBx sensor performance in HEK293T cells.**
HBx\_Broad sensor shows GFP fluorescence output in response to matched versus scrambled synthetic triggers. HBx\_Hep3B-specific sensor shows GFP fluorescence output in response to matched (HBx\_Broad trigger with 5% mismatch) versus scrambled synthetic triggers. White signals indicate GFP-positive cells; red signals represent sensor presence. Both sensors showed strong responses to matched triggers and negligible responses to scramble controls, confirming specificity of detection.

![Figure 2: Quantitative HBx evaluation](https://static.igem.wiki/teams/5802/resultsfinal/image3.avif)

**Figure 2. Single replicate quantitative evaluation of HBx sensor performance.** Flow cytometry analysis of GFP fluorescence in HEK293T cells transfected with HBx\_Broad and HBx\_Hep3B sensors in response to matched and mismatched synthetic triggers. Data was collected for 100,000 cell events and were gated on sensor-high cells. (Top) Histogram overlays show a clear rightward shift in GFP intensity for matched triggers (blue) compared to scramble controls (red), with the Hep3B sensor retaining activity even against 5% mismatched sequences. (Bottom) Tabulated mean fluorescence intensity (MFI) values demonstrate 13 fold and 23 fold increases in GFP output relative to scramble controls.

![Figure 3: HBx sensor discrimination](https://static.igem.wiki/teams/5802/resultsfinal/image4.avif)

**Figure 3. HBx sensor discrimination between matched and partially matched triggers.** Dot plot showing GFP fluorescence output in HEK293T cells transfected with HBx sensors in the presence of complete matched or partial matched synthetic triggers. Complete match triggers produced strong GFP activation, while partial match triggers (containing 5% mismatched sequence) still elicited a measurable, but reduced signal (p < 0.01). As expected, complete match triggers also produced a statistically significant increase in fluorescent intensity compared to partial match triggers (p=0.0233). Results confirm both the sensitivity and ability to tolerate limited sequence variation in viral sequences.

Next, we tested sensors targeting *hepatocellular carcinoma (HCC)-specific transcripts*, testing two targets strongly expressed in HCC, **GPC3 surface protein** and **AKR1B10 metabolic enzyme**. The **GPC3 sensor** showed clear activation in response to its respective trigger while remaining silent under scramble conditions (Figure 4). Similarly, the **AKR1B10 sensor** displayed strong discrimination between scramble and matched triggers, with event counts once again confirming specificity in functionality (Figure 4). Mean calculations of our flow cytometry values revealed that our **AKR1B10 sensor** exhibited a **20-fold increase** in *GFP fluorescence* in the top 1% of sensor expressing cells (**p = 0.08** following t-test) whereas **GPC3 sensor** exhibited a **15-fold increase** in *GFP fluorescence* (**p = 0.00262** following t-test) (Figure 5, 6, 7).

![Figure 4: HCC sensor validation](https://static.igem.wiki/teams/5802/resultsfinal/image1.avif)

**Figure 4. Validation of HCC-specific mRNA sensor performance in HEK293T cells.**
The GPC3 sensor showed GFP fluorescence output in response to matched versus scrambled synthetic triggers. The AKR1B10 sensor showed GFP fluorescence output in response to matched versus scrambled synthetic triggers. White signals indicate GFP-positive cells; red signals represent sensor presence. Both sensors showed strong responses to matched triggers and negligible responses to scramble controls, confirming specificity of detection.

![Figure 5: AKR1B10 quantitative evaluation](https://static.igem.wiki/teams/5802/resultsfinal/image2.avif)

**Figure 5. Single replicate quantitative evaluation of AKR1B10 sensor performance.** Flow cytometry analysis of GFP fluorescence in HEK293T cells transfected with the AKR1B10 sensor in response to matched and mismatched synthetic triggers. (Top) Histogram overlays show a clear rightward shift in GFP intensity for matched triggers (dark blue) compared to scramble controls (light blue). (Bottom) Tabulated mean fluorescence intensity (MFI) values demonstrate 20-fold increases in GFP output relative to scramble controls for this single replicate evaluation.

![Figure 6: GPC3 quantitative evaluation](https://static.igem.wiki/teams/5802/resultsfinal/image6.avif)

**Figure 6. Single replicate quantitative evaluation of GPC3 sensor performance.** Flow cytometry analysis of GFP fluorescence in HEK293T cells transfected with the GPC3 sensor in response to matched and mismatched synthetic triggers. (Top) Histogram overlays show a clear rightward shift in GFP intensity for matched triggers (bright orange) compared to scramble controls (light orange). (Bottom) Tabulated mean fluorescence intensity (MFI) values demonstrate 16-fold increases in GFP output relative to scramble controls, for the single replicate quantitative evaluation.

![Figure 7: GPC3 and AKR1B10 discrimination](https://static.igem.wiki/teams/5802/resultsfinal/image9.avif)

**Figure 7. GPC3 and AKR1B10 sensor discrimination between matched and mismatched triggers.** Dot plot showing GFP fluorescence output in HEK293T cells transfected with GPC3 and AKR1B10 sensors in the presence of complete matched or mismatched synthetic triggers. Complete match triggers produced strong GFP activation, while mismatch triggers elicited a negligible signal. GPC3 exhibited strong output signalling (p = 0.00262 following t-test)), while AKR1B10 exhibited a nonsignificant output signal (p = 0.08 following t-test statistical analysis). These findings demonstrate that the GPC3 sensor produces a sensitive and measurable output, in contrast to the weaker response observed with the AKR1B10 sensor.

We next tested whether combining the viral and tumor sensors in a **Boolean logic framework** could enable selective signal production, ultimately translating to a cell-targeted therapeutic response. The two halves of the full *GFP protein* were first co-transfected to assess **protein trans-splicing efficiency**. A strong *GFP output* resulted when both halves expressed under a constitutive promoter were co-transfected in *HEK293T cells* compared to negligible output produced in the cases of each transfected alone (Figure 9). When the **HBx and GPC3 sensors** were integrated into an **AND-gated circuit** controlling **split GFP**, fluorescence was observed only in the presence of both triggers (Figure 10). Neither input alone was sufficient to produce the signal. *HEK293T cells* transfected with both sensors and their triggers exhibited a **1.6-fold change** compared to a partial match containing only the **GPC3 trigger** and **1.7-fold change** compared to the partial match containing the **HBx trigger**. Relative to the trial containing a complete mismatch among both sensors, there was a **1.6-fold change** compared to the complete mismatch containing no triggers cognate to the sensors (Figure 11). These results demonstrate that the **dual-sensor framework** operates with strict **AND logic**, confirming compatibility of **RADAR-based components** in multi-input intracellular circuits for cell-targeted therapeutics.

![Figure 9: Split GFP reconstitution](https://static.igem.wiki/teams/5802/resultsfinal/image7.avif)

**Figure 9. Split GFP reconstitution validates sensor output design.** HEK293T cells were transfected with either spGFP1-10 alone, spGFP11 alone, or both simultaneously. No fluorescence was observed with either half alone, while measurable GFP signal was detected only when both fragments were co-expressed. This confirms proper reassembly of the split GFP protein for the AND-gated sensor to be tested.

![Figure 10: AND-gate microscopy validation](https://static.igem.wiki/teams/5802/resultsfinal/image10.avif)

**Figure 10. Microscopy validation of AND-gated GFP proof of concept.** HEK293T cells were transfected with GPC3 + HBx AND-gated sensors and co-transfected with either both GPC3 and HBx\_Broad triggers (dual match), just one of them, or neither of them (scramble). The dual match showed GFP output compared to negligible output in the remaining three samples. These results allow us to qualitatively validate the AND-gate sensor-output actuation design.

![Figure 11: AND-gate quantitative evaluation](https://static.igem.wiki/teams/5802/resultsfinal/image5.avif)

**Figure 11. Single replicate quantitative evaluation of AND-Gated sensor performance.**
Flow cytometry analysis of GFP fluorescence in HEK293T cells transfected with the GPC3-HBx AND-gated sensors in response to matched and mismatched synthetic triggers. Complete match triggers relative to both sensors (Specimen 4) produced the highest GFP output with a mean fluorescence of ~413.6 compared to all other samples, including complete mismatches and partial matches. Both partial mismatched and complete mismatched specimens showed lower signal relative to the dual match, confirming high specificity. These results demonstrate accurate distinction between matched and mismatched inputs, with a 1.6-fold activation observed only under complete match conditions. However, since cells from three technical replicates in a 48-well format were harvested and combined into one sample for sufficient cell counts to run flow cytometry, more biological and technical replicates will be conducted in the future to allow statistical tests to be done.

To move beyond proof-of-concept, we designed a **split IL-2 therapeutic output** to replace the **GFP split outputs**. Similar to the *GFP prototype*, upon activation of both sensors, it is expected that the **IL-2 fragments** will reconstitute to produce the functional protein, establishing that the system can be readily adapted from sensing to therapeutic actuation. This would validate the modularity of the **LiRA platform** for any case of cancer so long as two transcriptional inputs can be combined to produce the therapeutic protein of choice. We hope to validate this system in the future.

Finally, we are currently evaluating the tumor-specific **GPC3 sensor** in the *Hep3B cell line*, a *hepatocellular carcinoma line* that integrates *HBV sequences* and exhibits high **GPC3 expression**. We aim for the **GPC3 sensor** to display a measurable activation in the *Hep3B cell*, consistent with predictions from our dry lab algorithm, which evaluated **log-fold change** (HCC tissue relative to healthy counterparts) and **TPM values** for the *Hep3B cell line* to rank **GPC3** as the top transcript in this cell line. This cross-validation between computational and experimental findings will strengthen our confidence in both the design pipeline and biological performance in patient cases of HCC.

Collectively, these results demonstrate that LiRA can:

* Identified *viral/endogenous mRNA sequences* and validated **sensor function** for those sequences using high copy synthetic trigger mRNAs provided by transfection
* Integrate signals through an **AND-gated design** to deliver a cell-specific response
* Actuate a diagnostic (ex. **Split GFP**) as an actuator response

The system functioned as predicted in both engineered *HEK293T cells*, supporting the feasibility of **LiRA** as a **programmable platform** for selective recognition and treatment on a cancer case lacking specific biomarkers given any two *transcriptomic signatures*.

### Discussion

The successful results in the HEK293T demonstrate that two independent transcripts can be combined to drive selective activation of a therapeutic output, offering an alternative to conventional biomarker based therapies, which often suffer from poor specificity (Chakraborty & Rahman, 2012). By requiring the simultaneous presence of both, a viral integration product (HBx) and an HCC-associated transcript (GPC3), our LiRA system reduces the risk of off-target activation in healthy tissue.

One of the important findings from this work is the ability of our viral HBx sensor to maintain detection despite up to 5% sequence mismatch. Given the high mutation rate of HBV, this ensures the viralescape variants are unlikely to undermine the HBx sensor we designed using a highly conserved 3'UTR HBx sequence. In parallel, the GPC3 and AKR1B10 sensors were validated as reliable tumor-specific inputs, with GPC3 further confirmed as a high-confidence target in Hep3B cells akin to diseased cells. Validation of an AND gate using these sensors led to a more translational step. Our transition from a diagnostic GFP output to a therapeutic split IL-2 protein represents an application of this model. Il-2 is a clinically established cytokine with known immunostimulatory effects and its modular assembly through the split protein design gives us a framework for adapting LiRA to diverse therapeutic efforts. This adaptability positions LiRA not only as a way to diagnose cancers lacking specific biomarkers, but also as a system capable of real-time detection and treatment within the diseased cells themselves.

In summary, we developed a programmable intracellular RNA logic-gated therapeutic platform capable of both sensing and actuation. By combining detection of multiple RNA transcripts to therapeutic protein production, LiRA offers an alternative method to selectively target cancers that lack a single defining biomarker. Our results show proof of concept in engineered HEK293T cells, supporting the logic of LiRA as a modular system for precise cancer therapy.

#### Limitations and Future Directions

That said, some limitations remain. First, sensors require relatively high transcript expression to achieve activation and measurable output, which may limit applicability in tumors with lower biomarker abundance. Although our computational projects have developed a screening method capable of highlighting sequences highest in transcript prevalence for a given cell line, additional testing across a broader panel of HCC cell lines will be necessary to establish generalizability. Second, while IL-2 served as an effective demonstration of a therapeutic application using our modular system, future work should expand to alternative outputs, including pro-apoptotic proteins or immunomodulatory effectors, to diversify therapeutic potential and cover a broader range of cancers the system can combat. Finally, delivery remains a discussion for the system. Given this prototype, we envision the system could be administered intravenously to target the liver, where HBV-associated HCC arises. However, adapting this approach to other cancers would require new strategies to ensure delivery to the diseased tissues. Regardless of the specificity of delivery, the system's precision in actuation eases the worries of off-target harm.

Despite these questions, LiRA demonstrates the promise of RNA logic-gated systems for cancer therapy, enabling both precise detection and therapeutic response in a single system. With further optimization, this approach could be extended beyond HBV-associated HCC to other cancers defined by multiple RNA signatures.

### References

1. Chakraborty, S., & Rahman, T. (2012). The Difficulties in Cancer Treatment. *Ecancermedicalscience*, 6(16). https://doi.org/10.3332/ecancer.2012.ed16
2. Feitelson, M. A., Arzumanyan, A., Spector, I., & Arvin Medhat. (2022). Hepatitis B x (HBx) as a Component of a Functional Cure for Chronic Hepatitis B. *Biomedicines*, 10(9), 2210–2210. https://doi.org/10.3390/biomedicines10092210
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