iGEM 2025 — Team thessaloniki

# L A B

# N O T E B O O K

## *"I write to discover what I know."*

## *- Flannery O'Connor*

### Our Lab Notebook

---

If the Lab Notebook below does not render, you can view it [here](https://static.igem.wiki/teams/5891/notebook/lab-notebook-igem-thessaloniki-2025.pdf).

![](https://static.igem.wiki/teams/5891/accessibilitybutton/accessibility.webp)

Accesibility Bar

![](https://static.igem.wiki/teams/5891/accessibilitybutton/font-size.webp)Increase Font Size

![](https://static.igem.wiki/teams/5891/accessibilitybutton/hide-images.webp)Hide Images

![](https://static.igem.wiki/teams/5891/accessibilitybutton/paragraph-gaps.webp)Paragraph Distance

![](https://static.igem.wiki/teams/5891/accessibilitybutton/line-distance.webp)Line Distance

![](https://static.igem.wiki/teams/5891/accessibilitybutton/dyslexia-friendly.webp)Dyslexia Friendly

![](https://static.igem.wiki/teams/5891/accessibilitybutton/paragraph-alignment.webp)Text Allignment

![](https://static.igem.wiki/teams/5891/accessibilitybutton/links.webp)Links

![](https://static.igem.wiki/teams/5891/accessibilitybutton/pointer.webp)Pointer

#####

Close

# E N G I N E E R I N G

# S U C C E S S

## *"The only real mistake is the one from which we learn nothing."*

## *- Henry Ford*

### Overview

---

The applications of the engineering cycle were ****instrumental to the progress and success of our project****, guiding both our ****wet lab and dry lab efforts****. Through multiple ****Design-Build-Test-Learn (DBTL)**** iterations, we systematically ****refined the key components of our modeling**** approach, while simultaneously ****addressing and resolving experimental challenges in the laboratory****. Each iteration provided valuable insights that informed the next, ensuring a continuous process of improvement and optimization.

****In the Wet Lab****, our engineering cycle iterations focused on optimizing experimental design and validating gene silencing efficiency in MEC-1 cells:

* *Iteration 1*: Benchmark MEC-1 response to GSK126 inhibitor
* *Iteration 2*: Establishing transfection feasibility and reagent tolerance in MEC-1
* *Iteration 3*: INTERFERin titration for cytotoxicity optimization
* *Iteration 4*: Repetition 2-Establishing transfection feasibility and reagent tolerance in MEC-1
* *Iteration 5*: Functional validation of BCL2 knockdown/Expression Readouts

****In the Dry Lab****, our iterations aimed to enhance the performance and accuracy of our siRNA efficacy prediction pipeline through progressive data refinement and model advancement:

* *Iteration 1*: Training Dataset Creation and Designing Introductory ML Models
* *Iteration 2*: Improving Training Dataset and Employing A More Advanced ML Model
* *Iteration 3*: Employing A Different Advanced ML Model

*Click on each button to review our Engineering Cycle Iterations!*

![](https://static.igem.wiki/teams/5891/engineering/wet-engineering.webp)
![](https://static.igem.wiki/teams/5891/engineering/dry-engineering.webp)

![](https://static.igem.wiki/teams/5891/engineering/iteration-1-1.avif)
![](https://static.igem.wiki/teams/5891/engineering/iteration-2-1.avif)
![](https://static.igem.wiki/teams/5891/engineering/iteration-3-1.avif)
![](https://static.igem.wiki/teams/5891/engineering/iteration-4-1.avif)
![](https://static.igem.wiki/teams/5891/engineering/iteration-5-1.avif)

*Click on each button to review our Wet Lab Engineering Cycle Iterations!*

![](https://static.igem.wiki/teams/5891/engineering/iteration-1-1.avif)
![](https://static.igem.wiki/teams/5891/engineering/iteration-2-1.avif)
![](https://static.igem.wiki/teams/5891/engineering/iteration-3-1.avif)

*Click on each button to review our Dry Lab Engineering Cycle Iterations!*

### Iteration 1: Benchmark MEC-1 response to GSK126 inhibitor

---

![](https://static.igem.wiki/teams/5891/engineering/design.avif)
![](https://static.igem.wiki/teams/5891/engineering/build.avif)
![](https://static.igem.wiki/teams/5891/engineering/test.avif)
![](https://static.igem.wiki/teams/5891/engineering/learn.avif)

# Design

---

GSK126 is a selective small-molecule and an S-adenosyl methionine competitive inhibitor that inhibits EZH2 methyltransferase, which is often observed to be dysregulated in cancer. MEC-1 cells were cultured and exposed to increasing concentrations of GSK126 to observe potential changes in cell viability, proliferation, or morphology.

This step was essential not only to understand how MEC-1 cells respond to pharmacological treatment but also to gain practical familiarity with their handling, culture dynamics, and baseline behavior, enabling subsequent comparison with the cellular effects observed following siRNA treatment.

# Build

---

The experiment was conducted in accordance with the protocol provided in our [Experiments Page](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27experiments%27%29%20%7D%7D).

* MEC-1 cells were seeded in a 24-well plate at optimal density following centrifugation and resuspension.
* Cells treated with a gradient of GSK126 concentrations (nM).

+ 0
+ 5
+ 10
+ 20
+ 40

* Controls included untreated cells and DMSO vehicle (0.2–0.5% final concentration, non-toxic range).
* 2 technical replicates were prepared for each condition.
* Incubation for 24 and 48 hours at 37 °C, 5% CO₂.

More detailed descriptions of the experimental setup, dilutions, and handling steps can be found in our [Lab Notebook](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27notebook%27%29%20%7D%7D).

# Test

---

The effects of the inhibitor were evaluated through:

* Microscopic observation of morphology and culture homogeneity.
* Cell viability and proliferation capacity assessment using Trypan Blue staining and Neubauer chamber counts.
* 3 technical replicates were measured at both 24- and 48-hour timepoints to track time-dependent responses.

# Learn

---

From this cycle, we determined a concentration range that can be effectively paired with siRNA treatments, allowing us to fine-tune between cytostatic and cytotoxic responses based on the specific goals of each experiment.

### Iteration 2: Establishing transfection feasibility and reagent tolerance in MEC-1

---

![](https://static.igem.wiki/teams/5891/engineering/design.avif)
![](https://static.igem.wiki/teams/5891/engineering/build.avif)
![](https://static.igem.wiki/teams/5891/engineering/test.avif)
![](https://static.igem.wiki/teams/5891/engineering/learn.avif)

# Design

---

The goal of this experimental cycle was to establish a reliable method for delivering siRNAs into MEC-1 cells and to evaluate whether this approach could achieve functional gene silencing of BCL-2. Given that MEC-1 is a suspension cell line with known sensitivity to lipid-based transfection reagents, particular attention was placed on identifying conditions that balance efficient uptake with minimal cytotoxicity.

# Build

---

* MEC-1 cells were cultured until viability reached ≥90% and then were seeded into 24-well plates.
* siRNAs were prepared at working concentrations.
* siRNAs complexed with INTERFERin in Opti-MEM and incubated to allow complex formation.
* Experimental layout included:

+ Concentration gradient: 5–50 nM.
+ Controls: untreated, INTERFERin-only, scrambled siRNA, siRNA-only.

* Plates were incubated at 37 °C, 5% CO₂ for up to 96 h.

Further procedural details are provided in the [Lab Notebook](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27notebook%27%29%20%7D%7D).

# Test

---

* Samples were collected at 24, 48, 72, and 96 h for:

+ Cell viability and cell proliferation capacity assays (Trypan Blue)
+ Cy3 fluorescence uptake
+ Microscopic observation of morphology and culture homogeneity

# Learn

---

Transfection of MEC-1 cells with BCL-2 siRNAs revealed an inhibitory effect on cellular proliferation. At early timepoints (24 h), both proliferation and viability were reduced across all conditions, with the strongest inhibition observed at higher concentrations (50 nM) and in the INTERFERin-only group. However, at later timepoints, cells exhibited partial recovery of growth and viability, indicating an adaptive response following initial treatment stress, which appeared to be primarily associated with the transfection reagent rather than the siRNA itself.

These effects were largely cytostatic, reflecting transient stress responses rather than overt cytotoxicity. Over time, cells demonstrated progressive recovery: by 72 h, morphology and viability had stabilized, and by 96 h, proliferation in all groups, including high-concentration and INTERFERin-only conditions, surpassed that of the control. Simultaneously, indicates that MEC-1 cells adapt to INTERFERin exposure and restore normal growth dynamics following the initial stress phase.
From this cycle, we established that concentrations in the 5–20 nM range provide a balance between effective transfection and minimal long-term impact on cell viability, forming the basis for subsequent functional silencing studies, with 20 nM showing the most favorable results. However, the cytotoxicity observed at higher INTERFERin volumes prompted a dedicated titration experiment to optimize reagent-to-cell and siRNA ratios and enhance overall cellular tolerance.

### Iteration 3: INTERFERin titration for cytotoxicity optimization

---

![](https://static.igem.wiki/teams/5891/engineering/design.avif)
![](https://static.igem.wiki/teams/5891/engineering/build.avif)
![](https://static.igem.wiki/teams/5891/engineering/test.avif)
![](https://static.igem.wiki/teams/5891/engineering/learn.avif)

# Design

---

This cycle aimed to determine the optimal concentration range of INTERFERin that allows efficient siRNA delivery while minimizing cytotoxicity. MEC-1 cells were exposed to gradient volumes of INTERFERin. Cell viability, growth, and morphology were assessed over time to distinguish transient stress effects from sustained toxicity.

# Build

---

* MEC-1 cells were counted and plated in 24-well plates at uniform density.
* Master mixes are prepared by combining INTERFERin with Opti-MEM and incubating for 15 minutes to allow complex formation.
* Gradient of INTERFERin concentrations (μL) tested in parallel across technical replicates.

+ 1
+ 2
+ 3
+ 5

* Controls: untreated cells.
* Plates were incubated at 37 °C, 5% CO₂ for 48 h.

Further procedural details are provided in the [Lab Notebook](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27notebook%27%29%20%7D%7D).

# Test

---

* Cell viability was measured at 24 h and 48 h across all INTERFERin concentrations.
* Cell proliferation trends were recorded relative to controls.
* Microscopic assessment for stress indicators (aggregation, clustering, reduced viable cell numbers).

# Learn

---

Treatment of MEC-1 cells with INTERFERin demonstrated a dose-dependent inhibitory effect on cellular proliferation. Smaller volumes were associated primarily with cytostatic outcomes consistent with a temporary arrest of cell division, but higher volumes (5 μL) showed cytotoxicity. When comparing cultures seeded at different densities, the inhibitory effect was slightly less pronounced at 300,000 cells/mL than at 150,000 cells/mL, likely due to reduced effective dose per cell and the presence of survival-supporting microenvironmental factors.

INTERFERin exhibits cytotoxic/cytostatic effects at high doses. Its main negative impact at larger volumes (3–5 µL) is an inducible and reversible arrest of the cell cycle, rather than induction of cell death. This suggests that the observed effects are likely non-permanent and that MEC-1 cells can recover once the stress is alleviated.

From this cycle, we established an optimal working range of INTERFERin volumes that balances efficient transfection with minimal cytotoxicity and an optimal cell seeding setup, providing a reliable basis for downstream siRNA delivery experiments.

### Iteration 4: Repetition 2-Establishing transfection feasibility and reagent tolerance in MEC-1

---

![](https://static.igem.wiki/teams/5891/engineering/design.avif)
![](https://static.igem.wiki/teams/5891/engineering/build.avif)
![](https://static.igem.wiki/teams/5891/engineering/test.avif)
![](https://static.igem.wiki/teams/5891/engineering/learn.avif)

# Design

---

The objective of this second repetition was to validate and refine the conditions established in the initial siRNA transfection experiment. As MEC-1 cells are highly sensitive to lipid-based transfection reagents, we aimed to confirm the reproducibility of siRNA uptake and BCL2 gene silencing under the optimized parameters identified previously. Repeating the experiment allowed us to assess consistency across independent runs, strengthen the reliability of our observations, and further define the balance between efficient delivery and minimal off-target cytotoxic effects.

# Build

---

* MEC-1 cells were seeded into 24-well plates (200 µL/well).
* Eight experimental conditions were prepared with Master Mixes (Opti-MEM, siRNA at varying concentrations, including Cy3-labeled constructs, and INTERFERin).
* Complexes were incubated for 15 min at room temperature before addition to cells.
* Two plates were prepared for each time point (6 h, 24 h, 48 h):

+ Plate A: RNA extraction + cellular uptake
+ Plate B: protein extraction
+ Medium was supplemented at 6 h post-transfection to reach a final volume of 1 mL/well.

Further procedural details are provided in the [Lab Notebook](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27notebook%27%29%20%7D%7D).

# Test

---

* ****Cell viability & proliferation**** were measured at 6 h, 24 h, and 48 h using Trypan Blue staining and Neubauer chamber counting.
* ****Cell morphology**** was assessed by microscopy at each time point, with representative images captured.
* ****siRNA uptake**** was quantified using Cy3 fluorescence from one-third of the samples at each time point.
* ****RNA analysis****: remaining cells from RNA plates were pelleted for RNA extraction, and later processed for qPCR to measure BCL2 expression relative to reference genes.
* ****Protein analysis****: 300,000 cells per condition collected from protein plates, pelleted for downstream protein extraction and BCL2 assessment.

All conditions were tested in replicates to ensure reproducibility.

# Learn

---

Transfection of MEC-1 cells with BCL2 siRNAs revealed a concentration-dependent pattern of uptake and processing. At 20 nM, a distinct fluorescence peak was observed at 6 h, followed by a gradual decline and stabilization by 24–48 h. This dynamic profile indicates efficient internalization and intracellular processing of the siRNA. In contrast, at 50 nM, fluorescence increased continuously over time, suggesting overloading of the delivery system and sequestration of siRNAs in vesicles, accompanied by mild growth inhibition. Lower concentrations (5–10 nM), scrambled siRNA, and INTERFERin-only controls exhibited no significant fluorescence signal. Importantly, cell viability remained consistently high (>85–90%) across all conditions, demonstrating that the treatments influenced proliferation dynamics rather than inducing cytotoxicity.

Our observations demonstrated that higher siRNA concentrations correlated with increased uptake efficiency, confirming successful delivery into MEC-1 cells. Among the range of concentrations tested, the 20 nM and 50 nM treatments produced similar cellular outcomes, including slightly reduced viability and evident cell cycle arrest. Notably, the 20 nM concentration, representing a milder treatment, achieved comparable functional results to 50 nM, suggesting it may serve as a more optimal and less cytotoxic condition for subsequent experiments. These findings underscore the importance of balancing delivery efficiency with cellular tolerance, guiding further optimization of transfection parameters.

### Iteration 5: Functional validation of BCL2 silencing/Expression Readouts

---

![](https://static.igem.wiki/teams/5891/engineering/design.avif)
![](https://static.igem.wiki/teams/5891/engineering/build.avif)
![](https://static.igem.wiki/teams/5891/engineering/test.avif)
![](https://static.igem.wiki/teams/5891/engineering/learn.avif)

# Design

---

This cycle aimed to confirm that optimized transfection conditions translate into functional BCL2 silencing at the transcript (and, where feasible, protein) level. Cells across all conditions were harvested at 6h, 24 h, and 48 h for total RNA isolation, cDNA synthesis, and qPCR quantification of BCL2 normalized to stable reference genes (e.g., YWHAZ). To confirm that the qPCR amplified the correct product and total RNA integrity, we checked the samples by gel electrophoresis to verify the RNA pattern and the fact that the bands were specific and of the expected size. Although cell pellets were prepared for downstream protein analysis, Western blot experiments could not be completed before the wiki freeze due to time constraints. These assays remain part of our planned follow-up validation to corroborate transcript-level findings at the protein level.

# Build

---

* MEC-1 cells were transfected under optimized conditions (20 nM BCL2 siRNA with INTERFERin).
* Conditions included: untreated cells, INTERFERin-only, non-targeting siRNA (scrambled), siRNA-only, and BCL-2 targeting siRNA.
* RNA plates processed:

+ Cells were pelleted and total RNA extracted.
+ cDNA was synthesized for downstream qPCR.
+ qPCR performed for BCL2 and reference genes (e.g., p21, YWHAZ), with samples run in replicates.

* Specificity of amplicons verified by gel electrophoresis.
* Parallel protein pellets were prepared for Western blotting; however, due to time constraints, these assays could not be completed before the wiki freeze.

Further procedural details are provided in the [Lab Notebook](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27notebook%27%29%20%7D%7D).

# Test

---

* ****qPCR analysis**** performed on cDNA from BCL2-siRNA, non-targeting siRNA, INTERFERin-only, and untreated groups.
* ****Relative expression**** calculated using the ΔΔCt method with YWHAZ as the reference gene.
* ****Gel electrophoresis**** was run to verify the specificity and expected size of qPCR amplicons, as well as check total RNA status and its integrity.
* ****Fluorescence analysis****: siRNA uptake was monitored at every tested concentration using Cy3-labeled siRNAs. Fluorescence intensity was measured over time with a spectrometer to assess internalization dynamics.
* ****Viability and growth monitoring**** were performed at the same timepoints to confirm that reduced BCL2 expression was not due to cytotoxicity.
* ****Replicates**** were included across all groups to ensure reproducibility.
* ****Protein pellets**** were reserved for Western blotting, which could not be carried out before the wiki freeze.

# Learn

---

qPCR analysis supported these findings, showing a partial reduction in BCL2 mRNA levels (~30%) at 20 nM and a more pronounced decrease (~50%) at 50 nM, although these effects did not reach statistical significance due to experimental variability and technical constraints. Given that the total RNA level of quality and quantity of the extracted template was suboptimal, it is likely limiting detection sensitivity and contributing to the observed variability. Nevertheless, the biological evidence aligns strongly with the expected silencing effect: in the cells that successfully internalized the siRNA, clear signs of slightly reduced viability and evident cell cycle arrest were observed. These results validate the functional activity of the siRNA treatment and underscore the need to scale up the experimental setup to obtain higher-quality RNA templates, thereby enabling more robust and statistically supported downstream analyses.

Furthermore, fluctuations of the YWHAZ reference gene in negative controls highlighted the necessity of re-examining its suitability as a normalizer. Future validation efforts will therefore include the use of additional biological replicates and assessment of protein-level expression to confirm the stability of normalization strategies.

### Iteration 1: Training Dataset Creation and Designing Introductory ML Models

---

![](https://static.igem.wiki/teams/5891/engineering/design.avif)
![](https://static.igem.wiki/teams/5891/engineering/build.avif)
![](https://static.igem.wiki/teams/5891/engineering/test.avif)
![](https://static.igem.wiki/teams/5891/engineering/learn.avif)

# Design

---

The first step, before searching for training data, was to define the purpose of our machine learning model and what the expected input(s) and output(s) would be. Our goal from the beginning was to build a model that would predict the siRNA silencing efficiency. We defined this siRNA silencing efficiency to be the percentage of the target mRNA knockdown, therefore the output of the model would be a number in the range [0, 1]. where 0 indicates no silencing effect and 1 indicates maximum silencing effect. In other words, the higher the model output is, the more promising the candidate siRNA is.

Regarding the training data, we decided that our dataset should contain:

* ****The siRNA antisense strand: 19 nt length, without overhangs, in 5'-3' orientation****
* ****Exact target mRNA site****: 19 nt sequence
* ****Extended target mRNA site****: 57 nt sequence containing the 19 nt target site with 19 nt flanking regions on each side
* ****Efficacy score****: Normalized value from 0 (ineffective) to 1 (highly effective) representing silencing performance
* ****Data source****: The originating dataset source publication (from 11 public sources)
* ****Cell line****: The experimental cell system used for efficacy measurements
* ****Engineered features****: Computed properties for sequence analysis

As input to the model we decided to use: the siRNA antisense strand, the extended target mRNA site and the siRNA silencing efficacy score. The other data would be taken into consideration for the creation of the training dataset and its split into training and testing sets. For the creation of a harmonized dataset, we made sure that all siRNA sequences followed the below-mentioned rules:

* ****Sequence uniformity****: All siRNA antisense strands are 19 nt in length, oriented 5'→3', and reverse-complement to their corresponding exact target mRNA sites
* ****Efficacy normalization****: All efficacy values are normalized to a 0–1 scale, representing the percentage of target mRNA knockdown (0 = no silencing, 1 = complete silencing)
* ****Uniqueness****: Each siRNA-target mRNA pair appears only once in the dataset

For this model, we made the following assumptions:

* siRNA function is decided primarily by the antisense (guide) strand: The target recognition inside RISC is facilitated only by the antisense strand, which is assembled into Ago2.
* Availability of target mRNA sites influences silencing efficacy: siRNAs cannot bind to sequences that are buried in stable secondary structures or protein complexes. By incorporating the mRNA region surrounding the binding site, we are able to compensate for local folding and accessibility influencing binding.

For the first engineering cycle of the machine learning model development, we decided to employ not one, but two basic machine learning models: Support Vector Regression (SVR) and XGBoost and evaluate these models' performance. In order to transform the input data (siRNA and target mRNA sequences) into a numerical form that these models can process, we decided to employ the k-mer method, and used four different values for k (k=2, 3, 4, 5), to evaluate which value of k ensures greater model performance.

As a metric for the model performance evaluation, we decided to use the Mean Squared Error (MSE).

# Build

---

Initially, we had to build our training dataset, which needed to be rich and balanced in order to ensure great data quality and enhance model performance. We conducted literature review and came across 5 different siRNA silencing efficacy datasets: Huesken, Takayuki, Mixset, Shabalina and Simone. The Mixset dataset comprises siRNA antisense sequences from 7 different sources: Amarzguioui, Harborth, Hsieh, Khvorova, Reynolds, Vickers, Ui-Tei. We then preprocessed and curated the data (more information in the dropdown buttons below), resulting in the creation of a harmonized training dataset that comprises 4098 siRNA antisense strands, along with their respective silencing efficacy value.

**Preprocessing Huesken, Mixset and Takayuki Datasets**

We obtained data from these sources through previous publications on siRNA efficacy prediction, which provided .csv files containing: the siRNA antisense strand, the extended target mRNA sequence and siRNA efficacy values.

****Sequence orientation standardization****: We extracted the central 19nt target mRNA sequence and determined strand orientation by checking whether the siRNA was complement (3'-5') or reverse-complement (5'-3') to the target site. Huesken and Mixset sequences were already in 5'-3' orientation, while Takayuki sequences were 3'-5' and required reversal before integration into siRBencht.

****Efficacy normalization****: All efficacy values from these sources were already normalized to the [0,1] range, where higher values indicate stronger silencing activity.

****Cell line annotation****: Cell lines were H1299 for Huesken and HeLa for Takayuki. Since the Mixset dataset aggregated sequences from 7 different sources, we traced each sequence back to its original publication to assign the appropriate experimental cell line for each entry.

**Preprocessing Shabalina Dataset**

The siRNA dataset from Shabalina et al. comprised 653 antisense strand sequences with reported gene-silencing activity values, GenBank accession numbers for target mRNAs and target site coordinates. Unlike other sources, efficacy values were reported as non-negative real numbers where 0 indicated complete knockdown and higher values represented weaker silencing, the inverse of our desired scale.

We used the provided GenBank accession numbers and genomic coordinates to retrieve extended target mRNA sequences.However, we discovered that siRNA sequences were not always fully complementary to the reported target regions. To resolve this, we downloaded complete target mRNA sequences and performed exhaustive searches to identify exact complementary sites for each siRNA. This approach successfully located complementary targets for 650 of 653 siRNAs; the remaining 3 were excluded from siRBench.

We first normalized activity values to the [0,1] range. To align with other datasets where higher values indicate stronger silencing, we applied the transformation 1−α for each normalized activity value α. This ensured that all siRBench efficacy scores follow the same interpretation: 0 represents no silencing and 1 represents complete knockdown.

All Shabalina sequences were in 5'-3' orientation, and experiments were conducted in HeLa cells.

**Preprocessing Simone Dataset**

The Simone dataset comprises 322 siRNA sequences obtained from the siRNADiscovery GitHub repository. The data was provided in three separate files: two FASTA files containing the siRNA sequences and the full-length target mRNA sequences, and one CSV file mapping siRNA-target pairs (via FASTA IDs) to efficacy values. We merged these files into a single CSV file containing three columns: the siRNA sequence, the extended target mRNA site, and the efficacy value. All siRNA sequences from the Simone were21nt in length.To maintain consistency with our 19 nt standard, we trimmed the last 2nt of the siRNA sequence.

The efficiency values were already normalized to the [0,1] range with higher values indicating stronger silencing, matching siRBench specifications. All sequences were in 5'-3' orientation, and experiments were conducted in Hep3B cells.

**Checking for Duplicates**

Duplicate removal was the final essential step before finalizing our dataset. We defined duplicates as siRNA pairs sharing identical antisense strands and extended target mRNA sites.

We first examined each dataset individually. The Huesken, Takayuki and Simone datasets contained no duplicates. The Shabalina dataset contained 3 duplicate siRNAs with identical efficacy scores; we retained one sequence from each pair. Furthermore, the Mixset dataset contained 8 duplicate pairs, from different source studies with conflicting efficacy scores. For these cases, we retained the sequence from the less-represented source to maintain dataset balance, excluding 4 additional siRNAs from siRBench.

We identified 398 duplicate pairs between Mixset and Shabalina with conflicting efficacy values due to different experimental conditions and cell lines. We retained the Mixset-derived efficacy values, as this dataset is more widely used as a benchmark in siRNA prediction studies.

This comprehensive preprocessing procedure yielded a balanced, normalized and high-quality training dataset maximizing the number of unique, reliable siRNA sequences.

Before model development, we strategically split siRBench into training and test sets based on experimental cell line rather than using random splitting methods like scikit-learn's train\_test\_split function. This cell line-based approach ensures the model is trained and evaluated on different biological contexts, preventing information leakage and better reflecting real-world scenarios where predictions must generalize to new cellular environments. The training set comprises 3,408 siRNAs with efficacy measurements from H1299 and HeLa cell lines, while the test set contains 690 siRNAs from all other cell lines. The training set was further randomly divided into training (90%) and validation (10%) subsets.

The final step of the “Build” step was to write the code for the SVR and XGBoost models. The main parts of the code are shown in the pictures below.

![](https://static.igem.wiki/teams/5891/engineering/svr-model-code.webp)

**Figure 1:** Code for the Development of the Support Vector Regression Model

![](https://static.igem.wiki/teams/5891/engineering/xgboost-code-photo.webp)

**Figure 2:** Code for the Development of the XGBoost Model

# Test

---

As a metric for the model performance evaluation, we used the Mean Squared Error (MSE). The table below includes the calculated MSE (with 4 decimal digits) for the different values of k that we tried, for both machine learning models.

|  |  |  |
| --- | --- | --- |
|  | SVR Model | XGBoost Model |
| k=2 | 0,0702 | 0,0733 |
| k=3 | 0,0653 | 0,0738 |
| k=4 | 0,0733 | 0,0749 |
| k=5 | 0,0792 | 0,0764 |

# Learn

---

Through this cycle, we ****successfully built a harmonized dataset**** by integrating multiple different datasets, and preprocessing and curating the data. We also managed to ****build two different machine learning models**** for siRNA silencing efficacy prediction and evaluated their performance: from the above MSE metric table, we can conclude that both models showcased similar efficiency, while the greatest performance was accomplished when the Support Vector Regression Model was employed and the k value for the k-mer method was equal to 3.

We moved on to complete more engineering cycles for this model development, with the goal of improving model performance. In this step, we brainstormed ways to improve model efficiency and decided to implement these two ideas:

* Conduct literature review on ****features that affect the siRNA silencing efficacy****, and apply feature engineering in order to help the model make predictions based not only on the input sequences, but also on engineered thermodynamic, structural and sequence features.
* Conduct literature review for ****more advanced machine learning models**** to employ for this task and therefore, improve efficiency.

### Iteration 2: Improving Training Dataset and Employing A More Advanced ML Model

---

![](https://static.igem.wiki/teams/5891/engineering/design.avif)
![](https://static.igem.wiki/teams/5891/engineering/build.avif)
![](https://static.igem.wiki/teams/5891/engineering/test.avif)
![](https://static.igem.wiki/teams/5891/engineering/learn.avif)

# Design

---

Based on the findings of the previous engineering cycle, we firstly moved on to conduct ****literature review on the features that affect siRNA silencing efficacy****, with the goal of applying feature engineering. We hypothesized that the addition of engineered (pre-calculated) sequence features to the model input would result in the improvement of the model performance, since the model would be able to make predictions based on more features that previously could not calculate on its own.

Through extensive literature review, we ****identified 100 thermodynamic and structural features**** to enrich our training dataset. All feature values are rounded to three decimal places. The dropdown button below lists these features with descriptions of their biological significance.

**siRNA Silencing Efficacy Features**

Thermodynamic Features

* ****ends****: Terminal asymmetry metric comparing 5′ and 3′ end stability of the siRNA duplex
* ****DG\_total****: Total Gibbs free energy (ΔG) of duplex formation calculated from nearest-neighbor parameters
* ****DH\_total****: Total enthalpy change (ΔH, kcal/mol) of duplex formation from nearest-neighbor parameters
* ****DG\_pos1..18****: Per-step stacking ΔG along the duplex at each of the 18 nearest-neighbor positions in the 19-bp region
* ****DH\_pos1..18****: Per-step stacking ΔH at corresponding positions

Structural Constraint Energies (RNAfold/RNAcofold)

* ****single\_energy\_total****: Minimum free energy (MFE) of the isolated guide strand (RNAfold)
* ****single\_energy\_pos1..19****: Position-specific accessibility of each nucleotide in the guide strand (RNAfold)
* ****duplex\_energy\_total****: Hybridization ΔG for the guide-target duplex (RNAcofold)
* ****duplex\_energy\_sirna\_pos1..19****: Per-nucleotide contribution of the guide strand to duplex energy at each aligned position (RNAcofold)
* ****duplex\_energy\_target\_pos1..19****: Per-nucleotide contribution of the target site to duplex energy (RNAcofold)

RNAup Accessibility Features

* ****RNAup\_open\_dG****: Energy cost to unpair interacting regions on both guide and target strands prior to binding
* ****RNAup\_interaction\_dG****: Hybridization energy gained upon binding of unpaired regions

For the model fitting, we used 90% of the training dataset for model training and the rest 10% for the validation set.

Afterwards, we proceeded to search for more advanced machine learning models to employ for our task. We deemed that by using ****a model that adapts better to the characteristics of our task**** (multiple calculated features, medium-sized dataset, potential non-linear relationships), the model performance would be enhanced. After literature review, we selected ****LightGBM (Light Gradient Boosting Machine)****, an efficient implementation of gradient boosting decision trees.

Unlike linear models, ****LightGBM captures non-linear feature interactions****, where silencing depends on the complex interplay of thermodynamic stability, accessibility, and positional effects rather than simple linear rules. LightGBM builds ****an ensemble of decision trees sequentially****, with each tree correcting errors from previous ones. Its leaf-wise growth strategy expands branches that maximize error reduction, enabling the model to learn highly specific patterns. Efficiency optimizations including histogram-based feature binning, gradient-based sampling, and exclusive feature bundling make it faster and more memory-efficient than other gradient boosting implementations.

The advantages for our dataset are:

* ****Efficiently handles medium-sized datasets (~4,100 siRNAs)**** without requiring the massive training sets needed for deep learning.
* ****Naturally processes correlated and position-specific features**** (DG\_pos1..18, DH\_pos1..18, duplex energies) to identify non-linear patterns that simpler models cannot capture.

# Build

---

After completing the “Design” step of this engineering cycle, we moved on to write code for both feature engineering and LightGBM model development. ****Full documentation**** of these aspects of our modeling are available in our [GitLab Repository](https://gitlab.igem.org/2025/software-tools/thessaloniki/-/tree/main?ref_type=heads).

****Full documentation**** for the final training dataset creation can be found in our [GitLab Repository](https://gitlab.igem.org/2025/software-tools/thessaloniki/-/tree/main/data?ref_type=heads).

****Full documentation**** for LightGBM Model development can be found in our [GitLab Repository](https://gitlab.igem.org/2025/software-tools/thessaloniki/-/tree/main/siRBench-LightGBM?ref_type=heads).

# Test

---

The next step after we built our enriched training dataset and trained the model was to evaluate the model performance. We used the ****Mean Squared Error (MSE)**** metric, and added the ****Pearson Correlation Coefficient (R)**** metric, in order to measure the linear correlation between the predicted and the actual silencing efficacies. The table below shows the metrics values.

|  |  |
| --- | --- |
|  | LightGBM Model |
| Mean Squared Error (MSE) | 0,0529 |
| Pearson Correlation Coefficient (R) | 0,5454 |

# Learn

---

Compared to the previous models we implemented, we observed a ****clear improvement in predictive accuracy****, as the Mean Squared Error (MSE) decreased to 0,0529. A lower MSE indicates that the predicted siRNA efficacies are, on average, ****closer to the experimental values****, meaning the model has become more precise.

Additionally, the Pearson correlation coefficient (R = 0.5454) shows that there is a ****moderate positive linear correlation**** between our predictions and the true values. In practice, this means that the model successfully captures the trend of siRNA activity: when experimental efficacy is high, predicted efficacy also tends to be high, and vice versa. Although not perfect, this correlation confirms that the model is learning meaningful biological patterns rather than random noise.

At this stage, our LightGBM model already provided satisfactory results, showing both a reduced prediction error and a meaningful correlation with experimental values. We could have stopped here and considered this as our final predictive tool. However, we wanted to ****go the extra mile and explore whether a different machine learning approach****, based on a distinct underlying mechanism, could further improve performance and capture additional patterns in the data.

To achieve this, we recognized the need for a ****deeper literature review on alternative architectures and modeling strategies****, which would guide us in selecting and implementing the next model in our pipeline. Therefore, we implemented one more engineering cycle for the model development.

### Iteration 3: Employing A Different Advanced ML Model

---

![](https://static.igem.wiki/teams/5891/engineering/design.avif)
![](https://static.igem.wiki/teams/5891/engineering/build.avif)
![](https://static.igem.wiki/teams/5891/engineering/test.avif)
![](https://static.igem.wiki/teams/5891/engineering/learn.avif)

# Design

---

After implementing and evaluating LightGBM, we expanded our modeling strategy by exploring a fundamentally different machine learning approach. Our goal was to determine whether an ****alternative learning mechanism**** could capture additional patterns and potentially improve predictive performance.

Given our tabular dataset with complex non-linear dependencies, we focused our literature review on models specifically designed for tabular data prediction. We selected the ****TabPFN (Tabular Prior-Data Fitted Network) regressor****, a transformer-based model pre-trained on millions of synthetic tasks, that enables accurate predictions with minimal hyperparameter tuning.

TabPFN leverages transformer architecture and extensive pre-training to capture ****complex, non-linear feature interactions in tabular data**** without requiring dataset-specific training. This fundamentally different mechanism from gradient boosting allowed us to explore whether alternative model architectures could enhance siRNA efficacy prediction.

Regarding our training dataset, we did not make any changes to it for this iteration of the engineering cycle.

# Build

---

We moved on to write code for the TabPFN Regressor Model. ****Full documentation**** for TabPFN model development is available in our [GitLab Repository](https://gitlab.igem.org/2025/software-tools/thessaloniki/-/tree/main/siRBench-TabPFN?ref_type=heads).

# Test

---

For the model performance evaluation, we used the ****Mean Squared Error (MSE)**** metric, and the ****Pearson Correlation Coefficient (R)**** metric. The table below shows the metrics values.

|  |  |
| --- | --- |
|  | TabPFN Regressor Model |
| Mean Squared Error (MSE) | 0,0665 |
| Pearson Correlation Coefficient (R) | 0,5576 |

# Learn

---

From the evaluation of the TabPFN regressor, we obtained a ****Mean Squared Error (MSE) of 0,0665 and a Pearson correlation coefficient (R) of 0,5576**** on the test set. Although this MSE value is slightly higher than the one achieved by LightGBM (0,0529), it still ****reflects a reasonable prediction error****. On the other hand, the Pearson correlation coefficient is slightly higher compared to LightGBM (0,5454), showing that TabPFN was able to ****capture the overall trend between predicted and true efficacies slightly better****.

This comparison highlights the trade-off between the two models:

* ****LightGBM achieved lower prediction error****, meaning its predictions were closer to the experimental values on average.
* ****TabPFN showed a marginally stronger correlation with the true values****, suggesting it better preserved the global ranking and direction of siRNA efficacy trends.

Considering these complementary strengths, ****we propose both models as part of our modeling strategy****, since each offers distinct advantages for understanding and predicting siRNA silencing efficacy.

Finally, we recognize that ****further engineering cycles**** could involve the construction of ****deep learning architectures**** tailored to RNA data. These may include Convolutional Neural Networks (CNNs) to capture local sequence motifs, LSTMs and attention layers to learn sequential dependencies, and the ****integration of pre-trained RNA language models**** such as RNA-FM. Such approaches could provide even more powerful modeling capabilities in future iterations of our work.

![](https://static.igem.wiki/teams/5891/accessibilitybutton/accessibility.webp)

Accesibility Bar

![](https://static.igem.wiki/teams/5891/accessibilitybutton/font-size.webp)Increase Font Size

![](https://static.igem.wiki/teams/5891/accessibilitybutton/hide-images.webp)Hide Images

![](https://static.igem.wiki/teams/5891/accessibilitybutton/paragraph-gaps.webp)Paragraph Distance

![](https://static.igem.wiki/teams/5891/accessibilitybutton/line-distance.webp)Line Distance

![](https://static.igem.wiki/teams/5891/accessibilitybutton/dyslexia-friendly.webp)Dyslexia Friendly

![](https://static.igem.wiki/teams/5891/accessibilitybutton/paragraph-alignment.webp)Text Allignment

![](https://static.igem.wiki/teams/5891/accessibilitybutton/links.webp)Links

![](https://static.igem.wiki/teams/5891/accessibilitybutton/pointer.webp)Pointer

#####

Close

# E X P E R I M E N T S

#

## *"All life is an experiment. The more experiments you make the better."*

## *- Ralph Waldo Emerson*

### Lab Protocols

---

If the Lab Protocols below do not render, you can view them [here](https://static.igem.wiki/teams/5891/protocols/lab-protocols-igem-thessaloniki-2025.pdf).

![](https://static.igem.wiki/teams/5891/accessibilitybutton/accessibility.webp)

Accesibility Bar

![](https://static.igem.wiki/teams/5891/accessibilitybutton/font-size.webp)Increase Font Size

![](https://static.igem.wiki/teams/5891/accessibilitybutton/hide-images.webp)Hide Images

![](https://static.igem.wiki/teams/5891/accessibilitybutton/paragraph-gaps.webp)Paragraph Distance

![](https://static.igem.wiki/teams/5891/accessibilitybutton/line-distance.webp)Line Distance

![](https://static.igem.wiki/teams/5891/accessibilitybutton/dyslexia-friendly.webp)Dyslexia Friendly

![](https://static.igem.wiki/teams/5891/accessibilitybutton/paragraph-alignment.webp)Text Allignment

![](https://static.igem.wiki/teams/5891/accessibilitybutton/links.webp)Links

![](https://static.igem.wiki/teams/5891/accessibilitybutton/pointer.webp)Pointer

#####

Close

# R E S U L T S

#

## *"Success is a journey, not a destination."*

## *- Arthur Ashe*

### Results

* [MEC-1 Culture](#maincat-1)
* [BCL-2 mRNA Levels At Non-Treated MEC-1 Cells](#maincat-2)
* [Cellular effects of GSK126 in the MEC-1 Cell Line](#maincat-3)
* [Transfection of BCL-2 siRNA in MEC-1 Cell Line: Part 1](#maincat-4)
* [Titration of Different Volumes of INTERFERin in MEC-1 Cells: Assessment of toxicity](#maincat-5)
* [Transfection of BCL-2 siRNA in MEC-1 Cell Line: Part 2](#maincat-6)

### MEC-1 Culture

---

#### Introduction

---

We cultured MEC-1 cells without any treatment and monitored them for 21 days to assess cell growth, viability, and morphology. This allowed a better understanding of their cell cycle dynamics and overall behavior in culture, as well as documented morphological characteristics that will be important for downstream experimental analysis.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-2.webp)

**Figure 1: Viability of MEC-1 over time.** Cell viability of MEC-1 cells cultured under standard conditions
without treatment, over a period of 21 days. The number of viable and dead cells was measured every two days.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-1.webp)

**Figure 2: Time course of untreated MEC-1 proliferation.** Cell growth of MEC-1 cells cultured under standard conditions
without treatment, over a period of 21 days. Cell density was monitored to assess proliferation every two days.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-3.webp)

**Figure 3: Relative cell growth for 21 days for MEC-1 cells.** Cell growth was assessed by monitoring cell
density every two days for 21 days and normalizing to the value at Day 1 - that is right after seeding).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-4.webp)

**Figure 4: Relative cell viability for 21 days for MEC-1 cells.** The viability of untreated cells was assessed at 48-hour intervals
and normalized to the initial measurement (Day 1 - that is right after seeding).

It was successfully maintained in both T-75 and T-25 flasks throughout the 21-day culture period. T75 flasks exhibited higher cell numbers, reaching up to approximately 2.8 × 10⁶ cells/ml, although with greater fluctuations compared to T-25, which showed more stable growth pattern. Cell viability remained consistently high, above 90% at all time points, indicating that cells preserved their health and proliferative capacity under both culture conditions. MEC-1 cells have a reported doubling time of 48 hours and as a result the observed fluctuations in cell density likely reflect differences in seeding density, flask volume, and handling rather than inherent growth limitations. Overall, both systems are suitable for long-term culture, with T-75 favoring higher expansion potential and T-25 offering greater stability.

Microscopic observation of non-treated MEC-1 cell morphology

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-5a.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-5b.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-5c.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-5d.webp)

**Figure 5: Representative micrographs of MEC-1 cell culture progression.** Representative microscope images from T-25 and T-75 flasks are shown across consecutive days (Day 0–Day 19). The images illustrate the cell density and morphology before and after passaging events, demonstrating the cyclical pattern of confluence post splitting and subsequent re-growth.

Morphologically, cells were not affected throughout the culture period. No notable differences were observed between T-25 and T-75 flasks, either before or after passaging, and their appearance remained consistent with reference images from established databases for this cell line ****(DSMZ Cell Culture)**** [1](#r1). This confirms that the culture conditions did not alter the characteristic morphology of a MEC-1 cell.

#### Discussion

---

The comparable growth and viability profiles across T-25 and T-75 suggest that the culture conditions were consistent and did not induce stress or differentiation-related effects. The observed minor variations in cell density likely reflect differences in manual handling or local distribution of cells within the flasks rather than biological variability. This consistency validates the robustness of our culturing process and allows us to proceed confidently with subsequent experimental steps (e.g., transfection) using either setup, as both demonstrate equivalent baseline characteristics.

### BCL-2 mRNA Levels At Non-Treated MEC-1 Cells

---

#### Introduction

---

As an initial assessment, we aimed to verify whether BCL-2 is expressed in MEC-1 cells at a level to serve as a therapeutic target and assess in silico selected primers sets for successful and specific amplification of canonical (isomorph A) of BCL-2 gene.

#### 1st Primer Set

---

**Table 1: Cq values and amplification efficiencies of BCL-2 mRNA in different
non-treated cell lines using the first primer set.**

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/table-1.webp)

*\* BR1/BR2: Two biological replicates ≈ BR1P1/ BR2P1 of non-treated Mec-1 cells. Biological replicates performed with different RNA extraction protocols, serving as positive control.*

*JMP1: Human lymphoblast-like cell line (lymphoma) [2](#r2)*

*SUP-M2: Human anaplastic large cell lymphoma cell line [3](#r3)*

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-6.webp)

**Figure 6: Amplification plots of untreated cell lines using primer set 1.** Q-PCR amplification graph was generated using the first BCL-2 primer set,
for the indicated non-treated cell lines. Cq values reflect the relative amount of the target sequence in the different cell lines.

This analysis represents relative quantification, indicating that the target gene is present at high levels, consistent with the low Cq values observed. While the measurement remains relative, the signal is clearly detectable. MEC-1 cells exhibit moderate to high expression of the BCL-2 gene, with a Cq value of ~21–22, confirming that MEC-1 expresses it at significant levels compared to the positive controls (BR1P1 and BR2P1). The high amplification efficiency, together with the absence of signal in the non-template control (NTC), further confirms the reliability of these results (Figure 6).

The qPCR run was technically robust, with high efficiency, excellent reproducibility, and no evidence of contamination. BCL-2 mRNA expression levels can be ranked as: ****BR1P1≈, BR2P1 > MEC1> JMP1 > SUP-M2****.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-7.webp)

**Figure 7: Melting curve analysis for BCL-2.** Melting curve analysis of BCL-2 amplification products using the first primer set, yields a single peak,
confirming a single, specific amplicon.

For MEC-1 cells, the melting curve analysis showed a ****Tm of 85.88°C****, indicating a single, specific amplification product without evidence of primer-dimer formation or nonspecific products (Figure 7).

#### 2nd Primer Set

---

**Table 2: Cq values and amplification efficiencies of BCL-2 mRNA in
different non-treated cell lines using the second primer set.**

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/table-2.webp)

*\* BR1/BR2: Two biological replicates ≈ BR1P1/ BR2P1 of non-treated Mec-1 cells. Biological replicates performed with different RNA extraction protocols, serving as positive control.*

*JMP1: Human lymphoblast-like cell line (lymphoma) [2](#r2)*

*SUP-M2: Human anaplastic large cell lymphoma cell line [3](#r3)*

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-8.webp)

**Figure 8: Amplification plots of untreated cell lines using primer set 2.** The RT-qPCR amplification graph was generated using the second BCL-2 primer set for the indicated different non-treated cell lines. Cq values reflect the relative amount of the target sequence in the different cell lines.

The MEC-1 sample shows moderate to high expression of the BCL-2 gene, using the second pair of primers. The Cq value (22) confirms that MEC-1 cells express it at significant levels, compared to the positive controls (BR1P1 and BR2P1). The high amplification efficiency, together with the absence of signal in the non-template control (NTC), further confirms the reliability of these results (Figure 8).

The qPCR run was technically robust, with high efficiency, excellent reproducibility, and no evidence of contamination. Expression levels can be ranked as: ****BR1P1 ≈, BR2P1 > MEC1> JMP1 > SUP-M2****.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-9.webp)

**Figure 9: Melting curve analysis for BCL-2.** Melting curve analysis of BCL-2 amplification products using the
second primer set, yields a single peak, confirming a single, specific amplicon.

For MEC-1 cells, the melting curve analysis showed a ****Tm of 85.88°C****, indicating a single, specific amplification product without evidence of primer-dimer formation or nonspecific products (Figure 9).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-10.webp)

**Figure 10: Assessment of primers set performance.** Comparative qPCR amplification graph of the performance
of the two primer sets for a panel of different non-treated cell lines.

Both primer pairs performed consistently across all samples, producing nearly identical Cq values within each condition. This indicates high primer specificity, efficiency, and reproducibility, with no evidence of primer-dimer formation or non-specific amplification (as confirmed by NTC) (Figure 10).

#### Discussion

---

The detection of moderate to high expression in MEC-1 provides a critical foundation for downstream validation. This successfully establishes the first step of our proof-of-concept, enabling subsequent siRNA-based functional experiments. Consequently, MEC-1 cells are confirmed as a suitable model for downstream experiments and reliably validate our literature-based findings.

### Cellular Effects of GSK126 in the MEC-1 Cell Line

---

#### Introduction

---

In our work, we employed GSK126 before siRNA experiments in MEC-1 cells, not only to examine how our model responds to a well-characterized epigenetic inhibitor, but also to establish proof-of-principle control for dose-dependent cytostatic or cytotoxic effect. Given that GSK126 is a selective inhibitor of EZH2 (Enhancer of Zeste Homolog 2), an epigenetic modifier associated with transcriptional repression [4](#r4), the cellular effects will be assessed.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-11.webp)

**Figure 11: Dose-dependent effect of GSK126 on cell growth.** MEC-1 cells were treated with increasing concentrations
of the EZH2 inhibitor GSK126 for 24 hours following transfection, and cell growth was assessed.

A clear decreasing trend in cell number was observed with increasing concentrations of GSK126, with the most pronounced reduction detected at 40 μM. The 10 μM and 20 μM concentrations resulted in comparable low cell numbers, both lower than those observed at 5 μM. All tested concentrations exhibited reduced cell numbers compared to the control (Figure 11).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-12.webp)

**Figure 12: Dose-dependent effect of increasing concentration of GSK126 on cell viability.** MEC-1 cells were treated
with increasing concentrations of the EZH2 inhibitor GSK126 for 24 hours following transfection, and cell viability was assessed.

Cell viability remained high at the lower concentrations of the compound, but showed a decreasing trend from 20 μM onwards, dropping below 80%. At 40 μM, viability was drastically reduced, reaching as low as ~5% (Figure 12).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-13.webp)

**Figure 13: Effect of increasing concentration of GSK126 on cell growth 48h post-transfection**

A clear decreasing trend in cell number remained at 48h with increasing concentrations of GSK126, with the most pronounced reduction detected at 40 μM. The 5 μΜ, 10 μM and 20 μM concentrations resulted in comparable low cell numbers. All tested concentrations exhibited reduced cell numbers compared to the control (Figure 13).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-14.webp)

**Figure 14: Effect of increasing concentration of GSK126 on cell viability 48 h post-transfection**

Cell viability remained high at the lower concentrations of the compound, but showed a decreasing trend from 20 μM onwards, dropping below 60%. At 40 μM, viability was drastically reduced, reaching as low as ~5% (Figure 14).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-15.webp)

**Figure 15: Relative cell viability of MEC-1 cells following treatment with GSK126 for 24 h and 48 h.** The relative viability of cells
was measured after 24 and 48 hours of continuous exposure to a concentration gradient of GSK126.

Cell viability revealed a clear dose-dependent effect of GSK126. At both 24 h and 48 h, treatment with 5 μM and 10 μM maintained viability near control levels. A noticeable decline was observed at 20 μM, where viability dropped below 80%. At the highest tested concentration of 40 μM, viability was drastically reduced to nearly 5%. The reduction was more severe at 48 h, highlighting a combined effect of concentration and exposure time (Figure 15).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-16.webp)

**Figure 16: Relative cell growth of MEC-1 cells following treatment with GSK126 for 24 h and 48 h.** The relative growth
of cells was measured after 24 and 48 hours of continuous exposure to a concentration gradient of GSK126.

Analysis of relative cell growth demonstrated a strong dose-dependent inhibitory effect of GSK126. At 24 h, growth decreased progressively with concentration, reaching ~40% of control at 10 μM and 20 μM, and was nearly abolished at 40 μM. At 48 h, the inhibitory effect was more evident, with a marked decline already at 5 μM, a transient plateau at 10 μM, followed by further reduction at 20 μM, and complete suppression at 40 μM. These findings highlight both the dose- and time-dependent growth inhibition effect of GSK126 (Figure 16).

#### Discussion

---

Overall, treatment with GSK126 across a range of increasing concentrations revealed a distinct dose-dependent inhibitory effect on MEC-1 cells. Progressive escalation in concentration resulted in a consistent reduction of cell proliferation, indicating that GSK126 exerts its biological activity in a concentration-dependent manner. At 5 μM and 10 μM, GSK126 primarily exerted a cytostatic effect, consistent with cell cycle arrest rather than cell death, whereas at 20 μM and 40 μM, a clear cytotoxic effect was observed. The response was dose-dependent, and at 20 μM, there was also an indication of time-dependent enhancement of cytotoxicity. This aligns with literature reports that lower concentrations of GSK126 (5 μM and 10 μM) often require prolonged exposure (≥72 h) to manifest significant effects, with the nature of the response—cytostatic or cytotoxic—being cell-type dependent [4](#r4) [5](#r5). The requirement for higher concentrations or longer duration to observe effects in our system compared to some literature reports underscores the cell-type-specific sensitivity to EZH2 inhibition.

### Transfection of BCL-2 siRNA in MEC-1 Cell Line: Part 1

---

#### Introduction

---

Transfection of BCL-2–targeting siRNAs into MEC-1 cells enables the specific silencing of BCL-2 gene expression. This approach allows us to evaluate the consequent effects on cell growth and viability.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-17.webp)

**Figure 17: -Dose- and time-dependent effects of BCL-2 siRNA on cell growth under different control conditions (scrambled siRNA, siRNA alone, INTERFERin alone).** MEC-1 cells were transfected with increasing concentrations of BCL-2 siRNA or control treatments (scrambled siRNA, siRNA delivery reagent alone, and untreated cells). Cell growth was monitored at the indicated timepoints post-transfection.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-18.webp)

**Figure 18: Dose- and time-dependent effects of BCL-2 siRNA on cell viability under different control conditions (scrambled siRNA, siRNA alone, INTERFERin alone).** MEC-1 cells were transfected with increasing concentrations of BCL-2 siRNA or control treatments (scrambled siRNA, siRNA delivery reagent alone, and untreated cells). Cell viability was monitored at the indicated timepoints post-transfection.

At 24 h post-transfection, both cell proliferation and viability were reduced across all conditions compared to the control, with the greatest decrease observed in the condition containing only INTERFERin (5 µL), as well as in the groups treated with 50 nM siRNA + INTERFERin (5µL) and 20 nM siRNA + INTERFERin (5µL). Cell viability in these groups dropped to approximately 65–70%, showing the same pattern as the reduction in cell numbers.

At 48 h, proliferation remained lower than the control across all conditions but showed an overall increase compared to 24 h, except for the 50 nM scrambled siRNA group, which did not recover to the same extent. Cell viability at 48 h was high across all conditions, showing an increase relative to 24 h and a stable trend thereafter.

At 72 h, proliferation continued to be lower than the control but showed a substantial increase compared to 24 h and 48 h, while viability remained high and stable across all conditions. Finally, at 96 h, proliferation in all experimental groups surpassed that of the control, with cell numbers markedly higher than at earlier time points, particularly in the higher concentration and INTERFERin-only conditions. Viability at 96 h remained relatively high in all conditions, although slightly decreased compared to 72 h, with the lowest values observed in the 50 nM scrambled siRNA group (Figure 17-18).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-19.webp)

**Figure 19: Relative cell growth of increasing concentrations of BCL-2 siRNA under different control conditions (scrambled siRNA, siRNA alone, INTERFERin alone).** MEC-1 cells were transfected with increasing concentrations of BCL-2 siRNA or control treatments (scrambled siRNA, siRNA delivery reagent alone, and untreated cells). Cell growth was monitored at the indicated timepoints post-transfection.

Across the tested concentrations (5–50 nM), a consistent pattern was observed at 24 h, 48 h, and 72 h post-transfection, with cell growth decreasing progressively as siRNA concentration increased. At 48 h, a ~40% reduction in cell growth was noted in the 20 nM condition compared to the control, highlighting a dose-dependent effect. By 72 h, the same pattern remained, although the magnitude of reduction was lower than at earlier timepoints, indicating partial recovery of the cells. The condition containing only INTERFERin showed the strongest inhibitory effect at 24 h, with cell growth reduced by ~60% compared to the control. However, by 48 h and 72 h the reduction in this group was smaller, stabilizing around ~35%. At 96 h, overall growth increased across conditions, suggesting that MEC-1 cells progressively adapt and recover over time following transfection (Figure 19).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-20.webp)

**Figure 20: Relative cell viability of increasing concentrations of BCL-2 siRNA under different control conditions (scrambled siRNA, siRNA alone, INTERFERin alone).** MEC-1 cells were transfected with increasing concentrations of BCL-2 siRNA or control treatments (scrambled siRNA, siRNA delivery reagent alone, and untreated cells). Cell growth was monitored at the indicated timepoints post-transfection.

Relative cell viability remained largely stable across all conditions, showing that the treatments did not cause significant cytotoxicity. At 20 nM, a slight decrease was observed at 24 h, 48 h, and 72 h, while the strongest effect appeared at 50 nM, with only a ~20% reduction at 24 h. The INTERFERin-only condition did not display any notable toxicity at any timepoint. By 96 h, full recovery of cell viability was observed across all conditions, confirming that the treatments primarily affected proliferation rather than cell survival (Figure 20).

Microscopic observation of MEC-1 cell morphology following transfection with siRNAs

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-21-a.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-21-b.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-21-c.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-21-d.webp)

**Figure 21: Morphological assessment of BCL-2 siRNA transfection.** Representative microscope images of different conditions (control,5-50nM, scrambled, alone, INTERFERin) are shown across 5 timepoints (5h, 24h, 48h, 72h,96h). Transfection day accounts for 5h post-transfection. The images illustrate the dynamics of the cellular response regarding cell density and morphology.

In both the transfected conditions (+INTERFERin) and the INTERFERin-only condition, similar morphological changes were observed. At 5 h and 24 h post-transfection, cells appeared stressed, showing aggregation and clustering, while the overall number of viable cells under the microscope seemed noticeably reduced compared to the control. These early points indicate that INTERFERin exposure induces transient cellular stress, regardless of whether siRNA is present, which is confirmed by cell growth and viability across all conditions at 24h post-transfection. By 72 h post-transfection, cells showed signs of recovery, with morphology returning closer to normal and cell numbers visibly increasing. At 96 h, the cultures appeared fully recovered, with healthy morphology and growth restored across all conditions, suggesting that the observed effects were temporary and reversible. As well as conditions with siRNA alone even though with increased concentration (50nM) manifest morphologically the same features as control group, while conditions transfected with maximum volume of INTERFERin showed the same morphological pattern as INTERFERin alone condition (Figure 21).

#### Discussion

---

Our results highlight a transient toxicity with INTERFERin exposure, at higher concentrations, manifesting an initial reduction in cell number and viability. However, this effect appears to be reversible, as the cells progressively recover over time, regaining both growth capacity and viability. This suggests that while INTERFERin at higher concentrations exerts a temporary cytostatic effect immediately after transfection, MEC-1 cells can adapt and restore normal proliferation dynamics at later timepoints. It is important to note that in the absence of direct siRNA uptake validation, the observed phenotypes cannot be definitively attributed to specific BCL-2 silencing at these early timepoints.

### Titration of Different Volumes of INTERFERin in MEC-1 Cells: Assessment of Toxicity

---

#### Introduction

---

In our initial experiments, we observed signs of cytotoxicity, most likely arising due to the increased volume of INTERFERin required for transfection. Since no data was available on the cellular uptake of the siRNAs under our specific conditions, the next experiment included performing a titration of INTERFERin volumes, in parallel with adjusting the cell seeding density. Specifically, we compared two setups: 300,000 cells/mL, as in our previous experiment, and a lower density of 150,000 cells/mL, which is closer to the manufacturer’s recommendations. This approach allowed us to evaluate how MEC-1 cells respond to different levels of transfection stress under varying cell densities, providing more reliable insights into the balance between transfection efficiency and cell viability.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-22.webp)

**Figure 22: Effect of increasing volumes of INTERFERin upon different cell seeding set up on cell growth 24h post-transfection.** Cell growth was
assessed 24 hours post-transfection to evaluate the impact of increasing INTERFERin volumes across different initial cell seeding densities.

#### 24h:

---

Cell number decreases with increasing volumes of INTERFERin, reflecting the dose-dependent toxic effect of the reagent. Cell numbers are higher at 300.000 cell density seeding set up rather than lower cell density across all conditions. At lower volume of INTERFERin cell proliferation is less affected by INTERFERin (Figure 22).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-23.webp)

**Figure 23: Effect of increasing volumes of INTERFERin upon different cell seeding set up on cell viability 24h post-transfection.** Cell viability was assessed 24 hours post-transfection to evaluate the impact of increasing INTERFERin volumes across different initial cell seeding densities.

#### 24h:

---

Cell viability decreases with increasing volumes of INTERFERin, while it remains high at smaller volumes. Between the two seeding densities tested, relatively higher viability was observed in the 300,000 cells/mL setup compared to the lower-density condition. At 5 µL of INTERFERin viability dropped to 50–60% in both seeding setups, indicating that toxicity is directly associated with the volume of transfection reagent used rather than cell density alone (Figure 23).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-24.webp)

**Figure 24: Effect of increasing volumes of INTERFERin upon different cell seeding set up on cell growth 48h post-transfection.** Cell growth was
assessed 48 hours post-transfection to evaluate the impact of increasing INTERFERin volumes across different initial cell seeding densities.

#### 48h:

---

Cell number decreases with increasing volumes of INTERFERin, reflecting the dose-dependent toxic effect of the reagent. Cell numbers are higher at 300.000 cell density seeding set up rather than lower cell density across all conditions. At lower volume of INTERFERin cell proliferation is less affected by INTERFERin.

Nevertheless, across nearly all conditions, after 48 h the cultures showed an approximate two-fold increase in cell number compared to the initial seeding and transfection, indicating that despite the early impact of INTERFERin, MEC-1 cells retain the capacity to proliferate and partially recover over time (Figure 24).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-25.webp)

**Figure 25: Effect of increasing volumes of INTERFERin upon different cell seeding set-up on cell viability 48h post-transfection.** Cell viability was assessed 48 hours post-transfection to evaluate the impact of increasing INTERFERin volumes across different initial cell seeding densities.

#### 48h:

---

Cell viability decreases with increasing volumes of INTERFERin, while it remains high at smaller volumes. Between the two seeding densities tested, slightly higher viability was observed in the 300,000 cells/mL setup compared to the lower-density condition. At 5 µL of INTERFERin viability remained relatively high >80% in both seeding setups, indicating that MEC-1 cells retain the capacity to proliferate and partially recover over time (Figure 25).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-26.webp)

**Figure 26: Effect of increasing volumes of INTERFERin in 150.000 cells - set up on cell viability 24h versus 48h post-transfection**

At 48 h, cell viability remained like at 24 h in the Control and 1 μL INTERFERin conditions, indicating stable survival at low reagent volumes. In contrast, at higher volumes (2–5 μL), viability significantly increased at 48 h compared to 24 h, suggesting a recovery effect over time. The most pronounced improvement was seen at 5 μL, where viability reached from approximately 50% at 24 h to nearly 80% at 48 h, demonstrating that MEC-1 cells can partially overcome the initial toxic effect of INTERFERin (Figure 26).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-27.webp)

**Figure 27: Effect of increasing volumes of INTERFERin in 150.000 cells - set up on cell growth 24h versus 48h post-transfection**

At 24 h post-transfection, cell numbers across all conditions were relatively similar, ranging from approximately 120,000 to 300,000 cells. By 48 h, however, cell numbers had increased substantially in all conditions, highlighting a clear proliferation trend compared to the earlier timepoint (Figure 27).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-28.webp)

**Figure 28: Effect of increasing volumes of INTERFERin in 300.000 cells - set up on cell viability 24h versus 48h post-transfection**

At 48 h, across all conditions cell viability increased at 48 h compared to 24 h, suggesting a recovery effect over time. The most pronounced improvement was seen at 5 μL, where viability reached from approximately 65% at 24 h to nearly 90% at 48 h, demonstrating that MEC-1 cells can partially overcome the initial toxic effect of INTERFERin, (Figure 28).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-29.webp)

**Figure 29: Effect of increasing volumes of INTERFERin in 300.000 cells - set up on cell growth 24h versus 48h post-transfection**

At 24 h post-transfection, cell numbers across all conditions were relatively similar, ranging from approximately 250,000 to 600,000 cells. By 48 h, however, cell numbers had significantly increased in all conditions, highlighting a clear proliferation trend compared to the earlier timepoint, (Figure 29).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-30.webp)

**Figure 30: Relative cell viability of increasing volume of INTERFERin across seeding densities at 24h.** Relative cell viability was assessed 24 hours
post-transfection to determine the combined impact of INTERFERin volume and initial seeding density.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-31.webp)

**Figure 31: Relative cell growth of increasing volume of INTERFERin across different cell seeding densities at 24h.** Relative cell growth was
assessed 24 hours post-transfection to determine the combined impact of INTERFERin volume and initial seeding density.

#### 24h:

---

The comparison between the two seeding densities (150,000 vs 300,000 cells/mL) showed very similar patterns, suggesting that the observed effects are primarily volume-dependent rather than cell density–dependent. Cell viability gradually decreased with increasing volumes of INTERFERin, reaching a reduction of approximately 40% at 5 μL compared to the control. Similarly, cell growth displayed a clear dose-dependent decline, with growth at 5 μL reduced to less than half of the control, confirming the strong inhibitory effect of higher reagent volumes (Figure 30-31).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/fiure-32.webp)

**Figure 32: Relative cell viability of increasing volume of INTERFERin across seeding densities at 48h.** Relative cell viability was
assessed 48 hours post-transfection to determine the combined impact of INTERFERin volume and initial seeding density.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-33.webp)

**Figure 33: Relative cell growth of increasing volume of INTERFERin across different cell seeding densities at 48h.** Relative cell growth was
assessed 48 hours post-transfection to determine the combined impact of INTERFERin volume and initial seeding density.

#### 48h:

---

The cell density effect showed that higher seeding density (300,000 cells/mL) supported slightly better growth compared to 150,000 cells/mL, particularly at higher INTERFERin volumes. Viability remained relatively stable (~85–95% of control) across conditions, with only a slight dip at 3 μL followed by recovery at 5 μL. In contrast, cell growth displayed a clear dose-dependent decrease, with proliferation at 5 μL reduced to ~45–60% of the control, highlighting the strong inhibitory effect of higher INTERFERin volume (Figure 32-33).

Microscopic observation of MEC-1 cell morphology following transfection with INTERFERin (titration)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-34-a.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-34b.webp)

**Figure 34: The images illustrate the cell density and morphology. Morphological assessment of different volumes of INTERFERin across different cell seeding densities.** The panels compare two initial seeding densities (150,000 vs. 300,000 cells) and two timepoints (24- and 48-hours post-transfection) to illustrate the combined impact on cell density and morphology.

With increasing volumes of INTERFERin, cells displayed clear signs of stress and aggregation at both 24 h and 48 h post-transfection. However, at 48 h, in both cell density setups, the extent of cell stress and aggregate formation was reduced compared to 24 h, indicating a partial recovery of the culture over time (Figure 34).

#### Discussion

---

High volumes of INTERFERin primarily induce a transient inhibition of proliferation, confirming a temporary arrest of the cell cycle, while overall viability remained largely unaffected.

When comparing cultures seeded at 150,000 cells/mL versus 300,000 cells/mL, a generally similar trend was observed, though the effect of INTERFERin was slightly less pronounced at the higher density. In denser cultures (300,000 cells/mL), the dose of toxic lipid complexes per cell is effectively lower, meaning that each cell internalizes and processes less INTERFERin. In addition, a denser cell population can secrete more survival factors and create a more resilient microenvironment, helping the overall culture withstand the stress caused by INTERFERin. Cell–cell contacts may also strengthen pro-survival signaling pathways. As a result, with a lower effective dose per cell and a more “supportive” cellular environment, cultures at higher density experience slightly less stress, leading to a milder inhibition of growth compared to lower-density cultures. Furthermore, higher-density cultures provide a greater number of cells available for downstream analyses and further experimentation.

Concluding, INTERFERin exhibits cytotoxic/cytostatic effects at high doses. Its main negative impact at larger volumes (3–5 µL) is an inducible and reversible arrest of the cell cycle, rather than induction of cell death. This suggests that the observed effects are likely non-permanent and that MEC-1 cells can recover once the stress is alleviated.

### Transfection of BCL-2 siRNA in MEC-1 cell line: Part 2

---

#### Introduction

---

Transfection of BCL2-targeting siRNAs into MEC-1 cells enables the specific silencing of BCL2 gene expression through the RNA interference pathway. This experimental setup was designed based on the results of previous experiments and was repeated with optimized parameters, to assess the efficiency of siRNA uptake by MEC-1 cells, evaluate the downregulation of BCL2 mRNA levels, and determine the effects of BCL2 silencing on cell growth and viability.

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-35.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-36.webp)

**Figure 35 - 36: Fluorescence analysis of siRNA uptake in MEC-1 cells.** (Top) Fluorescence intensity (RFU) measured at 6 h, 24 h, and 48 h across different siRNA concentrations (5–50 nM) and conditions (siRNA alone, scrambled siRNA, INTERFERin alone, blank). (Bottom) Fold change in fluorescence relative to the control confirms this pattern.

At 20 nM, a distinct early fluorescence peak was observed at 6 h, indicating rapid uptake through active endocytosis. This signal gradually decreased and stabilized by 24–48 h, suggesting that the siRNA was being processed intracellularly. The decrease in fluorescence following the initial peak is a positive indication that the siRNA is being directed to its site of action, reflecting the normal pattern of siRNA action and being processed and used for target gene silencing, target gene.

At 50 nM, fluorescence levels increased over time (6 h, 24 h, 48 h), but the pattern did not show the same sharp uptake and subsequent stabilization seen at 20 nM. Instead, the sustained high signal suggests that the system becomes overloaded, with siRNA likely trapped in vesicles rather than being efficiently processed for gene silencing.

Other conditions, including scrambled siRNA and INTERFERin alone, show no significant fluorescence changes and, as a result, no cellular uptake (Figure 35-36).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-37.webp)

**Figure 37: Effect of increasing concentrations of BCL-2 siRNA and different control conditions (scrambled siRNA, siRNA alone, INTERFERin alone) on cell growth at different timepoints post-transfection.** The growth of MEC-1 cells was assessed at multiple timepoints post-transfection with increasing concentrations of BCL-2 siRNA.

Cell growth across all conditions showed a clear increase from 24 h to 48 h, indicating that cells remained viable and able to proliferate. The increase observed at 20nM is higher than at 50nM comparable to control levels. Analysis of the fluorescence uptake and cell growth data provides a consistent picture of how MEC-1 cells respond to different siRNA concentrations. Especially at 20 nM, siRNA is efficiently internalized and processed, leading to effective activity while cells remain viable and proliferative within timepoints (24h-48h). At 50 nM, the system becomes overloaded (Figure 37), resulting in cellular stress and a slight growth inhibition.

Lower concentrations (5–10 nM) overall showed modest fluorescence uptake (Figure 35-36) and relative moderate growth, while controls (scrambled siRNA, INTERFERin alone) demonstrated that part of the observed reduction in proliferation originates from the delivery reagent itself rather than siRNA activity (Figure 37).

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-38.webp)

**Figure 38: Effect of increasing concentrations of BCL-2 siRNA and different control conditions (scrambled siRNA, siRNA alone, INTERFERin alone) on cell viability at different timepoints post-transfection.** The viability of MEC-1 cells was assessed at multiple timepoints post-transfection with increasing concentrations of BCL-2 siRNA.

Cell viability remained consistently high (>85–90%) across conditions, indicating that no significant cytotoxic effects were observed. This suggests that the treatments primarily influenced cell proliferation rather than inducing cell death (Figure 38).

#### BCL-2 SET1 Primers:

---

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-39.webp)

#### BCL-2 SET2 Primers:

---

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-40.webp)

**Figure 39 - 40: Amplification graphs using two primer sets of BCL-2 mRNA after siRNA transfection in MEC-1. qPCR was performed 6 h and 24 h post-transfection with siRNA targeting BCL-2 at different concentrations (5 nM, 10 nM, 20 nM, 50 nM), as well as control conditions (untreated control, 20 nM siRNA alone, 20 nM scrambled siRNA, and INTERFERin-only). (\*) indicates significant differences (p < 0.05) compared to the untreated control.** At 6 SET1 ,20 nM scrambled siRNA, and INTERFERin-only, Cq value corresponds to 1 replicate and not the average of 3 replicates as the other conditions.

For both primer sets, at ****6h post-transfection****, minimal differences were observed among 5nM-10nM conditions compared to control, indicating that early silencing effects had not yet fully manifested at low and medium concentrations, along with fluorescence results which showed at those conditions not cellular uptake. Despite manifesting a distinct early fluorescence peak at 20nM, Cq value is comparable to control levels.

At 50 nM manifests Cq lower to control, but not statistically significant, along with low signal in cellular uptake at that early timepoint. At 20nM, alone Cq is lower to statistically insignificant control, as well. At INTERFERin and 20 nM scrambled conditions manifest Cq very high (>30) compared to control at both primer sets. For the first primer set, certain high Cq values resulted from single replicate amplification (only one Cq value was recorded, while the other failed to give a value) rather than biological differences. For the second primer set, high Cq values across all three biological replicates were observed at INTERFERin and 20 nM scrambled statistically significant conditions (p < 0.05) and as a result amplification is unexpectedly not observed.

For both primer sets, at ****24h post-transfection****, minimal differences were observed among 5nM-10nM conditions compared to control, indicating that early silencing effects had not yet fully manifested at low and medium concentrations, along with fluorescence results, which showed that at those conditions, no cellular uptake occurred. At 20nM, the Cq value is comparable to control levels, while the signal is gradually decreased and stabilized by 24h.

The increase at 50 nM might suggest reduced BCL-2 transcript abundance due to siRNA-mediated degradation of mRNA, with cellular uptake to increase at 24h post-transfection. At 20nM alone, Cq has unexpectedly increased as well. For the second primer set, high Cq values across all three biological replicates were observed at 20 nM alone and 20 nM scrambled conditions, which are statistically significant (p < 0.05) and, as a result, amplification is unexpectedly not observed (Figures 39-40).

However, unexpectedly high Cq values were also observed in some groups (INTERFERin-only, 20 nM scrambled siRNA, and siRNA alone), possibly reflecting technical variability or RNA quality issues. To further verify these results, agarose gel electrophoresis was performed both for total RNA samples and for some qPCR products at 6h, confirming the integrity of RNA and the specificity of amplification.

#### BCL-2 SET1 Primers:

---

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-41.webp)

**Figure 41: Agarose gel analysis of RNA integrity and qPCR products.** 1,2% agarose gel electrophoresis of total RNA and qPCR products following siRNA transfection in MEC-1 cells was assessed. The control group represents untreated MEC-1 cells. 20nM scr. and INTERFERin A & B represent the 2 duplicates for each condition in qPCR.

At the 6 h time point, RNA quantification revealed a lower absorbance compared to the 24 h samples, indicating a smaller total RNA yield. This difference is validated in the agarose gel, where RNA bands corresponding to the 6 h samples are markedly less intense than those at 24 h. The reduced band brightness reflects the lower RNA concentration and overall limited template availability for qPCR. Lower RNA input might have contributed to the increased Cq values observed in qPCR at 6 h, particularly in INTERFERin-only and 20nM scrambled siRNA conditions. The observed elevation in Cq at early time points and INTERFERin-only and 20nM scrambled siRNA conditions primarily stems from technical limitations related to low template concentration and low-quality RNA, rather than from biological factors.

In contrast, at 24 h, the increased RNA yield, which aligns with gel electrophoresis observations, enabled robust amplification and reduced variability between replicates and improved qPCR performance. RNA extraction quality and yield improve significantly between 6 h and 24 h post-transfection. Low RNA yield at 6 h contributes to technical elevation of Cq values in qPCR while at 24 h samples provide more reliable data for evaluating siRNA uptake and BCL-2 silencing (Figure 39-40).

Gel electrophoresis in qPCR products at 6h confirms the absence of major degradation or nonspecific artifacts, while validating the consistency with the high Cq values and the low amount and quality of initial RNA and except for INTERFERin, which produced a faint, barely detectable band (Figure 41).

Consequently, it appears that the low initial number of cells used at the beginning of the experiment led to a reduced total RNA yield, as well as lower levels of high-quality RNA. This limitation was subsequently reflected in the qPCR results of certain samples, particularly at the 6 h time point, where higher Cq values were observed due to insufficient RNA input. In contrast, at 24 h, a clear recovery in cell growth and an increase in total cell number were evident, resulting in higher RNA concentrations and improved amplification performance.

#### BCL-2 SET1 Primers:

---

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-42.webp)

#### BCL-2 SET2 Primers:

---

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-43.webp)

**Figure 42-43: BCL-2 mRNA expression levels across all conditions at 6h and 24h post-transfection for both primer sets. BCL2 mRNA levels were determined by normalization against the reference gene YWHAZ. (\*) indicates significant differences (p < 0.05) compared to the untreated control.**

For both primer sets, the 5 nM and 10 nM siRNA conditions, as well as the 20 nM siRNA-alone condition, exhibited a ~5-fold and above overexpression of BCL-2 compared to the control at 6 h post-transfection. By 24 h, BCL-2 levels under these conditions were reduced compared to control.

For the 20 nM siRNA condition, both timepoints showed a similar tendency, with BCL-2 expression slightly reduced relative to control (20% reduction).

At 50 nM, both timepoints showed a consistent reduction to about half of the control levels (40-50% reduction), with the strongest downregulation observed at 24 h, along with increased cellular uptake.

At 6 h post-transfection, both the INTERFERin-only and 20nM scrambled siRNA conditions showed a slight apparent increase in BCL2 expression compared to the control, which is attributed to the low RNA input and minor technical variability observed at this early timepoint.

At 24 h, BCL2 mRNA levels in both conditions returned close to baseline (control) values, with no statistically significant differences detected (Figure 42-43).

*During the experiments, an unexpected amplification in the negative control of T the reference gene (YWHAZ), was observed, probably due to the formation of primer dimers. Consequently, to investigate this anomaly, new qPCR reactions will be performed, including both biological replicates and additional reference gene controls to further investigate this observation and clarify its underlying cause.*

Microscopic observation of MEC-1 cell morphology following transfection with siRNAs

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-44a.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-44b.webp)

![One-Hot Encoing](https://static.igem.wiki/teams/5891/results/figure-44c.webp)

**Figure 44: Representative microscope images of MEC-1 cells under BCL-2 siRNA transfection.** Images show cell density and morphology at indicated conditions (control,5nM-50nM, scrambled, alone, INTERFERin) and timepoints (5h, 24h, 48h, 72h, 96h). Transfection day accounts from 5h post-transfection. The images illustrate time-dependent changes in cell density and morphology.

At 6 h post-transfection, morphological examination revealed signs of cellular stress, characterized by the presence of cell aggregates and clustering. From 24 h onwards, and more prominently at 48 h, a recovery was observed, with MEC-1 cells regaining a more typical and physiological morphology.

The cellular response observed at 10 nM - 50 nM siRNA concentrations was morphologically similar across all time points. At 6 h, pronounced cellular stress was evident, with cell aggregates indicating a strong response. This effect persisted at 24 h, while by 48 h a recovery was observed, with cells progressively regaining their typical morphology (Figure 44).

#### Discussion

---

Our initial assessment indicates that BCL-2 siRNA is efficiently internalized at 20 nM and 50 nM without inducing cytotoxicity. The 20 nM condition showed rapid siRNA uptake followed by a decrease in fluorescence, indicating efficient initial possible gene silencing without detectable cytotoxicity. In contrast, the 50 nM condition exhibited stronger uptake without signs of cytotoxicity, as well. To confirm these observations, the investigation of target mRNA downregulation was made by qPCR, where while a corresponding trend of mRNA downregulation was observed (~30% and ~50%, respectively), the data were variable and not statistically significant. Specifically, at 20 nM, a partial reduction in BCL2 mRNA levels was observed at both 6 h and 24 h, amounting to approximately 30% and at 50 nM, expression levels were reduced by about half (~50%), corresponding to changes that did not reach statistical significance. Given the variability and the persistence of certain technical limitations, these findings should be interpreted with caution. Consequently, from these experiments, it became evident that scaling up the experimental setup is necessary to increase the number of seeded cells and thereby obtain higher RNA yields. This will allow for more reliable and consistent downstream analyses, such as qPCR and related molecular assays, by ensuring both the quality and the quantity of extracted RNA, for reliable analysis.

Furthermore, the observed instability of the YWHAZ reference gene in some negative control groups mandates its re-evaluation under our specific transfection conditions. Additional validation will include both biological replicates and protein-level assessments to confirm whether YWHAZ remains a stable normalizer under our experimental conditions.

**References**

1. <https://www.dsmz.de/collection/catalogue/details/culture/ACC-497>
2. <https://www.atcc.org/products/crl-3378>
3. <https://www.dsmz.de/collection/catalogue/details/culture/ACC-509>
4. Park S, Jo SH, Kim JH, Kim SY, Ha JD, Hwang JY, Lee MY, Kang JS, Han TS, Park SG, Kim S, Park BC, Kim JH. Combination Treatment with GSK126 and Pomalidomide Induces B-Cell Differentiation in EZH2 Gain-of-Function Mutant Diffuse Large B-Cell Lymphoma. Cancers (Basel), 2020 doi: 10.3390/cancers12092541
5. Malik Bisserier, Narendra Wajapeyee; Mechanisms of resistance to EZH2 inhibitors in diffuse large B-cell lymphomas. Blood 2018; 131 (19): 2125–2137. doi: https://doi.org/10.1182/blood-2017-08-804344

![](https://static.igem.wiki/teams/5891/accessibilitybutton/accessibility.webp)

Accesibility Bar

![](https://static.igem.wiki/teams/5891/accessibilitybutton/font-size.webp)Increase Font Size

![](https://static.igem.wiki/teams/5891/accessibilitybutton/hide-images.webp)Hide Images

![](https://static.igem.wiki/teams/5891/accessibilitybutton/paragraph-gaps.webp)Paragraph Distance

![](https://static.igem.wiki/teams/5891/accessibilitybutton/line-distance.webp)Line Distance

![](https://static.igem.wiki/teams/5891/accessibilitybutton/dyslexia-friendly.webp)Dyslexia Friendly

![](https://static.igem.wiki/teams/5891/accessibilitybutton/paragraph-alignment.webp)Text Allignment

![](https://static.igem.wiki/teams/5891/accessibilitybutton/links.webp)Links

![](https://static.igem.wiki/teams/5891/accessibilitybutton/pointer.webp)Pointer