iGEM 2025 — Team smbu

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This serves as a chronological record of your team's progress throughout
the season. It documents your daily activities, experiments, discussions, and
decisions.
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# Notebook

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{% block title %}Engineering{% endblock %}
{% block lead %}Demonstrate engineering success in a technical aspect of your project by going through at least one iteration of the engineering design cycle.{% endblock %}
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# Engineering

### Design

The goal of our project was to create a system capable of efficiently binding DNA molecules via Cas proteins. As shown in a number of studies, the use of a
**multi-agent CRISPR approach**
made it possible to increase the efficiency of genetic therapy, including approaches based on
**epigenetic editing**. To reduce the size of the construct and facilitate its delivery, we decided to target two regions within the HBV cccDNA molecule simultaneously. Increasing cooperativity between Cas complexes could significantly enhance the
**specificity and efficiency of editing**.

Double complexes of Cas nucleases and dCas proteins were often used as parts of dimeric assemblies. Dimerization was typically achieved either through
**antibody-derived protein dimers**
or through **guide RNA modifications**.
However, the overall size of genetic constructs was critical for cell-therapy applications. For this reason, the use of large dimerization protein domains or fused Cas proteins was not suitable for the purposes of our project.

Therefore, the most optimal solution to our problem was to employ **modified guide RNAs**
that facilitated dimerization of dCas proteins via RNA–RNA interactions. To achieve this, we planned to
**use known hairpin RNA motifs**
capable of forming **kissing-loop structures**,
which could stabilize paired RNA molecules and promote complex formation between two Cas proteins.

![figure 1](https://static.igem.wiki/teams/5861/23/1.webp)

**Figure 1.**
*General design logic of the dual-CRISPR dCas system. Modified sgRNAs form kissing-loop interactions that bring together two dCas proteins bound to nearby cccDNA sites, promoting cooperative epigenetic repression.*

At the first stage of our work, we selected the **RNA dimerization domain** described in Mutsuro-Aoki H., *Acquisition of Dual Ribozyme-Functions in Nonfunctional Short Hairpin RNAs through Kissing-Loop Interactions, Life,*  2022. Using this motif, we modified a standard sgRNA by adding the domain to its 3′ end via a short linker.

We tested the efficiency of this RNA modification and compared it with the standard sgRNA using several analytical methods —
**electrophoretic mobility shift assay (EMSA), biolayer interferometry (BLI)**, and
**cell-based assays** evaluating the biological activity of the modified RNA in model systems. The data obtained indicated partial improvement in dimer formation, but the results were not sufficiently strong or reproducible to confirm that this modification reliably increased complex stability. For this reason, we proceeded to design and construct our own
**RNA dimerization domain** to achieve higher stabilization of Cas protein dimers.

### Build

The second stage of our work focused on creating an **improved RNA dimerization domain**
to enhance the stability of Cas–Cas complexes and to evaluate variants with different dimerization parameters. To achieve this, we developed a
**genetic selection system** that allowed direct comparison of dimerization domains within a controlled regulatory network.

The main idea of this stage was to enable **selection of the most stable kissing-loop structures** that could stabilize two Cas proteins through interactions between their guide RNAs. The use of
**chimeric guide RNAs** containing two scaffolds was excluded at this stage, since it would substantially increase RNA size, reduce activity, and potentially elevate off-target effects relative to genomic DNA.
To select the most effective RNA–RNA pairs, we employed a **Ptet/TetR-based genetic selection system** designed to identify stable interactions between guide RNAs in living cells.

![figure 2](https://static.igem.wiki/teams/5861/23/2.webp)

**Figure 2.**
*Schematic of the Ptet/TetR selection circuit. Three plasmids operated together: (1) a reporter plasmid (pTet–reporter), (2) a TetR-expressing sensor plasmid under a constitutive promoter with Cas-binding sites, and (3) an expression plasmid encoding dCas and the gRNA library.*

The selection system consisted of three plasmids:

1. **Reporter plasmid** — contained a fluorescent or antibiotic-resistance gene under the control of the pTet promoter.
2. **Expression plasmid** — encoded dCas proteins and a library of guide RNAs carrying various 3′ RNA motifs.
3. **Sensor plasmid** — included a promoter with multiple Cas-binding targets and expressed the
   **TetR** repressor protein.

The system functioned as follows. When two Cas complexes formed a stable pair through RNA–RNA interaction, a
**DNA loop** was generated. This structural change suppressed the activity of the constitutive promoter controlling
**TetR** expression, resulting in a decrease in TetR concentration. As TetR levels fell, repression of the pTet promoter was lifted, leading to increased expression of the reporter gene.
Such a configuration allowed rapid selection of guide-RNA sequences that provided the strongest stabilization of ribonucleoprotein (RNP) complexes.

In addition to known kissing-loop variants described in the literature, we performed **one round of randomization** at the 3′ end of the guide RNA. This approach allowed identification of novel sequences capable of effective RNA–RNA dimerization.

The resulting guide RNAs were subsequently tested **in vitro** using **minicircle-derived recombined cccDNA (mcccDNA)** — an experimental model that closely reproduced the structural and functional properties of native HBV cccDNA.

### Test

We evaluated the functionality of the constructs both **in vitro** and in **vivo**.
– **In vitro:** The selected gRNA constructs were introduced into bacterial systems containing the three-plasmid circuit. Fluorescence intensity and antibiotic-resistance profiles were used as indicators of successful RNA–RNA dimerization and Cas complex stabilization.
– **In vivo:** Validation experiments were conducted in HepG2 cell models with integrated cccDNA to assess whether RNA-mediated dCas cooperation enhances transcriptional repression without double-strand breaks.

Additionally, several **previously characterized RNA dimerization motifs** were employed as positive controls to benchmark the efficiency of our newly designed sequences.

### Learn

Analysis of the obtained data showed that standard RNA modifications resulted in only partial stabilization of Cas complexes, confirming the necessity of designing a **custom dimerization RNA motif**.

This first iteration of the **Design–Build–Test–Learn cycle** validated the feasibility of our concept and yielded quantitative metrics for the next optimization round. Based on the feedback from this iteration, we refined both the guide-RNA architecture and the sensitivity of the Ptet/TetR selection system, improving its dynamic range and reducing background activation.

Through this iterative process, we achieved our first **engineering success** — a complete cycle from conceptual design to experimental validation and analysis, demonstrating the applicability of engineering principles to RNA-mediated CRISPR complex formation.

Furthermore, within the same framework, we developed a **new inducible chloramphenicol-resistance plasmid** regulated by the **tetracycline promoter**. This construct served as a reversible selection module and could be adapted for clone selection or gene-repression studies in other synthetic-biology applications. Learn more in
[Contribution](https://2025.igem.wiki/smbu/contribution).

## References

Wang, L., & Han, H. (2024). Strategies for improving the genome-editing efficiency of class 2 CRISPR/Cas systems. Heliyon, 10(19), e38588. https://doi.org/10.1016/j.heliyon.2024.e38588

Mutsuro-Aoki, H., & Tamura, K. (2022). Acquisition of dual ribozyme functions in nonfunctional short hairpin RNAs through kissing-loop interactions. Life, 12(10), 1561. https://doi.org/10.3390/life12101561

#### Contents

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{% block lead %}Describe the research, experiments, and protocols you used in your project. It is designed to provide sufficient information for other teams to replicate our work.{% endblock %}
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# Experiments

### Design and Planning

The experimental section of our project focused on developing a genetic system capable of selecting and
optimizing RNA–RNA interactions between modified single guide RNAs (sgRNAs) bound to catalytically inactive
Cas proteins (dCas). The system was designed to identify cooperative sgRNA pairs that could form stable RNA
dimers through secondary structural elements known as kissing loops. Such dimeric RNA interactions would
enable two dCas proteins to form a DNA loop when bound to adjacent target sites, thereby increasing
targeting precision and stability without introducing double-strand breaks.

To achieve this goal, the work was divided into two major stages:

#### Evaluation of Known RNA Dimerization Domains

At the first stage, we focused on testing the functionality of previously characterized RNA structures
capable of forming hairpin loops and mediating RNA–RNA dimerization. We selected the dimerization domain
described by Hiromi Mutsuro-Aoki et al. (2022), “Acquisition of Dual Ribozyme-Functions in Nonfunctional
Short Hairpin RNAs through Kissing-Loop Interactions,” Life, 12(10):1561. This domain
was inserted into the 3′ end of a standard sgRNA scaffold via a short linker to assess its ability to
enhance RNA–RNA dimerization and the formation of paired dCas complexes on model DNA fragments. The modified
sgRNAs were tested alongside unmodified controls using complementary methods, including electrophoretic
mobility shift assay (EMSA), bio-layer interferometry (BLI), and in vivo fluorescence-based assays in
bacterial systems. The obtained data demonstrated that such modification partially enhanced complex
stability but was not sufficient to achieve consistent or reproducible results. Consequently, the team
decided to design an improved RNA domain with stronger dimerization potential to increase the stability of
paired dCas complexes.

#### Development of a New RNA Dimerization Domain and Selection System

Based on the results of the initial experiments, the second stage focused on developing a new RNA–RNA
interaction motif and constructing a three-plasmid genetic screening system to identify and quantify the
most stable sgRNA–sgRNA interactions. This selection system was built around the Ptet/TetR regulatory
circuit, which allowed the formation of stable RNA dimers between sgRNAs to be translated into a measurable
fluorescent signal.

The system consisted of three functional plasmids:

* **Reporter plasmid** — expressing GFP under the control of the Ptet promoter.
* **Sensor plasmid** — constitutively expressing TetR and containing dCas protein binding sites.
* **Expression plasmid** — encoding the dCas protein and pairs of modified sgRNAs.

In this system, strong RNA–RNA interactions between sgRNAs promote DNA looping between two dCas complexes,
reducing TetR expression and, consequently, activating GFP fluorescence. This feedback provides a
quantitative measure of RNA–RNA interaction stability inside living cells.

At this stage, we also introduced randomized sequences (10–20 nucleotides) at the 3′ end of the sgRNA
scaffolds to identify novel dimerization sequences beyond known kissing-loop motifs. The use of chimeric
sgRNAs with double scaffolds was intentionally avoided to minimize RNA size, preserve activity, and reduce
potential off-target effects. The resulting sgRNA constructs were subsequently validated in vitro using
minicircle-derived recombined cccDNA (mcccDNA) — a non-infectious model closely mimicking the structure and
behavior of natural HBV cccDNA.

#### Design Objectives

At the planning stage, the following key objectives were defined:

* Construct and validate plasmids expressing sgRNAs and dCas proteins under different promoter systems.
* Establish a controllable in vivo screening system capable of detecting RNA–RNA cooperativity through GFP
  activation.
* Quantitatively assess RNA–RNA binding strength using EMSA and BLI biophysical assays.
* Determine how RNA dimerization influences dCas–DNA complex formation and transcriptional repression
  efficiency in bacterial models.
* Develop an expandable platform adaptable to different Cas protein variants and regulatory circuits.

### Stage I — Dual dCas via RNA–RNA (kissing-loop) to modulate gene expression

#### Choice of dimerization domain and sgRNA modification

We selected the dimerization domain reported in *Life* 12(10):1561 (10.3390/life12101561). A standard
sgRNA was modified by appending this domain to the 3′ end through a short linker. We assessed the
adaptability of this modification versus unmodified sgRNA using EMSA, BLI, and cell-based assays.

#### Creation of literature-based structures (kissing loop) and primer design

After an extensive literature review, we selected novel RNA sequences for primer design to obtain
kissing-loop structures capable of stabilizing double dCas complexes. We then compared the binding and
dissociation kinetics of these structures with conventional sgRNA in dCas–RNA–DNA complexes.

**Table 1. Primer sequences for kissing-loop sgRNA construction**

| Primer Name | Sequence (5'→3') |
| --- | --- |
| sg1 AF1-F | TAATACGACTCACTATGTTTCGGTTGAGCAAATAACGGTTTTAGAGCTAGAAATAGCAAGTT |
| sg1 AF2-F | GCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTC |
| sg1 AF3-F | AAAAGTGGCACCGAGTCGGTGCTGTGATGCTGAAGTGCACACGGCA |
| sg2 AF1-F | TAATACGACTCACTATAGTTTCGGTTGAGCAAATAACGGTTTTAGAGCTAGAAATAGCAAG |
| sg2 AF2-F | GAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAG |
| sg2 AF3-F | GAAAAAGTGGCACCGAGTCGGTGCTGTGATGCTGAAGTGCACACGGCAGTGGCACCGAGTCGGTGCT |
| sg1 Tracr-R | TGTTGCCGTGTGCACTTCAGCATC |

#### Overlap PCR groups and conditions

We created **six** Overlap PCR groups as follows.

**Table 2. Six primer groups for Overlap PCR**

| Reagent | G1 | G2 | G3 | G4 | G5 | G6 | Volume |
| --- | --- | --- | --- | --- | --- | --- | --- |
| T7 FW10 | + | + | + | + | + | + | 1 μL |
| Sg1AF1 | + | + | + | + | + | + | 2 μL |
| Sg1AF2 | + | + | + | + | + | + | 2 μL |
| Sg1AF3 | + | + |  |  |  | + | 2 μL |
| Sg3AF3 |  |  | + | + |  |  | 2 μL |
| Sg4AF3 |  |  |  |  | + |  | 2 μL |
| Sg1TracrR |  | + |  |  |  |  | 1 μL |
| Sg2TracrR |  | + |  |  |  |  | 1 μL |
| Sg3TracrR |  |  | + | + |  |  | 1 μL |
| Sg3TracrR-T |  |  |  | + |  |  | 1 μL |
| Sg4TracrR |  |  |  |  | + |  | 1 μL |
| Sg4TracrR-T |  |  |  |  |  | + | 1 μL |

**Common mix for all six groups:** dNTP 2 μL; polymerase 1 μL; buffer 10 μL; PCR-grade water 69 μL.

After Overlap PCR, products were analyzed on a **2% agarose gel** (1 g agarose in 50 mL 1× TAE).

![figure 1](https://static.igem.wiki/teams/5861/31/1.avif)

**Figure 1.** Results of electrophoresis on a 2% agarose gel for the six primer sets.

#### In vitro transcription checks and PAGE analysis

We verified kissing-loop RNA products using Taq and a master mix. To exclude kit-related issues, six
transcription reactions were set up: three with an old kit and three with a new kit.

|  |  |
| --- | --- |
| Ultrapure Water | 6 µl |
| DNA template | 1 µl |
| 2X sgRNA Reaction Mix | 10 µl |
| RNase Inhibitor (40 U/µl) | 1 µl |
| Enzyme Mix | 2 µl |

**Table 3. Reaction (Groups 1 & 4)**

|  |  |
| --- | --- |
| Ultrapure Water | 2 µl |
| DNA template | 1 µl |
| 2X sgRNA Reaction Mix | 10 µl |
| RNase Inhibitor (40U/µl) | 1 µl |
| Enzyme Mix | 2 µl |
| NTP Mix | 4 µl |

**Table 4. Reaction (Groups 2 & 5)**

|  |  |
| --- | --- |
| Ultrapure Water | 2.8 µl |
| DNA template | 1 µl |
| 2X sgRNA Reaction Mix | 10 µl |
| Ribolock | 0.2 µl |
| Enzyme Mix | 2 µl |
| NTP Mix | 4 µl |

**Table 5. Reaction (Groups 3 & 6)**

Incubation: **4 h at 37 °C**. Products checked on  **10% urea PAGE**.

![figure 2](https://static.igem.wiki/teams/5861/31/2.avif)

**Figure 2.**  10% TBE gel results with different enzymes (SyBr-stained). EMSA image placeholder.

#### Preparation of targeted DNA molecules (for EMSA/BLI)

To compare binding kinetics between kissing-loop RNA and normal sgRNA, we prepared **2PAM**, **1PAM**,
**0PAM** DNA, dCas protein, kissing-loop RNA, and normal sgRNA for EMSA and BLI. DNA results for the
three PAM variants are shown below.

![Figure 3.](https://static.igem.wiki/teams/5861/31/3.avif)

**Figure 3.** 10% TBE gel results of 2PAM, 0PAM, 1PAM DNA (SyBr-stained). EMSA image placeholder.

#### Verification of RNA–RNA dimer formation

We examined the kissing-loop structure <A>, <Hairpin-U> , their complexity, and the binding of
K1+U1 and K1+A1 (K1 is the kissing-loop RNA from the first experiment).

![Figure 4.](https://static.igem.wiki/teams/5861/31/4.avif)

**Figure 4.** 10% TBE gel results of RNA structures (SyBr-stained). EMSA image placeholder.

This shows successful formation of a kissing-loop structure from <A> + <Hairpin-U> ,
outperforming **K1.**

#### Verification of double dCas complexes (EMSA)

We next evaluated formation of double Cas complexes. The gel below shows results for varying concentrations
of **sgRNA–Cas9** RNP mixed with **2PAM** or **0PAM** DNA.

![Figure 5. ](https://static.igem.wiki/teams/5861/31/5.avif)

**Figure 5.**  10% TBE gel of RNP–DNA complexes (2PAM vs 0PAM; SyBr-stained). *EMSA image
placeholder.*

EMSA indicated **no binding** to **0PAM DNA** , while **2PAM DNA** showed binding at **100 nM**
and **250 nM**. Further quantification was performed by BLI.

#### BLI kinetics vs standard scaffolds

We used **BLI** to measure binding kinetics.

![Figure 6.](https://static.igem.wiki/teams/5861/31/6.avif)

**Figure 6.** Normal sgRNA—dual Cas protein—DNA (Blue: 2PAM; Red: 0PAM).

![Figure 7.](https://static.igem.wiki/teams/5861/31/7.avif)

**Figure 7.** Kissing-loop RNA—dual Cas protein—DNA (Blue: 2PAM; Red: 0PAM).

BLI revealed enhanced binding to **2PAM** vs **0PAM**. Despite design specificity, RNPs showed some **non-specific**
binding to 0PAM. Kinetic parameters were analyzed and compared in **GraphPad Prism**.

![Figure 8.](https://static.igem.wiki/teams/5861/31/8.avif)

**Figure 8.** Four groups of BLI data analyzed in GraphPad Prism.

Because 0PAM binding is nonspecific/unstable, we compared **2PAM** data only:

**Table 6. Average Kon, Koff, Kd for 2PAM DNA groups**

| Group | Kon (min⁻¹·s⁻¹) | Koff (s⁻¹) | Kd (nM) |
| --- | --- | --- | --- |
| Normal sgRNA | 93,800 | 0.002576 | 27.46 |
| Kissing-loop RNA | 410,211 | 0.002422 | 5.90 |

Kissing-loop RNA increased **Kon** and reduced **Koff/Kd**, indicating faster and tighter targeting by
dCas.

#### Comparative CRISPR–dCas9 activity: dual kissing-loop vs. dual simple sgRNA

The sgRNAs in this study were designed to target promoter regions upstream of the **GFP** gene.
When guided by these sgRNAs, **dCas9** binds promoter DNA and blocks transcription, thereby
reducing GFP expression. Consequently, **cellular GFP fluorescence** provides an indirect but
robust readout of CRISPR–dCas9 repression: **lower median GFP intensity ⇒ stronger transcriptional
repression**.

To compare alternative **dual-sgRNA** designs (simple vs. kissing-loop scaffolds) fairly, we
first attempted to restrict analysis to **only those cells that actually expressed dCas9**. The
workflow was: fixation/permeabilization → **primary antibodies** (Recombinant Anti-Flag Tag
(mouse mAb)) → **secondary antibody** (Alexa Fluor 647 AffiniPure Goat Anti-Mouse IgG) → flow
cytometry (CytoFLEX). The plan was to gate **Alexa 647–positive** events (dCas9-expressing
cells) and then quantify GFP exclusively within this population, thereby minimizing bias from imperfect
transfection efficiency.

Despite multiple titrations (including **1:100**), **nonspecific primary binding**
produced background Alexa 647 signals sufficient to compromise confident separation of dCas9-positive from
negative cells. Given this persistent ambiguity, we **abandoned antibody gating** to avoid
inflating variance and instead adopted a simpler, more direct fluorescence readout.

Cells were washed **three times with PBS** to remove residual transfection reagents and unbound
molecules, then immediately analyzed on **CytoFLEX**. We used **median GFP
intensity** as the primary metric because it is robust to outliers and better reflects central
tendency in potentially heterogeneous populations (half the cells have lower GFP, half higher).
Representative density plots and the **singlet gating strategy** (to exclude aggregates) are
shown in **Figure 9**, confirming proper sample preparation and that the collected
distributions faithfully capture construct-dependent differences in GFP.

In both tested lines (**HEK293T, K562**), expressing **dual sgRNA CRISPR–dCas9**
systems reduced GFP fluorescence relative to the **dCas9-only control**, indicating successful
promoter repression. Importantly, the **dual kissing-loop sgRNA scaffold** consistently yielded
**lower median GFP** than the **dual simple sgRNA scaffold**, i.e., stronger
repression at the same targeting sites. This pattern reproduced across multiple biological repeats.

Below is a representative dataset (three independent K562 samples) reporting **median GFP
(FITC-A)** and normalization to the **dCas9-only control (=100%)**. In **replicate
1**, the median GFP decreased from **85,558.5** (control) to **79,569**
with dual simple sgRNA (**−7.0%**) and further to **76,756.3** with dual
kissing-loop sgRNA (**−10.3%**). Similar trends persisted in replicates 2–3, with kissing-loop
constructs outperforming simple sgRNA in each case.

**Table 7.** Quantitative comparison of median GFP fluorescence intensity in K562 cells transfected with
different CRISPR–dCas9 systems

*(Median GFP intensity measured by flow cytometry; values normalized to dCas9-only control = 100%)*

|  | K562 cell sample | Median GFP FITC-A |
| --- | --- | --- |
| 1 | dCas9 | 85558.5 |
| dCas9 + dual simple sgRNA | 79569 |
| dCas9 + dual kissing-loop sgRNA | 76756.3 |
| 2 | dCas9 | 79644 |
| dCas9 + dual simple sgRNA | 79086 |
| dCas9 + dual kissing-loop sgRNA | 76174 |
| 3 | dCas9 | 99292 |
| dCas9 + dual simple sgRNA | 94264 |
| dCas9 + dual kissing-loop sgRNA | 92708 |

Each row represents an independent sample set. Median GFP intensity was measured by flow cytometry, and
values were normalized to the dCas9-only control (set as 100%). Both dual sgRNA systems reduced GFP
expression, with the kissing-loop sgRNA system showing consistently higher repression efficiency compared to
the simple sgRNA system.

![Figure 9.](https://static.igem.wiki/teams/5861/31/9.avif)

**Figure 9.** CytoFLEX density plots and singlet-gating table illustrating cell density, singlet fraction,
and GFP intensity distributions used for median calculations.

![Figure 10](https://static.igem.wiki/teams/5861/31/10.avif)

**Figure 10.** Bar graph of median GFP intensity in K562 for **dCas9 only**, **dual simple sgRNA**,
and **dual kissing-loop sgRNA** across three independent experiments.

#### Concluding note on Stage I progress

Across Stage I we established that sgRNA scaffolds engineered with kissing-loop dimerization motifs improve
biophysical and cellular performance relative to simple dual-sgRNA designs. On the biophysical side, Overlap
PCR and transcription workflows (Tables 1–5; Figures 1–3) produced the intended RNA species and PAM-variant
DNA substrates; EMSA verified RNA–RNA dimer formation and supported assembly of dual dCas–DNA complexes with
2PAM, but not 0PAM, targets (Figures 4–5). BLI kinetics demonstrated a higher association rate and lower
apparent dissociation constant for kissing-loop sgRNAs versus unmodified scaffolds on 2PAM DNA (Table 6;
Figures 6–8), indicating faster target engagement and tighter complex stability. In cells, median GFP
measurements on CytoFLEX confirmed functional repression by both dual-sgRNA formats, with a reproducible
additional decrease in GFP for the kissing-loop design across biological replicates in HEK293T and K562
(Table 7; Figures 9–10). Collectively, these data support a mechanistic model in which engineered RNA–RNA
interactions stabilize paired dCas occupancy and promoter occlusion, thereby enhancing transcriptional
repression without double-strand breaks. The residual non-specific interactions observed on 0PAM in BLI,
along with modest but consistent effect sizes in populations, motivated Stage II:

1. building a robust parts pipeline (restriction/ligation/assembly) to iterate kissing-loop variants at
   scale,
2. implementing selectable/reportable circuits (e.g., TetR/Ptet) to enrich higher-affinity RNA dimers in
   vivo,
3. and preparing validated target architectures (including mcccDNA-based constructs) for downstream
   screening and functional evaluation.

### Stage II — Constructing advanced kissing-loop systems (genetic parts and assembly)

#### Plasmid sourcing, culture, and purification

At the second stage, we created and tested the genetic constructs necessary to implement advanced
kissing-loop systems. A series of experiments was conducted to clone and assemble genetic parts. For several
designs, we used ready-made components from the iGEM collection.

Initially, we used **two plasmids from the iGEM BOX** (the complete list of plasmids is shown in **Table
8**). We transformed **E. coli Top10** to obtain the target plasmids. After propagation, plasmids were
purified using an **Omega midi kit**.

![Figure 11.](https://static.igem.wiki/teams/5861/31/11.avif)

**Figure 11.** Results of gel electrophoresis of the obtained parts.

**Table 8. Plasmids containing the target genes used in the experiment.**

| Plasmid species | Part ID | Well | Antibiotic | Plasmid size (bp) | Target size (bp) |
| --- | --- | --- | --- | --- | --- |
| Constitutive promoter | BBa\_J23114 | I5 | Chloramphenicol | 2,424 | 39 |
| Strong constitutive promoter | BBa\_J23110 | G5 | Chloramphenicol | 2,424 | 39 |
| Weak constitutive promoter | BBa\_J23116 | K5 | Chloramphenicol | 2,424 | 39 |
| RBS | BBa\_J428032 | O5 | Chloramphenicol | 2,413 | 28 |
| TetR | BBa\_J428028 | M4 | Chloramphenicol | 2,670 | 625 |
| Terminator 1 | BBa\_J428092 | C1 | Chloramphenicol | 2,520 | 135 |
| Terminator 2 | BBa\_J428091 | A1 | Chloramphenicol | 2,432 | 47 |

#### Additional plasmid preparation and quality check

On the following day, we obtained the remaining plasmids listed in **Table 8** using the same **Top10**
culture and **Omega midi kit** purification workflow; however, the concentrations were not very high.
Electrophoresis results of these additional plasmids are shown below.

![Figure 12.](https://static.igem.wiki/teams/5861/31/12.avif)

**Figure 12.** Electrophoresis of the remaining plasmids (concentration check).

#### Restriction digestion and target-fragment isolation

To obtain target DNA fragments from the purified plasmids, we performed restriction digestion using **Rapid
Cut II BsaI** (Biosharp), a type IIS endonuclease recognizing **5′-GGTCTC-3′** and cutting outside the
site to generate sticky ends compatible with subsequent ligations.

**Reaction (10 μL total)**: 8.8 μL purified plasmid (50 ng/μL), 1 μL 10× Rapid Cut Buffer, 0.2 μL BsaI
(10 U/μL). Incubation **37 °C for 60 min**, then heat inactivation **80 °C for 20 min**. Digestions
were verified by gel electrophoresis.

![Figure 13.](https://static.igem.wiki/teams/5861/31/13.avif)

**Figure 13.** Electrophoresis results.The red-marked parts indicate lanes with uncut plasmids (control
groups), showing single bands at the original plasmid size. The blue-marked parts indicate lanes with
digested plasmids, showing two bands: one corresponding to the target DNA fragment and the other
corresponding to the plasmid backbone . The presence of two distinct bands confirms successful digestion by
BsaI. Note: The small target fragments may appear faint or migrate off the gel due to their small size;
longer fragments are more clearly visible.

#### Gel extraction (recovery), ligation into backbone, and transformation

To isolate target fragments (e.g., **39 bp promoter**, **625 bp TetR**) from the
digested mixtures, we performed **DNA gel recovery** using the **Beyotime kit**.
Steps: excise bands under UV (minimize exposure), weigh gel, dissolve in **Binding Buffer**,
load onto spin column, centrifuge to bind DNA, wash with **Wash Buffer**,
elute with **Elution Buffer**. Concentration and purity were measured on a
**Nanodrop** (results below).

**Table 9. Concentration and purity of recovered target DNA fragments.**

| Sample ID (Target Fragment) | ng/µL | A260/A280 | A260/A230 | Purity assessment |
| --- | --- | --- | --- | --- |
| 1 (BBa\_J23114) | 0.5 | 1.09 | 0.01 | Low |
| 2 (BBa\_J23110) | 0.7 | 1.43 | 0.09 | Low |
| 3 (BBa\_J23116) | 2.6 | 3.25 | 0.00 | High |
| 4 (BBa\_J428032) | 1.6 | 0.95 | 0.19 | Low |
| 5 (BBa\_J428032) | 2.2 | 2.40 | 0.00 | High |
| 6 (BBa\_J23116) | 7.1 | 1.58 | 0.44 | Low |
| 7 (BBa\_J428028) | 2.2 | 1.20 | 0.17 | Low |
| 8 (BBa\_J428326) | 22.1 | 1.51 | 0.36 | Moderate |

Recovered fragments (promoter, RBS, TetR, terminators) were ligated into a **linearized vector backbone**
(e.g., **pSB1C3**, BsaI-opened and gel-purified).

To verify **transformation efficiency** of **Top10 competent cells**, we
transformed serial dilutions of a control plasmid
(final DNA: **10, 1, 0.1, 0.01 ng/µL**) into **100 µL** aliquots, plated **100
µL** on
**kanamycin LB plates**, and incubated **overnight**.
Colony counts were used to calculate efficiency (colonies/µg DNA).
The results indicate **high efficiency**, adequate for routine cloning.
Colony counts are shown below.

![Figure 14.](https://static.igem.wiki/teams/5861/31/14.avif)

**Figure 14.** Colony counts from the transformation-efficiency test.

After overnight incubation, **all colonies on kanamycin LB** exhibited
**green fluorescence** under **365 nm UV**, indicating expression of the
**GFP reporter** (linked to the assembled construct).
The absence of non-fluorescent colonies suggests that
**ligation efficiency for intended non-GFP assemblies was low**
(i.e., background GFP-positive constructs predominated).

![Figure 15.](https://static.igem.wiki/teams/5861/31/15.avif)

**Figure 15.**  Green fluorescent colonies on LB-kanamycin plates (UV transillumination).

#### Design and synthesis of target-DNA–containing structures

When selecting target sequences, we relied on *Permanent Inactivation of HBV Genomes by
CRISPR/Cas9-Mediated Non-cleavage Base Editing* (Mol Ther Nucleic Acids. 2020 Jun 5;20:480-490), which
evaluated Cas protein activity across various regions of the hepatitis B virus (HBV) genome. Based on this
analysis, we selected targets judged optimal and effective by the authors and used them in our work.

In subsequent experiments, we plan to use **minicircle-derived recombined cccDNA (mcccDNA)** as a model
DNA molecule. After analyzing the paper, we identified a set of targets matching our criteria (see
Supplement for the full list). For our experiments, we used sequences **gP3** (GGGAACAAGATCTACAGCAT) and
**gP7** (TGCTCCAGCTCCTACCTTGT) as target sites for Cas proteins.

To test our system, we constructed sensor plasmid variants with different arrangements of the selected target
sequences.

![Figure 16.](https://static.igem.wiki/teams/5861/31/16.avif)

**Figure 16.** Schematic of the constructed element showing the positions of the most active targets on
the planned mcccDNA molecule for future validation.

![Figure 17.](https://static.igem.wiki/teams/5861/31/17.avif)

**Figure 17.** Schematics of sensor plasmid variants with different target-sequence arrangements.

#### Tetracycline resistance analysis and system verification

We confirmed with **Sangon Biotech** the feasibility to synthesize
**three DNA sequences**, clone them into **pCDFDuet-1** using
**EcoRI** and **PstI**, and introduce
**random nucleotides at two positions** within the third sequence.
These three sequences and **two restriction endonucleases** were ordered.

We plated **10 µL** of bacterial suspension containing the target plasmid
(from Sangon) on **10 mm kanamycin LB agar**.

![Figure 18](https://static.igem.wiki/teams/5861/31/18.avif)

**Figure 18.** Colony growth result.

To prevent excessively rapid growth and to standardize comparisons, we performed
**gradient dilutions** (×10 and ×100), plated **100 µL** on
**kanamycin LB**, and incubated at **37 °C**.
Colonies were inoculated into **4 mL LB** and grown **overnight**.
We prepared **eight** plates with different plasmid/antibiotic combinations
(see **Table 10**). We observed **no growth** on plates
**6** and **7**, whereas other plates showed growth.

**Table 10. Contents of plasmid/antibiotic culture dishes.**

| No. | Plasmid | Antibiotic |
| --- | --- | --- |
| 1 | P1AP1gP3gP7A + P2sfGFP | Amp + Kan |
| 2 | P1AP1gP3gP7B + P2sfGFP | Amp + Kan |
| 3 | P1AP1gP3gP7R + P2sfGFP | Amp + Kan |
| 4 | P2sfGFP | Amp |
| 5 | P1AP1gP3gP7A + P2cmR | Amp + Kan |
| 6 | P1AP1gP3gP7A + P2cmR | Amp + Kan + Cm |
| 7 | P1AP1gP3gP7A + P2cmR | Cm + Kan |
| 8 | P2cmR | Amp |

We prepared a **tetracycline stock (50 mg/mL)** and **two sets of six LB-agar plates**  with
tetracycline concentration gradients (Table 11).

**Table 11. Different concentration gradients of TetA required.**

| No. | C\_TetA | Volume of stock solution |
| --- | --- | --- |
| 1 | 10 ng/mL | 0.004 µL |
| 2 | 100 ng/mL | 0.04 µL |
| 3 | 1 µg/mL | 0.4 µL |
| 4 | 10 µg/mL | 4 µL |
| 5 | 50 µg/mL | 20 µL |
| 6 | 100 µg/mL | 40 µL |

From the 50 mg/mL stock, we prepared **10 ng/mL**, **100 ng/mL**, and **1
µg/mL** TetA working concentrations.
One set of plates contained tetracycline gradients with **chloramphenicol + kanamycin**;
the other set contained **ampicillin + chloramphenicol + kanamycin** across all six plates.

We took colonies (see Figure 18) from **No. 5 (P1AP1gP3gP7A + P2cmR)** and
**No. 2 (P1AP1gP3gP7B + P2sfGFP)**, prepared suspensions, and
**spotted 5 µL** onto each plate in both sets; incubation **37 °C overnight**.

![Figure 19.](https://static.igem.wiki/teams/5861/31/19.avif)

**Figure 19.** Growth of bacteria on Plates **No. 4, 5, 6** (tetracycline gradients).

Cells carrying **P1P1gP3gP7B + P2sfGFP (Plate No. 2)** and
**P1AP1gP3gP7A + P2cmR (Plate No. 5)** grew at
**10 ng/mL** and **100 ng/mL** tetracycline.
Moreover, **Plate No. 5 (P1AP1gP3gP7A + P2cmR)** showed growth
even at **1 µg/mL** tetracycline, though colony numbers decreased
as tetracycline increased.

![figure 20](https://static.igem.wiki/teams/5861/31/20.avif)

**Figure 20.** Growth of bacteria on **Plate No. 5**  in tetracycline-containing media.

![figure 21](https://static.igem.wiki/teams/5861/31/21.avif)

**Figure 21.** Growth of bacteria on **Plate No. 2**  in tetracycline-containing media.

We also prepared **six** plates with **Amp + Kan** and the same tetracycline
gradients and cultured
**No. 1 (P1AP1gP3gP7A + P2sfGFP)**. Then we repeated plate preparation for
**No. 3 (P1AP1gP3gP7R + P2sfGFP)** and performed transformation.
Colonies from **No. 1**, **No. 2**, and **No. 3** plates were
inoculated into
**4 mL LB** and grown **overnight at 37 °C**.

#### Concluding note on Stage II progress

In summary, **target plasmids were successfully obtained and characterized**, workflows for
**restriction, ligation, and transformation** were established, and
**in vivo functional testing** of the assembled constructs was initiated.
The antibiotic-selection and tetracycline-gradient results provide a first validation layer for construct
integrity
and selectable marker function, and they set the stage for integrating these advanced kissing-loop
components
into the full selection system described in Stage I.

## References

Mutsuro-Aoki, H., & Tamura, K. (2022). Acquisition of dual ribozyme functions in nonfunctional short hairpin RNAs through kissing-loop interactions. Life, 12(10), 1561. https://doi.org/10.3390/life12101561

Guo, X., Chen, P., Hou, X., Xu, W., Wang, D., Wang, T.-Y., Zhang, L., Zheng, G., Gao, Z.-L., He, C.-Y., Zhou, B., & Chen, Z.-Y. (2016). The recombined cccDNA produced using minicircle technology mimicked HBV genome in structure and function closely. Scientific Reports, 6, 25552. https://doi.org/10.1038/srep25552

Yang, Y.-C., Chen, Y.-H., Kao, J.-H., Ching, C., Liu, I.-J., Wang, C.-C., Tsai, C.-H., Wu, F.-Y., Liu, C.-J., Chen, P.-J., Chen, D.-S., & Yang, H.-C. (2020). Permanent inactivation of HBV genomes by CRISPR/Cas9-mediated non-cleavage base editing. Molecular Therapy – Nucleic Acids, 20, 480–490. https://doi.org/10.1016/j.omtn.2020.03.005

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# Results

### Evaluation of the Activity of Known Dimerization Domains

The results of the first part of our work focused on evaluating the effectiveness of known RNA dimerization domains. In the course of this stage, we conducted a series of experiments to assess RNA–RNA dimerization, the possibility of forming double Cas complexes, and to measure the dissociation constants and functional activity of these systems in model eukaryotic cells.

![figure 1](https://static.igem.wiki/teams/5861/24/1.webp)

**Figure 1.** *Scheme of experiments in vivo.*

During the in vivo tests, we used a dimerization stem-loop described in the study by Mutsuro-Aoki H., “Acquisition of Dual Ribozyme-Functions in Nonfunctional Short Hairpin RNAs through Kissing-Loop Interactions,” Life, 2022. The standard sgRNA scaffold was modified with RNA hairpins, and their activity was analyzed using several complementary methods. These modifications enabled the formation of RNA–RNA dimers and stabilized double complexes of dCas proteins on a model DNA molecule.

![Figure 2](https://static.igem.wiki/teams/5861/24/2.webp)

**Figure 2.**
*10% TBE gel results of sgRNA structures modified with different hairpins, stained with SyBr Green (EMSA image).*

As shown in the figure, the modification with the **A-type hairpin** (see sequence in [Experiments](https://2025.igem.wiki/smbu/experiments)) most actively promoted the formation of RNA–RNA pairs. This sequence was further analyzed for its potential to form dimers between dCas proteins.

![figure 3](https://static.igem.wiki/teams/5861/24/3.webp)

**Figure 3.**
*Results of the 10% TBE gel for complexes of varying concentrations.*

The obtained data indicate that the complexes successfully formed dimers with a model DNA molecule, confirming that this hairpin structure can be used to create stable dCas–RNA complexes.

In addition, the activity of this system was examined using **Bio-Layer Interferometry (BLI)**.

![figure 4](https://static.igem.wiki/teams/5861/24/4.webp)

**Figure 4.**
*Kissing-loop RNA–dual Cas protein–DNA and normal sgRNA–dual Cas protein–DNA combinations. Measurement of dissociation constants using BLI.*

As shown in the figure, there was a noticeable stabilization of Cas complexes on the targeted DNA molecule compared to the standard guide RNA. This result demonstrated the potential of using structures such as **kissing-loop RNA** to form more stable double complexes. In subsequent stages, this structure was used to measure the activity of the complexes in in vivo models.

![figure 5](https://static.igem.wiki/teams/5861/24/5.webp)

**Figure 5.**
*Bar graph showing the median GFP fluorescence intensity in K562 cells transfected with the modified CRISPR–dCas9 system.*

Flow cytometry analysis of K562 cells showed no statistically significant decrease in GFP expression levels. Consequently, we decided to design a new system that would enable the generation of additional RNA–RNA interaction variants to improve the functional parameters of the complexes.

### Creation of a System for Selecting the Most Effective RNA–RNA Interactions

When selecting target sequences, we relied on the study by Yu-Chan Yang et al., “Permanent Inactivation of HBV Genomes by CRISPR/Cas9-Mediated Non-cleavage Base Editing,” Mol Ther Nucleic Acids, 2020. This work evaluated the activity of Cas proteins in different regions of the hepatitis B viral genome. After analyzing the results presented by the authors, we selected the most optimal and effective targets and applied these sequences in our experiments. The complete nucleotide sequences used are listed in
[Experiments](https://2025.igem.wiki/smbu/experiments).

#### Design of the Selection System

At the second stage of our work, we focused on creating an efficient system for generating and selecting stable pairwise interactions between two modified sgRNAs.

A schematic representation of the experimental design is shown below.

![figure 6](https://static.igem.wiki/teams/5861/24/6.webp)

**Figure 6.** *General scheme of RNA–RNA interaction screening.*

To achieve this goal, we designed and constructed a set of plasmids carrying different fragments of the genetic cassette required to obtain and select the most stable RNA–RNA dimers in dCas complexes.

The complete plasmid schematics and maps are provided in Experiments.

The system consisted of **three functional plasmids**:

1. **Reporter plasmid** – encoding GFP under the control of the pTet promoter.
2. **Sensor plasmid** – containing the TetR gene and dCas protein with variable promoter configurations.
3. **Expression plasmid** – encoding guide RNAs with modified scaffolds.

**Table 1.** Expression Plasmids

| Plasmid Name | Description |
| --- | --- |
| P3sgRNAgen | Control plasmid |
| P3sgRNAp3p7 | Plasmid with a standard sgRNA scaffold |
| P3sgRNAp3p7n10 | Scaffold with 10 random nucleotides at the 3′ end |
| P3sgRNAp3p7n15 | Scaffold with 15 random nucleotides |
| P3sgRNAp3p7n20 | Scaffold with 20 random nucleotides |

**Table 2.** Sensor Plasmids

| Plasmid Name | Description |
| --- | --- |
| P1gP3gP7A | Plasmid with a promoter and two target sites (before and after screening) expressing TetR |
| P1gP3gP7B | Plasmid with two targets upstream of the promoter |
| P1gP3gP7R | Plasmid with two targets downstream of the promoter |

#### Experimental Evaluation

During the experiments, all three plasmid types were constructed and functionally tested. Cotransfection produced cells containing all plasmid combinations, which were then analyzed for clone viability and growth dynamics in the presence of tetracycline.

![figure 7](https://static.igem.wiki/teams/5861/24/7.webp)

**Figure 7.**
*Growth of bacterial clones on tetracycline-containing plates. Tetracycline concentration increases from 10 ng/mL (top) to 100 µg/mL (bottom).*

When analyzing the results, we observed an unexpected trend: as the concentration of tetracycline increased, cells carrying the tetracycline-resistance gene failed to survive at concentrations above **10 µg/mL**. This finding indicated insufficient promoter activity in the sensor plasmid and revealed the need to modify the regulatory system for improved responsiveness.

Despite this limitation, our experiments confirmed high activity of double Cas complexes on a model DNA substrate, and we successfully established a functional three-plasmid system. Subsequent control tests using tetracycline further demonstrated that replacing the constitutive promoter with a stronger variant would significantly improve system efficiency.

Learn more in [Experiments](https://2025.igem.wiki/smbu/experiments).

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