iGEM 2023 — Team cjuh-jlu-china

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Engineering

* [Part I: Simulation and Validation of the activity of CRISPR/Cas13a system](#part1)

* [Part II: Cloning and Expression of crRNA](#part2)

![](https://static.igem.wiki/teams/4611/wiki/dnaimg.png)

# Engineering

## Part I: Simulation and Validation of the activity of CRISPR/Cas13a system

### Brief Introduction

The CRISPR/Cas13a system is frequently used in detection and cleavage of oligonucleotide due to its
unique collateral cleavage activity. In recent years, the relationship between microRNA (miRNA) and
cancer progression has gained attention, making it an important biomarker. Our project aims to use the
CRISPR/Cas13a system to detect miR-21-5p, which is a biomarker for breast cancer. Usually, optimizing a
CRISPR/Cas13a system that could specifically and efficiently target a miRNA requires a large amount of
wet lab experiments, which needs a lot of experimental materials and time. To save these experimental
materials and time, we utilized a set of computational approaches to simulate the reaction conditions
and validated the activity of CRISPR/Cas13a system by actual experiments. Noteworthy, this set of
approaches can be readily utilized in other iGEM projects that also use engineered CRISPR/Cas13a for
diagnosis or treatment of diseases.

### Design

First, We designed four crRNA sequences corresponding to the sequence of miR-21-5p. Previous studies have
shown that the length of the crRNA guide sequence affects the overall activity of the CRISPR/Cas13a
system. The activity of the CRISPR/Cas13a
system was significantly reduced if the length of the guide
sequence was less than 20 nt.

| Name | Sequence |
| --- | --- |
| miR-21-5p | UAGCUUAUCAGACUGAUGUUGA |

Table 1 miRNA candidates' sequences

| crRNA | Sequence |
| --- | --- |
| crRNA full length | GAUUUAGACUACCCCAAAAACGAAGGGGACUAAAAC UCAACAUCAGUCUGAUAAGCUA |
| crRNA1 | GAUUUAGACUACCCCAAAAACGAAGGGGACUAAAAC AACAUCAGUCUGAUAAGCUA |
| crRNA2 | GAUUUAGACUACCCCAAAAACGAAGGGGACUAAAAC CAACAUCAGUCUGAUAAGCU |
| crRNA3 | GAUUUAGACUACCCCAAAAACGAAGGGGACUAAAAC UCAACAUCAGUCUGAUAAGC |

Table 2 crRNA sequence for miR-21-5p

To validate the stability of these crRNA, we evaluated crRNA in three ways: 1) Minimum Free Energy; 2) GC
content; 3) MFE heterodimers.

| crRNA | MFE (kcal/mol) | GC content | MFE heterodimer (kcal/mol) |
| --- | --- | --- | --- |
| crRNA full length | -9.6 | 0.40 | -42.5 |
| crRNA1 | -9.8 | 0.39 | -41.6 |
| crRNA2 | -9.7 | 0.41 | -40.9 |
| crRNA3 | -9.6 | 0.41 | -39.8 |

Table 3 Simulation parameters for crRNA

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig1.png)
Figure 1 Secondary structure of crRNA. (A) crRNA1; (B) crRNA2; (C) crRNA3; (D)
crRNA full length(Green: Stems, Yellow: Interior Loops, Blue: Hairpin loops, Orange: 5' and 3' unpaired
region)

The stability of crRNA can be described using two factors: the Minimum Free Energy (MFE) and the GC
content. A smaller MFE value and a larger GC content value indicate a more stable crRNA. In addition,
the MFE Heterodimer is used to describe the stability of the binary complex formed between crRNA and
miRNA. A smaller MFE Heterodimer value indicates a more stable binary complex formed by the combination
of crRNA and miRNA. The stability of both the crRNA structure and binary complex structure are essential
for the proper functioning of the CRISPR/Cas13a system. Our findings indicate that the stability of
crRNA full-length is better than the other crRNA sequences, as is the stability of the complex formed by
crRNA and miRNA.

### Build and Test

#### Computer simulation

##### Section 1 Molecular Docking

Molecular docking is a method of predicting receptor-ligand interactions. Based on the properties of
receptor and ligand molecules, various possible conformations are docked and scored based on energy and
binding conditions.

The first step of molecular docking is to obtain the PDB structure files of receptors and ligands. Since
PDB file of the LwaCas13a protein (1152AA) was not available in the UniProt, we used a homologous
modeling prediction method to obtain the PDB structure file of LwaCas13a. Due to the 82.56% similarity
between protein sequences of LwaCas13a and LbuCas13a, we used LbuCas13a as the template protein. We
adopted the best result of SWISS-MODEL homology modeling as the receptor PDB file of LwaCas13a for
the
subsequent docking. Results of the homologous modeling is displayed with PyMol software.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig2.png)
Figure 2 Homologous modeling of proteins & Preparation of docking files (A) The
crystal structure of LbuCas13a; (B) A homology modeling structure of LwaCas13a; (C) A superimposed image
of LbuCas13a and LwaCas13a; (D) Structural domain annotation of LwaCas13a.

We used the 3d RNA server to obtain the tertiary structure and PDB file of crRNA from its previously
obtained secondary structure.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig3.png)
Figure 3 Predicted structure of crRNA with 3dRNA. (A) crRNA1; (B) crRNA2; (C)
crRNA3; (D) crRNA full length.

After obtaining the PDB files for LwaCas13a and crRNA required for molecular docking, we performed rigid
docking of the LwaCas13a protein and crRNA using HDOCK server. The server provided scoring and RMSD for
each docking.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig4.png)
Figure 4 Docking scores for crRNA and LwaCas13a proteins calculated by HDOCK

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig5.png)
Figure 5 RMSD for crRNA and LwaCas13a proteins calculated by HDOCK

The Docking Score is a widely-used evaluation metric in HDOCK for assessing molecular docking. The metric
measures the capacity of a docking simulation to accurately predict the binding between two molecules. A
lower Docking Score indicates a better docking performance. RMSD (Root Mean Square Deviation) is a
parameter that quantifies the deviation of atoms from their expected positions during molecular docking.
A lower RMSD value corresponds to a more stable docking. By integrating these two parameters, it was
observed that crRNA full length exhibited superior performance in molecular docking.

At HDOCK, we utilize a rigid docking algorithm that streamlines the calculation process and conserves
computational power. However, we acknowledge that the algorithm's relative simplicity may not always
reflect the true scenario with accuracy. To ensure precision in our calculations, we incorporated MOE
(Molecular Operating Environment) to conduct semi-flexible docking of the crRNA full length and
LwaCas13a proteins. This approach enabled us to determine the interaction between the crRNA full length
and LwaCas13a with utmost accuracy.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig6.png)
Figure 6 LwaCas13a/crRNA full length complex

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig7.png)
Figure 7 Interactions and Hydrogen Bonds between LwaCas13a and crRNA

Our findings from molecular docking experiments suggest that crRNA predominantly interacts with the REC
lobe of the LwaCas13a protein, which is consistent with the current literature.

We utilized MOE software to dock miRNA to Cas13a/crRNA complexes, and subsequently visualized the docking
results via Discovery Studio Visualizer.The
three-dimensional structure of the Cas13a/crRNA/miRNA
complex was obtained, and their interactions and schema were calculated.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig8.png)
Figure 8 The predicted structure of LwaCas13a/crRNA/miRNA ternary complex

| Name | Number of Binding Sites | |
| --- | --- | --- |
| miR-21-5p | Helical-1 | crRNA |
| 4 | 8 |

Table 4 Binding sites between miR-21-5p and LwaCas13a/crRNA complex

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig9.png)
Figure 9 Schematic diagram of interaction among LwaCas13a, crRNA and miRNA

Our simulation results demonstrate that miRNAs exhibit strong binding affinity to Cas13a/crRNA complexes,
resulting in robust interactions. It is postulated that in practical experiments, miRNAs exhibit a high
affinity towards the Cas13a/crRNA complex, thereby triggering the nuclease activity of LwaCas13a and
facilitating rapid and efficient cleavage reactions.

Molecular docking provides a static view of how molecules interact
with each
other, much like a still
photograph. On the other hand, molecular dynamics simulation gives a dynamic and continuous perspective
of the same interaction, providing a more detailed and thorough understanding of the underlying
processes. This shift from a static to dynamic approach allows for a deeper exploration of the behavior
and movement of molecules in a system.

##### Section 2 Molecular Dynamic Simulation

To assess the stability of the CRISPR/Cas13a system, we conducted molecular dynamics simulations
utilizing the GROMACS software. We set various parameters to ensure the simulation conditions closely
resembled the actual scenario.

After completing the simulation, GROMACS tools were used to analyze the trajectory and calculate the root
mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible area (SASA), and
hydrogen bonds (Hbonds).

The Root Mean Square Deviation (RMSD) is an important measure of the positional variance of the atoms in
a molecule with respect to a reference position. A small and stable RMSD value indicates a high degree
of stability in the molecule.

The Root Mean Square Fluctuation (RMSF) is a measure of the average position of atoms in a molecule over
time, which indicates the degree of flexibility of the molecule being analyzed during the simulation.
Generally, a larger RMSF value implies a higher degree of flexibility of a particular region of the
molecule and indicates its relative distance from the active position.

Hydrogen bonds are capable of forming both within individual molecules and between multiple molecules.
The stability of a molecule can be determined by the number of hydrogen bonds present. In the context of
molecular dynamics simulations, a consistent count of hydrogen bonds is a strong indication of the
molecule's stability.

The solvent accessible surface area (SASA) is a parameter that is closely associated with the solvation
free energy of a molecule. Additionally, it can serve as a measure of the stability of macromolecules
when placed in different solvents.

The calculation of four parameters for the crRNA was the first step of our analysis.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig10.png)
Figure 10 Molecular dynamics simulation parameters of crRNA full length. ( A.
RMSD of crRNA full length, B. RMSF of crRNA full length, C. SASA of crRNA full length, D. Hbonds of
crRNA full length )

The findings demonstrate that the parameters of crRNA are characterized by good stability, indicating
that the three-dimensional structure of crRNA is relatively stable. Furthermore, we executed simulations
and calculations to determine the parameters of LwaCas13a protein, yielding the following results.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig11.png)
Figure 11 Molecular dynamics simulation parameters of LwaCas13a. ( A. RMSD of
LwaCas13a, B. RMSF of LwaCas13a, C. SASA of LwaCas13a, D. Hbonds of LwaCas13a )

The outcome of the simulation experiment indicated that the LwaCas13a protein parameters remained
relatively stable. This suggests that the structure of the LwaCas13a 3D model was robust and well-suited
for the subsequent docking process.

After determining the stability of crRNA full length and LwaCas13a, we proceeded with molecular dynamics
simulation. The same approach was employed for the complex of crRNA full length and LwaCas13a molecules,
and the parameters were calculated. The results indicate that the crRNA full length and LwaCas13a
complexes are stable.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig12.png)
Figure 12 Molecular dynamics simulation parameters of complex. ( A. Binding Free
Energy of complex, B. RMSD of complex, C. Hbonds of complex, D. RMSF of complex, E SASA of complex. )

Together, low and stable RMSD, stable SASA and Hbonds indicate that the LwaCas13a/crRNA complex is
stable. Furthermore, the complex has low binding energy, which further confirms that the LwaCas13a
protein and crRNA form a stable complex.

##### Section 3 Reaction kinetics simulation

Reaction kinetics simulation is a mathematical modeling method that helps predict the concentration of
reactants or products under different conditions. It involves constructing a system of ordinary
differential equations.

Our designed CRISPR/Cas13a system cleaves reporter RNA. We simulated this process using the
Michaelis-Menten equation. By altering various parameters, we can predict the trend of reactant or
product concentration.

Once miR-21-5p binds to crRNA, it activates the cleavage activity of the Cas13a/crRNA complex, which
cleaves not only the target RNA but also other RNA the reporter RNA. Therefore, the Cas13a/crRNA/miRNA
ternary complex actually can be treated as an enzyme with RNA cleavage activity. We use a fluorophore
(Carboxyfluorescein,FAM) and a quenching group (Black hole quencher, BHQ1) double attached 6U
oligonucleotides as a reporter RNA to evaluate the cleavage efficiency of the Cas13a/crRNA/miRNA ternary
complex, since cleavage of the reporter RNA results in the release of fluorescence quenched by BHQ1,
which can be measured easily. Such process can be simplified as the reaction below.

![](https://static.igem.wiki/teams/4611/wiki/reaction-1.png)

The positive reaction rate constant for the binding reaction between the Cas13a/crRNA/miRNA ternary
complex and the reporter RNA is k1, the reverse reaction rate constant is k2, and the reaction rate
constant for fluorescence generated by cleavage is k3. We can use Michaelis-Menten equation to study the
rate of cleavage activities.

\[
v\_0 = \dfrac{dF}{dt} = \dfrac{V\_m[S]}{K\_m+[S]}
\]

\(v\_0\) represents reaction rate, which is experimentally expressed as the rate of increase in
fluorescence
intensity, and [S] represents the concentration of the substrate, which is the reporter RNA. In actual
reactions, fluorescence gradually degrades. Therefore, the equation can be rewritten as below.

![](https://static.igem.wiki/teams/4611/wiki/kin-equ.png)

In this equation, the \(k\_{cleavage}\) represents the rate of the enzyme activity, which is the cleavage
activity of Cas13a/crRNA/miRNA complex in our case. \(k\_{cleavage} = k3\), \( K\_m=\frac{k2+k3}{k1}\). We
set the initial concentration of the ternary complex as 20 nM and the initial concentration of the
reporter RNA as 1000 nM. The parameter settings are as follows.

\[
k1 = 10 nanomole^{-1} {hour}^{-1}
\]

\[
k2 = 0.1 {hour}^{-1}
\]

\[
k3 = 1000 {hour}^{-1}
\]

\[
r = 0.5{minute}^{-1}
\]

By changing the initial concentration of substrate, enzyme, and the values of different parameters, the
following simulation results can be obtained.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig13.png)
Figure 13 Curves of fluorescence production with different association rate
constants

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig14.png)
Figure 14 Curves of fluorescence production with different decomposition rates

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig15.png)
Figure 15 Curves of fluorescence production with different degradation constants

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig16.png)
Figure 16 Curves of fluorescence production with different reporter RNA
concentrations

It can be seen from (1) that the larger the forward reaction rate constants (\(k\_1, k\_3\)), the faster
the fluorescence reaches its peak. The larger the degradation constant, the faster the fluorescence
reaches its peak. Considering the degradation of fluorescence, the fluorescence intensity ultimately
tends to zero. Since the main difference in actual reactions is the miRNA concentration, and under
physiological conditions miRNA will not be saturated. Therefore we use miRNA concentration to represent
the concentration of the ternary complex in our model. Setting the r value to 0.1 and changing the
initial concentration of the ternary complex, the following curves are obtained.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig17.png)
Figure 17 Curves of fluorescence production with different ternary complex
concentrations

Considering fluorescence degradation, there is an approximate linear relationship between fluorescence
intensity and miRNA concentration within a large range of miRNA concentration (Figure 17).

#### Experimental validation

##### Section 1 Collateral cleavage activity corresponding to different crRNAs

To evaluate the efficiency of the crRNA we designed, we tested four different crRNA sequences for
cleavage of the FAM-BHQ RNA probe. This allowed us to compare the collateral cleavage activity of
various crRNA, as activated by single-stranded RNA such as miRNA.

![](https://static.igem.wiki/teams/4611/wiki/crrna-fit.svg)
Figure 18 Real-time fluorescence intensity curve of different crRNA

Our experiment involved the observation of the activity of different crRNA. The findings indicate that
crRNA 1 and crRNA 2 had poor activity when compared to crRNA 3 and crRNA full ength, which displayed
relatively better activity. Computer simulations were also carried out to determine the parameters of
these crRNAs, and it was observed that crRNA full length had better parameters than crRNA 3(Figure 4 ＆
Figure 5 ). Based on our observations and simulations, we strongly recommend using crRNA full length as
the most suitable option for subsequent experiments.

##### Section 2 Collateral cleavage activity of different miRNA concentrations

We tested the relationship between different miRNA
concentrations (0nM，10nM，25nM，50nM，75nM，100nM，125nM，150nM，175nM，200nM) and the cleavage activity of the
CRISPR/Cas system, using the measured fluorescence intensity to represent the cleavage activity of the
CRISPR/Cas system, and we plotted the following curve.

![](https://static.igem.wiki/teams/4611/wiki/mirna.png)
Figure 19 Fitting curves for different miRNA concentrations and fluorescence
intensities

Based on the experimental results, the system generates the strongest fluorescence intensity when the
miRNA concentration is 100nM. This indicates that the CRISPR/Cas system has the strongest cleavage
activity at this point. As the miRNA concentration increases, the fluorescence intensity decreases,
eventually becoming constant. Our hypothesis is that due to the CRISPR/Cas system's unique collateral
cleavage activity, miRNA may compete with the Reporter RNA substrate. Consequently, a high concentration
of miRNA could lead to a decrease in fluorescence generated by the system's cleavage.

##### Section 3 Experimental validation of enzymatic reaction kinetics

To ascertain the cleavage activity of our CRISPR-Cas system, we initially utilized computer to simulate
the kinetics of the reaction.

In the next step, we conducted experiments to validate the results of the computer simulation and
calculated the relevant parameters. We tested the changes in fluorescence intensity induced by different
concentration gradients of Reporter RNA(0nM, 100nM, 125nM, 250nM, 500nM) over time.

![](https://static.igem.wiki/teams/4611/wiki/reporter.svg)
Figure 20 Real-time fluorescence intensity curve of different Reporter RNA
concentration

The CRISPR/Cas13a system, when activated, can be viewed as an enzyme system. The reporter RNA in the
environment is considered the substrate. The relationship between the two can be described by the
Michaelis-Menten equation.

\[
\dfrac{1}{v\_0} = \dfrac{K\_m}{V\_m}\dfrac{1}{[reporter]}+\dfrac{1}{V\_m}
\]

We used the experimentally obtained data for fitting the above equation, and obtained the relationship
between the concentration inverse and the fluorescence intensity inverse.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig21.png)
Figure 21 The fitting curve of Lineweaver-Burk

After using double inverse graphing, the experimental results were found to be consistent with
Michaelis-Menten equation, which is in line with our expectations.

### Learn

We have made great efforts in the ENGINEERING part and finally optimized the simulation and validation
pipeline of CRISPR/Cas13a system. Thanks to the development of computer science, we simplified many
experimental processes, and save experimental materials and time.

By evaluating crRNAs, we identified the most efficient crRNA; through molecular docking and molecular
dynamics, we revealed the stability of crRNAs, Cas13a proteins, and crRNA/Cas13a complexes, and
clarified the feasibility of fluorescence experiments; and through reaction kinetics simulations, we
explored the effects of individual reaction conditions. In conclusion, physical simulation played a
crucial role in our project, and we believe that this pipeline can be applied to other iGEM teams, which
also uses CRISPR/Cas13a system, to simplify the experimental process and save experimental materials and
time.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part1-fig22.png)
Figure 22 Pipeline of our crRNA engineering

## Part II Cloning and Expression of crRNA

### Brief Introduction

crRNA is the critical part that determines the target oligonucleotides of CRISPR/Cas system. Since crRNA
is a short single-stranded RNA, which has poor stability, it is difficult to store crRNA for long time.
More importantly, the general laboratory protocol for crRNA production is also somewhat deficient. To
produce a short ssRNA, the DNA template of this ssRNA is obtained first, and the template is used to
transcribe RNA. However, primers for short-stranded DNA are difficult to design, so PCR of
short-stranded DNA is difficult to achieve. Also, it is difficult to observe whether PCR is successful
or not for short-stranded DNA by gel electrophoresis. Therefore, we set to design an improved laboratory
protocol for crRNA production.

### Design

Based on SEVA3.1 standard, we designed this laboratory protocol for crRNA production. As a trackless
assembly, Golden Gate Assembly is better than other cloning methods in accuracy, efficiency and
convenience. Therefore, we chose Golden Gate Assembly as our general assembly method. First, we design
two sets of primer--spacer and loop that are corresponded to the crRNA spacer sequence and loop sequence
respectively. The two sets of primers are designed to bind with pveg, a plasmid recommended by iGEM. The
primers of spacer F are designed with guide sequence and restriction site of SapI enzyme. After PCR,
spacer primers will amplify the products carrying guide sequence of crRNA for Golden Gate Assembly. Loop
R primers are designed with loop sequence of crRNA and sequence of T7 promoter. It will amplify the
products carrying loop sequence for Golden Gate assembly after PCR amplification. Both groups of primers
have BsaI enzyme recognition sites at the ends. After enzymatic digestion by BsaI, they both produce
sticky ends. The two PCR products interact with each other depending on the specific sticky ends, and
then they are ligated with T4 ligase. As a result, a plasmid with full crRNA sequence is obtained.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part2-fig23.png)
Figure 23 (A)Primer design schematic (B)Primer sequence description
diagram(C)Plasmid construction flowchart

To get large amount of plasmid with crRNA sequence, the plasmid is transformed into the bacterial
chassis. Then, we will extract the plasmid and digested it with Sapl enzyme to make it linearized. After
purification, T7 RNA polymerase is used for transcription of crRNA in vitro.

### Build and Test

We used primers loop and primers spacer, and used the plasmid pveg as the template to conduct PCR
amplification to get the product for Golden Gate Assembly. We used this procedure for PCR amplification
(98℃ 1min/(98℃ 10s/55℃ 5s/72℃ 1min/kb)x35/72℃ 2min ). After PCR amplification, we followed the
instruction of BsaI enzyme for enzyme digestion, and used T4 ligase to ligate the digested PCR product.

By performing gel electrophoresis, we would like to confirm that the PCR experiment was successful, but
did not obtain the expected results. We conducted several rounds of experiments following the control
principle and adjusted the annealing temperature, but the results were still unsatisfactory despite
repeated efforts. Therefore, we thought that the primer Loop R might be self-complementary, resulting in
its inability to bind to the template plasmid. In order to verify this thought, we analyzed the
secondary structure of the primers by computational modeling, and found that the primer indeed have a
great potential too form multiple stem-loop structures.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part2-fig24.png)
Figure 24 Secondary structure of primers predicted by UNAfold software

Based on the fact that the primer itself may form a loop, we needed to design new loop R primers to
reduce the possibility of self-complementary pairing. After our analysis, we thought that there might be
two factors that lead to inefficient of old loop R primer. On one hand, the primer Loop R was too long,
which led to the formation of complementary pairs on its own. On the other hand, the banding site was
designed to bind to Biobrick prefix, which has the sequence of GCGGCCGC that was easy to form
self-complementary stem-loop region. So we designed two sets of new loop R primers with different
improvements. One shortened the overhanging sequence, and the other changed the banding site. Finally,
we successfully amplified the desired DNA sequence using these new primers, so we choose one of it
continue our experiment. Result is shown in the following figure:

![](https://static.igem.wiki/teams/4611/wiki/engineering-part2-fig25.png)
Figure 25 Gel electrophoresis after PCR of two set primers. (Lane1: DNA marker.
Lane2: guide sequence of crRNA3. Lane3: guide sequence of crRNA2. Lane4: guide sequence of crRNA1.
Lane5: guide sequence of crRNA full length. Lane6: loop region.)

Through gel electrophoresis, we proved that the PCR experiments successfully amplified the DNA sequences
for Golden Gate assembly. After Golden Gate assembly, the plasmid was transferred into the bacterial
chassis for replication, and colony PCR was carried out to select colonies with successful assembly.

![](https://static.igem.wiki/teams/4611/wiki/engineering-part2-fig26.png)
Figure 26 Colony gel electrophoresis after PCR. (Lane1: DNA Marker, Lane2-4:
crRNA full length, Lane5-7: crRNA1, Lane8-10: crRNA2, Lane11-13: crRNA3.)

After that, we used SapI enzyme to linearize the plasmid. Finally, T7 RNA polymerase was used to
transcribe crRNA.

### Learning

This is the first time for us to do molecular cloning. When we designed primers at begining, we only
cared about whether the design of the sticky end was reasonable, and neglected the possibility of
forming self-complementary pairing of the primers. After discovering this problem, we learned the primer
design method and designed new primers. When designing new primers, we paid special attention to the
secondary structure of the primers and the possibility of forming primer dimers between forward and
reverse primers, and we finally succeeded in designing good primers.

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[12] Accelrys. Discovery Studio Visualizer, v20.1.0.19295, BIOVIA, Dassault Systèmes, San Diego, 2020.

[13] RNA structural dynamics as captured by molecular simulations: A comprehensive overview. doi:
10.1021/acs.chemrev.7b00427.a

[14] MATLAB and Simbiology Toolbox Release 2020b, The MathWorks, Inc., Natick, Massachusetts, United
States.

[15] Ramachandran A, Santiago JG. CRISPR Enzyme Kinetics for Molecular Diagnostics. Anal Chem. 2021 May
25;93(20):7456-7464. doi: 10.1021/acs.analchem.1c00525.

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Results

* [Overview](#overview)
* [miR-21-5p](#miR-21-5p)

* [LwaCas13a](#lwaCas13a)
* [crRNA](#crRNA)
* [Cleavage Activity](#cleavage)
* [Lateral Flow Strip](#lateral)
* [Future Plan](#future)

![](https://static.igem.wiki/teams/4611/wiki/dnaimg.png)

# Results

## Overview

The goal of our project is to develop a breast cancer screening kit, BreFast. Our project integrated the
knowledge of computer science, molecular biology and biochemistry. We used bioinformatics approaches to analyze
breast cancer-associated miRNA (microRNA), and selected miR-21-5p as the best biomarker for detection by
BreFast. Subsequently, we verified the expression of miR-21-5p in breast cancer cells by qRT-PCR, and explored
the function of miR-21-5p by wound healing and transwell assays. Furthermore, we designed a crRNA, an essential
component of CRISPR/Cas13a system, to target miR-21-5p, and made an engineered CRISPR/Cas13a system that could
be activated by miR-21-5p. The cleavage efficiency of the CRISPR/Cas13a system was investigated by fluorescence
reaction and kinetics simulation. Finally, the efficacy of the CRISPR/Cas13a system on detecting high levels of
miR-21-5p was demonstrated with lateral flow strips. In summary, we have combined CRISPR/Cas13a system and
lateral flow strips into a convenient and reliable kit to detect miRNA associated with breast cancer (Figure 1).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig1-workflow.png)
Figure 1 The workflow of the Results section.

## miR-21-5p

### Bioinformatics Analysis

During the progression of breast cancer, the expression levels of miRNAs are changed. Some of these miRNAs are
closely associated with breast cancer and can be used as potential biomarkers for detection of breast cancer. We
focused on utilizing miRNAs that show increased expression in breast cancer as biomarkers for detection by
BreFast.

### Differential expression analysis

To identify miRNA with significant changed expression in breast cancer patients, we analyzed TCGA-BRCA dataset
in TCGA database and GSE59247 dataset in GEO database, and selected miRNA with thresholds of |logFC|>1 and
PValue<0.05. 118 up-regulated and 98 down-regulated miRNA were identified from GSE59247 dataset, and 226
up-regulated and 107 down-regulated miRNA were identified from TCGA-BRCA dataset.

| Dataset | Upregulation | Downregulation |
| --- | --- | --- |
| GSE59247 | 118 | 98 |
| TCGA-BRCA | 226 | 107 |

Table 1 Number of miRNA with significantly changed expression in breast cancer.

Venn diagram was plotted to show the total numbers of miRNA with increased expression in breast cancer from
GSE59247 dataset and TCGA-BRCA dataset, and the intersection of the Venn diagram shows that 9 miRNA were
identified from both datasets.

![](https://static.igem.wiki/teams/4611/wiki/venn.png)
Figure 2 Venn diagram showing the number of up-regulated miRNA from GSE59247 and
TCGA-BRCA datasets.

Volcano plots were generated to show the distribution of the miRNA on fold change and p-value from both
GSE59247 dataset and TCGA-BRCA dataset.

![](https://static.igem.wiki/teams/4611/wiki/volcano-plot-gse59247.png)
Figure 3 Volcano plot of miRNA from GSE59247 dataset.

![](https://static.igem.wiki/teams/4611/wiki/volcano-plot.png)
Figure 4 Volcano plot of miRNA from TCGA-BRCA dataset.

### ROC Curve

To further refine the selection of target miRNAs for BreFast detection, we plotted ROC curves for the 9 miRNAs
identified from differential expression analysis. Since the sample size in the GSE59247 dataset was relatively
small, we used the TCGA-BRCA dataset to generate the ROC curves, as shown in the figure below.

![](https://static.igem.wiki/teams/4611/wiki/roc-mir.svg)
Figure 5 ROC curves generated from TCGA-BRCA data.

The area under the curve is an important index of the ROC curve. The larger the area under the curve, the
higher the sensitivity and specificity of the corresponding miRNA. AUC>0.9 was used as a criterion for
selection, and miR-21-5p, miR-183-5p and miR-96-5p were selected for subsequent analysis.

### Feature importance analysis

Feature importance analysis is a machine learning method to analyze how important of a feature to a certain
characteristics (label). We treated each miRNA as a feature, and labeled samples as positive or negative based
on whether they were from cancer patients or normal persons, thus transforming the problem of selecting optimal
cancer-associated miRNA into a machine learning feature importance issue. Datasets were divided into training
data and testing data in an 8:2 ratio. We used grid search and five-fold cross-validation to find the optimal
parameters (achieving an accuracy of 0.98924 in five-fold cross-validation). The binary classification task was
performed using a random forest model, and the accuracy reached 1 on the testing dataset. Feature importance was
calculated based on the Gini index, and the top 10 ranked miRNAs were shown below.

![](https://static.igem.wiki/teams/4611/wiki/rf10-importance.svg)
Figure 6 The feature importance of the top ten miRNAs analyzed by random forest, where
the upregulated and downregulated miRNAs have been labeled based on the results of differential expression
analysis of TCGA data.

While tree models, such as random forest, can reflect the classification value of a feature (each miRNA)
concerning sample (each patient) classification, they usually do not consider the interactions between different
features. Considering the possibility of strong interactions among different miRNAs[[1]](#ref1), our team has employed
SHAP (SHapley Additive explanations) analysis, a methodology based on cooperative game theory, to conduct
further analysis on the feature importance derived from the Random Forest model. The top 10 miRNA with high SHAP
values were shown below, and miR-21-5p ranked first.

![](https://static.igem.wiki/teams/4611/wiki/shapnew-10.png)
Figure 7 SHAP value analysis of the feature importance derived from Random Forest,
where the upregulated and downregulated miRNAs have been labeled based on the results of differential expression
analysis of TCGA data.

In summary, our bioinformatic analysis demonstrated that miR-21-5p is the optimal breast cancer-associated
miRNA for detection.

### Survival Analysis

Differential expression analysis and feature importance analysis have been performed to identify miR-21-5p as
an optimal target for breast cancer screening. Next, we would like to know whether miR-21-5p could truly affect
the progression of breast cancer. Therefore, we did survival analysis of miR-21-5p with Kaplan-Meyer curves. The
data of Metabric and TCGA were used to draw the survival curve of miR-21-5p.

![](https://static.igem.wiki/teams/4611/wiki/sur.png)
Figure 8 The survival curves for miR-21-5p, where Figure (a) represents data from the
Metabric database, and Figure (b) represents data from the TCGA dataset.

The results of survival analysis demonstrated that the survival time of breast cancer patients with high
expression of miR-21-5p was shorter than that of patients with low expression, and there was a statistically
significant correlation between miR-21-5p and the survival time of breast cancer patients.

### Literature Research

In addition to bioinformatics analysis, we also read literatures to confirm that miR-21-5p is the best miRNA
candidate to be used as the optimal breast cancer-associated biomarkers for screening by Brefast.

A comprehensive review on International Journal of Molecular Sciences summarized 75 independent clinical
studies. 20 miRNAs were suggested to be breast cancer associated biomarkers, which were supported by at least
two independent studies in the same biological specimen (serum, plasma, or whole blood). 8 out 20 of them
demonstrated similar tendency of change in expression: miR-21 (12 up versus 1 down), miR-155 (14 up versus 1
down), miR-10b (5 up, only serum), miR-373 (3 up, only serum), miR-652 (3 down, only serum), miR-425 (2 down,
only serum), miR-29a (2 up, only serum), and miR-148b (2 down, only serum). Others miRNA, such as miR145
displayed different tendency of change in expression[[2]](#ref2).
As a result, miR-21, miR-155, miR-10b were supported by
multiple studies as highly associated with breast cancer[[3-4]](#ref4). It is worthy to note that the predominant form
of miR-21 is miR-21-5p.

![](https://static.igem.wiki/teams/4611/wiki/research.png)
Figure 9 Pyramidal graph of the direction of miRNA expression (microRNA concentration
in breast cancer cases versus controls) by type of specimens (only microRNAs that were analysed in two or more
independent studies). (A) whole blood; (B) plasma; (C) all specimens; (D) serum.

In summary, both our own bioinformatic analysis and studies from multiple research groups demonstrated that
miR-21-5p is the best miRNA biomarker for detection of breast cancer.

### Experimental Investigation

Studies have shown that miR-21-5p can promote peritoneal metastasis in gastric cancer[[5]](#ref5). Therefore, we not
only verified the expression of miR-21-5p in breast cancer cells, but also investigated the effects of miR-21-5p
on the migration and invasion of breast cancer cells.

### miR-21-5p is highly expressed in breast cancer cells

To determine the expression of miR-21-5p in breast cancer cells, we did qRT-PCR to determine the relative
expression of miR-21-5p in the breast epithelial cell line MCF10A and the breast cancer cell line MCF7, which
showed that the expression of miR-21-5p in breast cancer cells was higher than that in normal breast cells.
(Figure 10).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig10-qrtpcr.png)
Figure 10 Expression of miR-21-5p in MCF10A and MCF7 cells (A) qRT-PCR primer sequence
for miR-21-5p ; (B) Raw data of qRT-PCR ;
(C) miR-21-5p was highly expressed in breast cancer cells (p<0.001).

### miR-21-5p enhances the migration and invasion abilities of breast cancer cells

The effect of miR-21-5p on the invasion capability of breast cancer cells was investigated by Transwell assay
(Figure 11). miR-21-5p was overexpressed in breast cancer cell line MDA-MB-231 by transfection. The invasion
ability of MDA-MB-231 transfected with miR-21-5p were significantly higher than that of cells transfected with
control RNA, demonstrating that miR-21-5p increased the invasiveness of MDA-MB-231 cells (Figure 12).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig11-principle-of-transwell.png)
Figure 11 Schematic diagram of Transwell invasion assay.

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig12-experiments-of-transwell.png)
Figure 12 (A) Penetration of cells in the compartment; (B) Cell count results (n=5, p
<0.001) .

To verify the effects of miR-21-5p on the migration of breast cancer cells, wound healing experiment was
conducted. MDA-MB-231 cells were transfected with miR-21-5p or control RNA, and the migration of cells were
recorded at 0h, 24h and 48h. The wound area of the MDA-MB-231 cells tranfected with miR-21-5p group was
significantly smaller than that of MDA-MB-231 cells tranfected with control RNA, indicating that miR-21-5p
enhanced the migration ability of MDA-MB-231 cells (Figure 13).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig13-wound-healing.png)
Figure 13 Wound healing assays of miR-21-5p transfected cells (A) Wound healing
experimental images taken at 0h, 24h and 48h; (B) Relative migration level (p<0.001).

In summary, the expression level of miR-21-5p in breast cancer cell MCF7 is much higher than that in breast
epithelial cell MCF10A, and increased expression of miR-21-5p enhanced the migration and invasion capability of
breast cancer cells. Together, these results demonstrated that increased expression of miR-21-5p had significant
effects on breast cancer cells.

## LwaCas13a

### Construction of pSB1C3-His-SUMO-LwaCas13a plasmid

Since we decided to use CRISPR/Cas13a system to detect miR-21-5p, we constructed a pSB1C3-His-SUMO-LwaCas13a
plasmid for expression of LwaCas13a. The schematic presentation of pSB1C3-His-SUMO-LwaCas13a plasmid was shown
below (Figure 14C). LacI operon was used for the induced expression of LwaCas13a[[6]](#ref6), and a SUMO tag was added to
the N-terminal of LwaCas13a protein to increase the solubility[[7]](#ref7). To purify the LwaCas13a protein, a 6×His tag
was also added to the N-terminal of LwaCas13a protein[[8]](#ref8).

The pSB1C3-His-SUMO-LwaCas13a plasmid was constructed using PCR amplification and Golden gate assembly
technology which is an excellent methods of DNA recombination capture the principal advantages of the BioBrick
standard[[9]](#ref9). Fragment containing SUMO-LwaCas13a was
amplified from plasmid pC013-TwinStrep-SUMO-huLwCas13a by
primers Cas13a F and Cas13a R. Fragment containing LacI operon was amplified from the plasmid
pC013-TwinStrep-SUMO-huLwCas13a by primers LacI F and LacI R. Fragment containing pSB1C3 backbone sequence was
amplified from the plasmid Pveg by primers Backbone F and Backbone R (Figure 14A,B).

![](https://static.igem.wiki/teams/4611/wiki/results-re-1.png)

Figure 14 Cloning strategy for pSB1C3-His-SUMO-LwaCas13a (A) Illustration of Pveg plasmid. (B) Illustration of
pC013-Twinstrep-SUMO-huLwCas13a. (C) Illustration of pSB1C3-His-SUMO-LwaCas13a plasmid.

The PCR products were analyzed by electrophoresis with 1% agarose gel, which showed that the pSB1C3 backbone
fragment (2086 bp), LacI operon fragment (1525 bp) and SUMO-LwaCas13a fragment (4227 bp) were amplified
successfully (Figure 15).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig15-lwa-laci-backbone.png)

Figure 15 Gel electrophoresis of the PCR product for Golden Gate assembly of pSB1C3-His-SUMO-LwaCas13a plasmid.
Lane1: DNA marker. Lane2-Lane3: LwaCas13a. Lane4-Lane5: LacI operating system. Lane6-Lane7: pSB1C3 Backbone.

These fragments were assembled to generate pSB1C3-His-SUMO-LwaCas13a by Golden Gate assembly, and the assembled
products were transformed into Trans1-T1 competent bacterium (Figure 16).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig16-colonies.png)

Figure 16 Colonies that grow after transformation.

To select colonies with successful assembly, colony PCR was performed. PCR Product 1 was amplified by Primer VR
and Primer CHECK F that covered the junction between fragment SUMO-LwaCas13a and fragment LacI operon and the
junction between fragment LacI operon and fragment pSB1C3 backbone (3000 bp). PCR Product 2 amplified by Primer
CHECK R and Primer VF2 covered the junction between fragment pSB1C3 backbone and fragment SUMO-LwaCas13a (3050
bp). The two products were analyzed by agarose gel electrophoresis (Figure 17). Gel electrophoresis results
demonstrated that the Golden Gate assembly was successful.

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig17-pcr-product12.png)

Figure 17 Gel electrophoresis of the colony PCR product. (A) Illustration of PCR Product 1. (B) Illustration of
PCR Product 2. (C) Lane1: Marker. Lane2-Lane4: The product amplified by Primer VR and Primer CHECK F.
Lane5-Lane7: The product amplified by Primer CHECK R and Primer VF2.

For further verification, we carried out the endonuclease digestion. The restriction endonuclease EcoRI was
used for single cut, and the restriction endonuclease EcoRI and Spel were used for double cut, followed by 1%
agarose gel electrophoresis. Two bands (5712bp and 2068bp) were produced by digestion with both EcoRI and SpeI,
while one band (7780bp) was produced by digestion with EcoRI only. The electrophoresis results confirmed
successful assembly of pSB1C3-His-SUMO-LwaCas13a (Figure 18).

![](https://static.igem.wiki/teams/4611/wiki/result-re-18.png)

Figure 18 Plasmid pSB1C3-His-SUMO-LwaCas13a was verified by endonuclease digestion (A) Digestion using EcoRI and
SpeI results in two bands. One is 5712 bp, and the other is 2068 bp.; (B) Digestion using EcoRI enzymes results
in a band with a length of 7780bp.
(C) Lane1: Marker. Lane2: Plasmid was digested with EcoRI and SpeI. Lane3: Plasmid was digested with EcoRI.

### Expression and purification of LwaCas13a protein

5 mL LB medium and 1 mL of bacteria-glycerol mixture were added into 15 mL tubes, and cultured on a shaker for
4 h until the bacteria culture were cloudy. They were then transferred into 1L flasks, and supplemented with
250mL of medium and 100 uL of CmR solution (50mg/mL), and incubated with shaking at 220rpm for 8 h. 1 mL of
bacterial solution was collected for later use. Subsequently, 200 μL of 0.5M IPTG was added to each vial to
induce protein expression and incubated overnight at 28°C. The cell lysate from bacteria before and after IPTG
induction were analyzed by SDS-PAGE gel electrophoresis, and the results demonstrated that the expression of
LwaCas13a was induced successfully (Figure 19).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig19-uninduced-induced.png)

Figure 19 Results of SDS-PAGE gel analysis of cell lysate from bacteria before and after IPTG induction. Lane1:
Protein marker. Lane2-Lane6: Uninduced cell lysate from bacteria. Lane7-Lane11: IPTG-induced cell lysate from
bacteria.

Bacteria were lysed by ultrasound, and centrifuged to separate supernatant and pellet. The soluble proteins
were present in the supernatant, and the insoluble proteins were in the pellet. Both supernatant and pellet were
analyzed by SDS-PAGE (Figure 20). SDS-PAGE gel analysis showed that the amount of LwaCas13a protein in the
pellet was very little, and most of LwaCas13a protein was in the supernatant, indicating that SUMO tagged
LwaCas13a was soluble.

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig20-supernatant-pellet.png)

Figure 20 SDS-PAGE detection of lysed supernatant of cells with lysis pellet of cells. Lane1: Protein marker.
Lane2-Lane6: Lysed supernatant of cells. Lane7-Lane11: Lysis pellet of cells.

Western blot examined uninduced bacteria lysed by RIPA, induced bacteria lysed by RIPA, and induced bacteria
lysed by ultrasonic, and showed that SUMO-LwaCas13a protein was expressed successfully (Figure 21).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig21-western-blot.png)

Figure 21 Western Blot with LwaCas13a antibody. Lane1: Protein marker. Lane2-Lane3: Uninduced bacteria for
RIPA-lysed. Lane4-Lane5: Induced bacteria for RIPA-lysis. Lane6-Lane7: Induced bacteria for ultrasonic lysis.

The final step of LwaCas13a production was to use His-Tag Protein Purification Kit with IDA-Ni Magnetic Agarose
Beads for purification[[10]](#ref10) and SUMO protease ULP1 to
digest SUMO-LwaCas13a. We used SDS-PAGE gel electrophoresis
to analyze the purification results (Figure 22).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig22-purification.png)

Figure 22 Results of purification of LwaCas13a protein. Lane1:Protein marker. Lane2-Lane3: Uninduced bacteria
for RIPA-lysed. Lane4-Lane5: Induced bacteria for RIPA-lysis. Lane6-Lane7: Induced bacteria for ultrasonic
lysis. Lane8-Lane9: Purified LwaCas13a protein.

## crRNA

### Construction of pSB1C3-crRNA plasmid

crRNA consists of two parts, a repeat part that contains a loop region and a detection part that contains a
linear guide sequence to recognize target RNA[[11]](#ref11).
Studies have shown that the length of crRNA guide sequence
affects its activity. When the length of crRNA guide sequence is less than 20nt, its activity is significantly
decreased[[12]](#ref12). We designed four crRNA guide sequences
that were complementary to miR-21-5p, the length of which
ranges from 20nt to 22nt. The secondary structures of these four crRNA were as below (Figure 23).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig23-four-crrnas.png)

Figure 23 The secondary structure of crRNA1 (A), crRNA2 (B), crRNA3 (C), crRNA full length (D).

crRNA sequences were cloned into Pveg plasmid with PCR. Most loop region of crRNA was synthesized as an
overhang part of primer loop R, which was introduced into the fragment 1 by PCR with primer loop R and primer
loop F. The rest of loop region and the guide sequence of crRNA was synthesized as an overhang part of primer
spacer F, which was introduced into the fragment 2 by PCR with primer spacer F and primer spacer R. Fragment 1
and 2 were assembled into a plasmid with full length crRNA by Golden Gate assembly. (Figure 24).

![](https://static.igem.wiki/teams/4611/wiki/results-re-24.png)

Figure 24 cloning of Pveg plasmid with full-length crRNA (A) Schematic presentation of cloning strategy of Pveg
plasmid with full-length crRNA sequences. (B) Primer sequences for cloning of crRNA into Pveg.

PCR was carried out, followed by gel electrophoresis, which demonstrated that fragments containing loop region
and guide sequences were amplified successfully (Figure 25).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig25-loop-spacer.png)

Figure 25 Gel electrophoresis of PCR products for Pveg plasmid with full-length crRNA. Lane1: DNA marker. Lane2:
Guide sequence of crRNA1. Lane3: Guide sequence of crRNA2. Lane4: Guide sequence of crRNA3. Lane5: Guide
sequence of crRNA full length. Lane6: Loop region.

The assembled product was transformed into Trans1-T1 competent cells, and then colony PCR was performed to
select the assembled product. PCR product amplified by primer VR and VF2 covered the junction between the
fragment 1 and the fragment 2 (403bp/401bp). Agarose gel electrophoresis showed that the assembly of the two
fragments was successful (Figure 26).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig26-4crrna-pcr.png)

Figure 26 PCR verified that the plasmid has been successfully assembled. （A）Schematic representation of PCR
product.（B） Colony gel electrophoresis after PCR. Lane1: DNA Marker. Lane2-Lane4: crRNA full length.
Lane5-Lane7: crRNA1. Lane8-Lane10: crRNA2. Lane11-Lane13: crRNA3.

### In vitro transcription and purification of crRNA

Prior to in vitro transcription of crRNA, Pveg plasmids with full crRNA sequences were linearized by SapI
digestion. The linearized plasmids were used for in vitro transcription using the T7 High Yield RNA Synthesis
Kit from YEASEN Biotechnology Co., Ltd. The reaction system was shown in Table 2, and the mixture was incubate
at 37 °C for 5h to allow the transcription reaction to proceed sufficiently.

| Reagent | Quantily μL(for 20μL mix) |
| --- | --- |
| T7 RNA Polymerase Mix | 2μL |
| NTP Mix (100mM) | 8μL |
| T7 Reaction Buffer (10×) | 2μL |
| RNase Inhibitor | 0.5μL |
| Template DNA | 1μg |
| RNase-free water | to 20μL |
| Total | 20μL |

Table 2 In vitro transcription reaction system.

Transcription products were recovered using the RNA Purification Kit from Tiangen. RK solution and absolute
ethanol were added to the product, and mixed well. The mixture was added to the adsorption column and
centrifuged. The column was washed with deproteinization solution and rinse solution, and elution buffer was
added to obtain purified crRNA. Agarose gel electrophoresis showed that all four crRNAs were successfully
transcripted and purified (Figure 27).

![](https://static.igem.wiki/teams/4611/wiki/results-fig-27-neww.png)

Figure 27 Gel electrophoresis plot of in vitro transcription of crRNA. Lane1: crRNA full length. Lane2:
crRNA1.Lane3: crRNA2. Lane4: crRNA3.

In summary, we have made two essential components of CRISPR/Cas13a system: crRNA and LwaCas13a protein.

## Cleavage activity

Since fluorescence reaction has the characteristics of high sensitivity and quantification[[13]](#ref13), the cleavage
activity of our CRISPR/Cas13a system was first analyzed by fluorescence detection. We used a reporter RNA
designed based on the principle of fluorescence resonance energy transfer (FRET)[[14]](#ref14), with a FAM group at one
end and a black hole quencher group (BHQ1) at the other end, connected by 6 U in between. Activated
CRISPR/Cas13a system could cleave the reporter at 6 U, and release FAM group from the quencher group. As a
result, fluorescence from the released FAM group could be detected. If the crRNA that we designed can form a
functional complex with LwaCas13a, the cleavage activity of this complex could be activated by miR-21-5p, which
could cleave the reporter RNA to release FAM group for fluorescence detection.

Four crRNA named crRNA1, crRNA2, crRNA3 and crRNA full length, were used in the experiment. The results showed
that the CRISPR reaction system assembled by crRNA full length and Cas13a protein had the strongest cleavage
activity, followed by crRNA3, while crRNA2 and crRNA3 had the weakest activity (Figure 28).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig28-find-crrna.png)

Figure 28 Fluorescence intensity measured at different time with different crRNAs.

Next, for the fluorescence reaction, the concentrations of crRNA, LwaCas13a, and reporter RNA were fixed at 20
nM, 100 nM and 100 nM respectively, and the concentration of miR-21-5p was gradually increased from 0 nM to 100
nM. The experimental results showed that the fluorescence intensity increased as the concentration of the miRNA
increased (Figure 29).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig29-find-mirna.png)

Figure 29 Fluorescence intensity measured at different times with different concentrations of miRNA.

In addition, we also set up a series of reporter concentration at 0 nM, 100 nM, 125 nM, 250 nM and 500 nM for
experiments. After we plotted the double inverse of fluorescence intensity and reporter RNA concentration, the
experimental results were found to be consistent with Michaelis-Menten equation[[15]](#ref15), which was in line with our
expectations. (Figure 30).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig30-find-reporter.png)

Figure 30 (A) Fluorescence intensity measured at different times with different concentrations of reporter RNA.
(B) The fitting curve of Lineweaver-Burk.

## Lateral flow strip

After completing a series of fluorescence experiments that validated the cleavage activity of our CRISPR/Cas13a
system, we began to simplify the method that was used to detect the cleavage activity of the CRISPR/Cas13a
system with lateral flow strips.

To facilitate the lateral flow strip to display the results, the BHQ1 modification of our RNA reporter 3' was
changed to biotin, while the sequence remained unchanged, still 6 U. In the experiment of lateral flow strip
test, we adopted the detection method of "line elimination". On the probe pad of the lateral flow strip, there
is streptavidin-labeled colloidal gold, which binds to the biotin at the RNA reporter. The position of the Test
line (T line) at the lateral flow strip is planted with FAM antibodies, which binds to FAM group of the RNA
reporter. In the case that the RNA reporter is uncleaved, the RNA reporter forms a complex with the
streptavidin-labeled colloidal gold through the interaction between biotin and streptavidin. As this complex
flows to the T line, it is captured by the FAM antibody that binds to the FAM group of the RNA reporter. As a
result, the colloidal gold accumulates at the T line, which can be observed by naked eyes. In addition, the
gold-labeled mouse antibodies are included in the lateral flow strip detection system as controls. They are
captured by the sheep-anti mouse antibodies at the C line position to show the samples are added successfully on
the lateral flow strip.

In the case that miR-21-5p is not expressed, the CRISPR/Cas13a system is not activated, and the RNA reporter is
not cleaved. The uncleaved RNA reporter with streptavidin-labeled colloidal gold is captured by the FAM antibody
at the T-line position, and the gold labeled mouse antibodies is captured by the sheep-anti mouse antibodies at
the C line position. As a result, the lateral flow strip shows both C line and T line, indicating negative
result (Figure 31).

In the case that miR-21-5p is expressed low, the CRISPR/Cas13a system is not fully activated, the reporter is
not completely cleaved. The uncleaved RNA reporter with streptavidin-labeled colloidal gold can still be
captured by the FAM antibody at the T-line position, while the cleaved RNA reporter can not link
streptavidin-labeled colloidal gold with FAM antibody anymore. As a result, the lateral flow strip shows C line
and a weak T line, indicating negative result.

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig31-high-or-low-or-no.png)

Figure 31 Principle of our lateral flow strips.

Next, the lateral flow strip was tested. The CRISPR/Cas13a system was composed of working solution and sample
solution, and they were mixed for reaction. The composition of the working solution included 10×Cas13a Reaction
Buffer (the recipe was shown in Table 3), RNase Inhibitor, LwaCas13a protein, crRNA-Full length and RNase-free
water, and it has a total volume of 10μL (Table 4). The composition of the sample solution includes miR-21-5p,
reporter and so on, its volume was also 10μL (Table 5). The concentration of miRNA in the reaction system was
changed to test the effects of strips.

| Component | Concentration |
| --- | --- |
| Tris-HCl | 10mM |
| KCl | 50nM |
| MgCl2 | 1.5mM |
| pH | 8.3 |

Table 3 Components of Cas13a Reaction Buffer (10×).

| Reagent | Quantily μL (for 10μL mix) |
| --- | --- |
| Cas13a Reaction Buffer (10×) | 2μL |
| RNase Inhibitor | 1μL |
| LwaCas13a (2μM) | 1μL |
| crRNA full length (0.4μM) | 1μL |
| RNase-free water | 5μL |
| Total | 10μL |

Table 4 Components of the working solution.

| Reagent | Quantily μL (for 10μL mix) |
| --- | --- |
| Cas13a Reaction Buffer (10×) | 2μL |
| RNase Inhibitor | 1μL |
| miR-21-5p (1μM) | xμL |
| Reporter (2μM) | 2.4μL |
| RNase-free water | to 10μL |
| Total | 10μL |

Table 5 Components of the sample solution.

After mixing the working solution and the sample solution, the reaction solution was obtained (Figure 32), and
the reaction droplets were added to the sample pad of the test strip, the result was recorded in 3 minutes.

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig32-real-experiment.png)

Figure 32 The reaction solution with different concentration of miRNA.

The results of the lateral flow strip detection were as in Figure 33. The C lines of the five test strips were
clearly visible, indicating that these test strips were good to use. In the case that the miRNA concentration in
the reaction mix was 0 nM, T line was obvious. While the miRNA concentration increased to 5 nM, T line was
decreased. A further increase of miRNA concentration to 25 nM made the T line almost disappeared. In the case
that the miRNA concentration increased to 50 and 100 nM, T line completely disappeared. These results
demonstrated that the lateral flow worked as we expected, which could distinguish low and high concentration of
miR-21-5p easily (Figure 33).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig33-lateral-flow-strips-concentrations.png)

Figure 33 High and low concentrations of miRNA were distinguished successfully by lateral flow strip.

Subsequently, the outer shell of the lateral flow strip was manufactured to make it easy to carry and work with
(Figure 34-35).

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig34-kit.png)

Figure 34 Diagram of the lateral flow strip and its outer shell.

![](https://static.igem.wiki/teams/4611/wiki/exp-results-fig35-out-shell.png)

Figure 35 The outer shell of the lateral flow strip facilitates sample loading and result interpretation.

## Future plan

In this year's project, we engineered a CRISPR/Cas13a system to detect miR-21-5p for breast cancer screening.
In the future, we set to make a breast cancer screening kit that can simultaneously detect multiple breast
cancer-associated miRNAs, such as miR-21-5p, miR-3923, miR-885-5p and miR-142-5p, so as to achieve higher
sensitivity and accuracy of breast cancer detection. In addition, we would like to apply our CRISPR/Cas13a
system to screening of other types of cancers.

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