Team:Tsinghua/Pro-lac

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Pro-LAC

Pro-LAC

• Design
  • Chemical labelling of RNA
  • RNA labelling
  • Read-out
• Result
  • Expression and purification
  • Transcription of RNA and non-specific labelling
  • Specific labelling
• Conclusion and Perspective

Design

IBD is currently diagnosed by clinical, endoscopic, radiologic, and histologic criteria^[1]. A molecular diagnosis method will facilitate the early diagnosis and clinical intervention of IBD, which might relief the pain caused by IBD for many patients. We here propose an RNA biomarker-based molecular diagnosis method named Proximity-labelling Assisted and CRISPR-Cas Inspired RNA Targeting (Pro-LAC) system. Under the guidance of gRNA and blue light, miniSOG-containing fusion protein can label the guanosine on biomarker RNA with biotin-NH₂ probes. Combined with lateral flow assay, we are developing a household, easy-to-use, and rapid early diagnosis method not only for IBD but also for an universal RNA detection system in vitro.

Our diagnosis method can be separated into three different parts:

1. Chemical labelling of RNA
2. Specific labelling of RNA using CIRT system
3. Read-out using lateral flow assay

Chemical labelling of RNA

We first considered how can we detect the presence of RNA easily. We came up with the idea of covalently modify target RNA with biotin or fluorophore-containing probes so that we can detect the target RNA by detecting color (Streptavidin-HRP catalyzed substrate color development or fluorescence signal from fluorophore). However, due to the chemically inert property of nucleobases, it is very challenging to covalently label target RNA for further detection.

Inspired by two recent literature^[2][3], we decided to take an enzymatic approach to achieve the RNA labelling. We used a protein called miniSOG, an engineered flavoprotein originally derived from A. thaliana phototropin 2, to generate singlet oxygen ¹O₂ via type II photoreaction under blue light illumination^[4]. The singlet oxygen will oxidize guanosine into an intermediate, which could be attacked by primary amine to form covalent bond with NH₂-containing probes. In this way, we are able to label RNA molecule with biotin or fluorophore for further detection. Considering the accessibility of the probes, we used biotin-NH₂ probes for our labelling assay.

Fig.1 The labelling principle of RNA using miniSOG^[2]

Specific labelling of RNA using CIRT system

We next thought about the specific labelling of our RNA of interest since many different RNAs could exist in clinical samples. There are several RNA targeting systems, including RNA-targeting Cas9 system^[5] (developed by Doudna Lab at UC Berkeley), Cas13 system^[6]-[8] (developed by Zhang Lab at Broad Institute of Harvard and MIT), and CIRT system^[9] (developed by Dickinson Lab at UChicago).

Fig.2 Currently available RNA targeting systems^[5]-[9]

We chose CIRTS (CRISPR-Cas-Inspired RNA Targeting System) because of its flexibility. To be more specific, CIRTS system is composed of three parts: ssRNA binding protein, RNA hairpin binding proteins, and effector protein. While the first two proteins bind to gRNA and mediate the formation of target RNA-CIRTS complex, the effector protein, in our case, will be miniSOG that catalyzes the labelling of target RNA. We noticed that there are several choices for RNA hairpin binding protein and ssRNA binding protein (Table 1), which means our fusion protein is not limited to one option. Instead, by using a combination of different RNA binding proteins, we can construct a fusion protein library that share the same effector protein miniSOG while having different RNA binding protein. In this way, we might be able to fine-tune the system properties to achieve better labelling effect.

Table 1. Protein choices for RNA hairpin binding protein and ssRNA binding protein

FUNCTION	PROTEIN1	PROTEIN2	PROTEIN3
RNA HAIRPIN	TBP	SLBP	--
SSRNA BINDING	ORF5	HBEGF	β-defensin 3

In short, our Pro-LAC system will use the CIRTS system (with gRNA) to target the biomarker miRNA, and by adding biotin-NH₂ probe into the system and adding blue light, miniSOG will catalyze the labelling of target miRNA while untouching other RNA in the system. In this way, we can achieve specific labelling of our target RNA. The labelling progress is shown at Fig.3.

Fig.3 The progress of Pro-LAC specific labelling miRNA

Read-out using lateral flow assay

After specific labelling of the biomarker miRNA with biotin, an easy read-out is required for actual application. We have designed the read-out system based on lateral flow assay:

Fig.4 Proposed read-out device using lateral flow assay

The principle underlying this read-out system is simple. After applying the post-reaction mixture to the system, the proteins and RNAs will diffuse from sample pad to the absorption pad. If the target miRNA is presented in the system, it will bind to the fusion protein and be labelled by the probes. So when passing the test line (on which anti-TBP antibody is immobilized), the complex will be trapped by antibodies while other components will continue to diffuse. The remaining probes will be absorbed by the streptavidin on control line. After the diffusion, streptavidin-HRP will be used to bind to biotin and substrates will be applied for color development. By visualizing the color of test line and control line, we could tell whether our target miRNA exists or not in the system:
Scenario 1: Both test line and control line change color: positive result. This indicates that the target miRNA exists in the system and the result is valid.

Fig.5 Lateral Flow test model

Scenario 2: Control line changes color while test line does not: negative result. This indicates that target miRNA does not exist in the system or the amount is below detection limit and the result is valid.
Scenario 3: Control line does not change color. This indicates the system is not working and therefore the result is invalid.

Modeling

In order to make our pro-LAC have a greater application prospect, we also build a mathematical model. The stability and effectiveness of our detection system are explained from two aspects of protein degradation and lateral flow. Check out the modelling page for specific details.

Result

The detection method we proposed for this project is named Proximity-labelling Assisted and CRISPR-Cas Inspired RNA Targeting (Pro-LAC) system. We have successfully achieved the specific labelling of RNA using our system and will examine the lateral flow read-out in the future due to limited time.

Expression and purification of fusion protein

We first expressed and purified the protein we used to label RNA. His-tagged miniSOG plasmid is a gift from Prof. Peng Zou’s lab at Peking University, and we ordered the plasmid encodes miniSOG, TBP, and ORF5 connected by glycine/serine linker (with His tag). We then transform them separately into commercial BL21 chemocompetent cells and plates on ampicillin agar plates, followed by picking single colony, culturing in liquid LB, and inducing using IPTG (please see protocol for details). Bacteria were then collected, lysed by ultrasound, and proteins would undergo affinity purification using Ni-NTA or Co-NTA beads. Purified proteins were subjected to SDS-PAGE or blue light illumination, which proved successful expression of both the miniSOG and the fusion protein (Fig.6-8).

Fig.6 SDS-PAGE result of miniSOG

Fig.7 SDS-PAGE result of fusion protein

Fig.8 Fusion protein fraction under blue light illumination

In vitro transcription of RNA and non-specific labelling

We then turned to RNA synthesis. Considering the accessibility and flexibility, we chose in vitro transcription to prepare RNA instead of ordering from companies. We chose a T7 promoter-containing DNA which our lab already had as template since the labelling using miniSOG only requires the presence of guanosine and is therefore sequence-independent. We will use the exact miRNA biomarker sequence as target RNA in future to further validate our system, but currently we use this RNA sequence as a demo to illustrate that our system can function properly and universally. We first synthesized our RNA in vitro according to IVT protocol, incubating our DNA template with T7 RNA polymerase, NTPs and RNase inhibitor at room temperature for ten hours (Fig.9). Then we used urea-PAGE for RNA gel electrophoresis to prove that whole length of RNA had been successfully transcribed (Fig.10). Acquired RNA was then loaded to nanodrop to measure its concentration and purity parameters, as shown in Table 2.

Fig. 9 Sequence of DNA template used in in vitro transcription for miRNA (A) and gRNA (B)

Fig. 10 Urea-PAGE of IVT product

Table 2 RNA concentration and other parameters

RNA	Concentration	A260/A280	A260/A230
miF RNA	8187.5ng/ul	1.93	2.32

After synthesizing the RNA, we tested the non-specific labelling of RNA using miniSOG alone. Our results showed that, under blue light illumination, miniSOG can catalyze labelling of RNA with biotin-NH₂ probes. After the reaction, RNA Clean & Concentrator^TM-25 (ZYMO, Catalog #R1017, R1018) was used to get rid of excess biotin-NH₂ probes and dot-plot assay was used for color development using streptavidin-HRP followed by substrates (see protocol for experimental details) (Fig.11-12). This result is in line with the results reported by previous literature^[10], and we established a relatively fixed work flow for labelling assay during these initial experiments.

Fig.11 miniSOG Labelling activity test

Fig.12 Non-specific labelling of RNA using miniSOG with RNA concentration gradient

Specific labelling of RNA

With the non-specific labelling data in hand, we moved to the specific labelling of our target RNA. We designed our gRNA based utilizing an online tool^[11] to include appropriate hairpin structure and avoid other unnecessary secondary structures. gRNA was also synthesized using in vitro transcription from DNA template (ordered as primers, already shown in Fig.6).

Fig.13 Secondary structure prediction using online tools

Worrying that the fusion of miniSOG to TBP and ORF5 could possibly cause the loss of its catalyzing function, we first tested the non-specific labelling using fusion protein. Our results showed that fusion protein can effectively label RNA under blue light illumination as well as miniSOG alone can do, which supported our fusion strategy.

Fig.14 The non-specific labelling of RNA using miniSOG-containing fusion protein

Our hypothesis is that gRNA will facilitate the formation of target RNA-fusion protein-gRNA complex, therefore specifically label the target RNA without touching other RNA in the system. To test this, we need to make sure the non-specific labelling efficiency is low to minimize background noise. We first tested the non-specific labelling effect using different protein concentration. Our results indicated that if the protein concentration in the reaction system is higher than 200 uM, the non-specific labelling of RNA will generate very high background noises, which will be a big concern when testing the specific labelling effect. So we decided to use 200 uM protein in the specific labelling reaction system.

Fig.15 Non-specific labelling of RNA using different concentration of fusion protein

Due to limited time, we were unable to gain any solid conclusion at this time. We plan to optimize the reaction condition and repeat the initial trials in the future.

Conclusion and Perspective

Our experiments completed the initial proof-of-concept of our RNA detection method, although further experiments are needed to perfect our detection project. We successfully designed, expressed, and purified miniSOG-containing fusion protein and proved its labelling ability in non-specific way. Due to limited time, we were unable to carry out the specific labelling assay and the lateral flow assay after labelling, which will provide an easier read-out for potential users in real world. We planned to carry out the following experiments afterwards to make out project more complete:

1. Repeat RNA specific labelling assay to reach a solid conclusion
2. Design and carry out lateral flow assay for read-out
3. Change different ssRNA binding protein and hairpin binding protein to optimize our labelling efficacy
4. Explore the possibility of applying this system to other diseases that have RNA biomarkers
5. Cooperate with medical schools and hospitals to acquire clinical samples for further testing using our system

Our RNA detection project is of great novelty and potential significance. We are the first iGEM team to introduce CRISPR-like system into our project to achieve the modularity of our diagnosis proteins, which is greatly valued in synthetic biology. Also, instead of using already existed methods, we are proposing and validating a conceptually novel approach to detect RNA biomarker in biological samples. We envision that our detection system will benefit the early, home-based diagnosis of diseases including but not limited to IBD and will therefore contribute our own effort in improving human health. We believe our detection method is an elegant combination of chemical and synthetic biology and will inspire future iGEM teams or other students to work on similar topics.

Reference

[1] Nikolaus, S., & Schreiber, S. (2007). Diagnostics of inflammatory bowel disease. Gastroenterology, 133(5),1670–1689. https://doi.org/10.1053/j.gastro.2007.09.001
[2] Wang, P., Tang, W., Li, Z., Zou, Z., Zhou, Y., Li, R., Xiong, T., Wang, J., & Zou, P. (2019). Mapping spatial transcriptome with light-activated proximity-dependent RNA labeling. Nat. Chem. Biol., 15(11), 1110–1119. https://doi.org/10.1038/s41589-019-0368-5
[3] Ding, T., Zhu, L., Fang, Y., Liu, Y., Tang, W., & Zou, P. (2020). Chromophore-Assisted Proximity Labeling of DNA Reveals Chromosomal Organization in Living Cells. Angew. Chem. Int. Ed., 59(51), 22933–22937. https://doi.org/10.1002/anie.202005486
[4] Ruiz-González, R., Cortajarena, A. L., Mejias, S. H., Agut, M., Nonell, S., & Flors, C. (2013). Singlet oxygen generation by the genetically encoded tag miniSOG. J. Am. Chem. Soc., 135(26), 9564–9567. https://doi.org/10.1021/ja4020524
[5] O'Connell, M. R., Oakes, B. L., Sternberg, S. H., East-Seletsky, A., Kaplan, M., & Doudna, J. A. (2014). Programmable RNA recognition and cleavage by CRISPR/Cas9. Nature, 516(7530), 263–266. https://doi.org/10.1038/nature13769
[6] Abudayyeh, O. O., Gootenberg, J. S., Konermann, S., Joung, J., Slaymaker, I. M., Cox, D. B., Shmakov, S., Makarova, K. S., Semenova, E., Minakhin, L., Severinov, K., Regev, A., Lander, E. S., Koonin, E. V., & Zhang, F. (2016). C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector. Science, 353(6299), aaf5573. https://doi.org/10.1126/science.aaf5573
[7] Abudayyeh, O. O., Gootenberg, J. S., Essletzbichler, P., Han, S., Joung, J., Belanto, J. J., Verdine, V., Cox, D., Kellner, M. J., Regev, A., Lander, E. S., Voytas, D. F., Ting, A. Y., & Zhang, F. (2017). RNA targeting with CRISPR-Cas13. Nature, 550(7675), 280–284. https://doi.org/10.1038/nature24049
[8] Abudayyeh, O. O., Gootenberg, J. S., Franklin, B., Koob, J., Kellner, M. J., Ladha, A., Joung, J., Kirchgatterer, P., Cox, D., & Zhang, F. (2019). A cytosine deaminase for programmable single-base RNA editing. Science, 365(6451), 382–386. https://doi.org/10.1126/science.aax7063
[9] Rauch, S., He, E., Srienc, M., Zhou, H., Zhang, Z., & Dickinson, B. C. (2019). Programmable RNA-Guided RNA Effector Proteins Built from Human Parts. Cell, 178(1), 122–134.e12. https://doi.org/10.1016/j.cell.2019.05.049
[10] Wang, P., Tang, W., Li, Z., Zou, Z., Zhou, Y., Li, R., Xiong, T., Wang, J., & Zou, P. (2019). Mapping spatial transcriptome with light-activated proximity-dependent RNA labeling. Nat. Chem. Biol., 15(11), 1110–1119. https://doi.org/10.1038/s41589-019-0368-5
[11] http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi

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