Creation of a system for checking the activity of Сas proteins in test tubes
The first stage consisted of studying the ability to bind Cas proteins in simple model systems. There are various approaches to studying the binding of Cas proteins to a DNA sequence. The most popular is the use of EMSA analysis. Unfortunately, this method is quite time-consuming. After analyzing the literature, we found a large variety of different ways to measure binding constants using fluorescent labels. Therefore, we have proposed an experiment design in which constants are measured using fluorescence polarization.
To bind Cas proteins to the DNA sequence, we have constructed examples containing fluorescent labels at the 5’ ends. In addition, we have constructed a universal DNA sequence with pam sites available for the interaction of type 2 and type 5 nucleases. Then, we have generated the necessary sgRNAs for different types of Cas proteins to ensure binding to these DNA sequences.
To determine the interaction activity, we obtained purified samples of Cas proteins. Plasmids were used for this purpose. A DNA matrix containing a t7 promoter was prepared for the development of sgRNA. A RNA synthesis kit was used for the synthesis. The resulting RNA protein and DNA matrix were used to determine the interaction of Cas proteins with the DNA sequence.
During the experiments, we used classical EMSA analysis and compared it with the use of fluorescence polarization methods. During testing, we have shown that it is possible to use fluorescence polarization to measure the interaction with the Cas protein RNA DNA complex.
Creation of a plasmid assembly system for subsequent testing of the system in the cell
The second stage is the preparation of a system for studying the activity of Cas proteins in cellular systems.
There are many papers devoted to the activity of Cas proteins inside cells, as well as their ability to edit individual genes. For us, the most inspiring work was comparing the activity of different systems in different types of cells (Escobar M et al 2022). In our opinion, this kind of research is the most interesting one in the context of comparing different systems in different types of cells. It shows how important it is to choose the optimal system of genetic editing for a particular case.
The most difficult part was the creation of a system that allows to determine the activity of Cas systems inside cells. It was necessary to take into account many different parameters. We have chosen two directions. In one case, we planned to use the elements available in the kit to create a test system, and in another case, we planned to create a new part based on the available plasmin proteins in the Addgene database. The existing plasmid repository allowed us to quickly and efficiently assemble the necessary genetic element from individual parts using the Golden Gate assembly. But each collection uses its own set of linkers for assembly, which complicates the assembly of parts from different collections. CDS from the collection of braids have excellent fusion sites Kozak-CDS from the collection of mammals. As part of our project, we liked to use various combinations of promoters, 5’ UTR and combine them with different variants of reporter proteins and Cas proteins to find optimal pairs for specific systems.
Since part of the CDS from the Cas parts collection cannot be directly cloned into the Asimov Mammalian Parts Collection due to the lack of Kozak sequence in the parts from the Cas collection, we decided to add an additional 5', 3’UTP. We also planned to add the necessary sequences to this area in the future to evaluate the effectiveness of Cas proteins.
Design of an automatic medium change system for cell cultivation
The cultivation of eukaryotic cells required serious training of personnel. It was necessary to carefully monitor all procedures to prevent contamination by bacteria or mold fungi. In the future, we plan to work with eukaryotic cells, but at the very first stages we thought about how we could simplify the system of working with them.
There are a number of solutions for automatic cell culture in 96 well plates. Robots for cell culture are often used in companies and in large research centers. But such devices are expensive for a simple laboratory, so we could not afford such devices. They require specialized plastic and expensive consumables.
There are many solutions for automatic peptization stations, including for cell culture (Dettinger P et al 2022). They are implemented by an automatic hand. In our work, we decided to resort to a different approach using a tablet cover that can change the environment in all cells at the same time.
Construction of a mathematical model and selection of parameters
from the Streptococcus pyogenes CRISPR (clustered regularly interspaced palindromic repeats) pathway, requiring only the co expression of a catalytically inactive Cas9 protein (dCas) and a customizable single guide RNA (sgRNA) [1]. Our team made a simple model of a CRISPRi-mediated gene regulation circuit to predict the system's behavior. This is necessary to create a predictive model of the behavior of our genetic circuits depending on the initial parameters of the system elements.
Figure 1: Diagrammatic representation of the CRISPRi model
The above schematic diagram represents the genetic circuit of CRISPRi-mediated gene regulation
- Gene 1 normally produces protein 1
- dCas derived from gene 2 and sgRNA derived from gene 3 bind together
- dCas•sgRNA binds to promoter 1 which prevents gene 1 from transcription and results in silencing the gene
Table 1 Parameters
| Parameter | Value | References | ||
|---|---|---|---|---|
| a1 | sgRNA•dCas binding | sec-1 | [3] | Depends on sgRNA and dCas concentration |
| a2 | sgRNA•dCas•DNA binding | nM-1 sec-1 | [3] | Depends on sgRNA•dCas and promoter concentration, promoter activity etc. |
| b1 | sgRNA•dCas unbinding | 0 | [3] | |
| b2 | sgRNA•dCas•DNA unbinding | 0.003 sec-1 | [3] | |
| z2 | dCas Degradation | 0 | [3] | |
| z3 | sgRNA Degradation | (e - 3) sec-1 | [3] | Depends on the concentration of RNases |
| r1 | Production rate of protein 1 (average production rate of protein) | 1/min-1 | [2] | Depends on the concentration of mRNA1, length of a protein, efficiency of ribosomes, etc. |
| r2 | Production rate of dCas | 1/min-1 | [3] | Depends on the concentration of mRNA2, efficiency of ribosomes, etc. |
| p1, p2 | Production of mRNA (average) | 20/min-1 | [2] | Depends on the activity of promoter, efficiency of polymerase, concentration of a substrate, etc |
| p3 | Production rate of sgRNA | 5/min-1 | [3] | Depends on the activity of promoter, efficiency of polymerase, concentration of a substrate, etc |
All values in the table above are estimated approximately, they may depend on every individual case due to the big complex of factors, some of them are mentioned in the table.
Table 2
| U1 | Concentration of protein 1 |
| U2 | Concentration of dCas |
| U3 | Concentration of sgRNA |
| W1 | Concentration of mRNA1 |
| W2 | Concentration of mRNA2 |
[1] Larson, M., Gilbert, L., Wang, X. et al. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat Protoc 8, 2180–2196 (2013). https://doi.org/10.1038/nprot.2013.132
[2]Tae Jun Lee, Dennis Tu, Chee Meng Tan, and Lingchong You, Systems Bioinformatics: An Engineering Case-Based Approach, Artech House, Chapter 6, 159-178 (2007)
[3] Clamons, Samuel & Murray, Richard. (2017). Modeling Dynamic Transcriptional Circuits with CRISPRi. 10.1101/225318.
Model
Plasmid
| № | Addgen # | Name |
|---|---|---|
| 1 | 188505 | DpbdCas12e KRAB |
| 2 | 188500 | HiFi dCas9 KRAB |
| 3 | 188510 | HiFi dCas9 VPR |
| 4 | 188515 | Expresses FLAG tagged DpbdCas12e VPR |
| 5 | 188519 | miniCMV eGFP hPGK BSD |
| 6 | 188520 | EFS eGFP hPGK BSD |
| 7 | 182875 | BiDi-[mEGFP]mCherry2 |
| 8 | 39318 | pMJ841 |
| 9 | 8827 | pRK793 |
| 10 | 126418 | pET 2C-T10-dCasX |
| 11 | 171667 | pJCC_058 FnCas12a D917A |
Parts
| № | Name |
|---|---|
| Promoter | |
| BBa_J433026 | UAS-Gal4-minCMV |
| BBa_J433001 | hEF1a_v3 |
| BBa_J433002 | EFS |
| BBa_J433045 | L0 promoter |
| BBa_J433000 | CMV_v1 |
| Fluorescent proteins | |
| BBa_J433017 | mRuby2 |
| BBa_J433011 | TagBFP v2 |
| BBa_J433012 | YFP v2 |
| BBa_J433013 | iRFP720 |
| Cas collection | |
| BBa_J428064 | Nickases_Cas9n_H840A |
| BBa_J428062 | Cas_variants_hSpCas9 |
| BBa_J428055 | Anti_CRISPR_AcrIIA2_mammalian |
| BBa_J428057 | Cas_variants_CP1028_ABE8e |
| BBa_J428058 | His_TwinStrep_SUMO_LwCas13a |
| BBa_J428066 | Anti_CRISPR_AcrIIA2_bacterial |
| BBa_J428060 | Cas_variants_dhSpCas9 |
| BBa_J428059 | Cas_variants_dhLwCas13a |
| BBa_J428061 | Cas_variants_hLwCas13a |
| BBa_J428056 | Cas_variants_ABE8e_TadA_8e_V106W |
| BBa_J428063 | Nickases_Cas9n_D10A |
| 5' UTR | |
| BBa_J433005 | uORFs (1x) |
| BBa_J433006 | uORFs (4x) |
| BBa_J433004 | uORFs (1x weak) |
| BBa_J433003 | 5UTR13 |
| BBa_J433045 | L0 5'UTR |
| 3' UTR | |
| BBa_J433045 | L0 3'UTR |
| BBa_J433019 | inert/synthetic2 3UTR |
| BBa_J433018 | Inert/synthetic1 3UTR |
| PolyA | |
| BBa_J433045 | L0 polyA |
| BBa_J433023 | rb glob |
| BBa_J433021 | bGH |
Primers
| № | Name | Seq |
|---|---|---|
| 1 | templ _cas_for | ATGGATCCGGTACCACGTTATTTGCTCA |
| 2 | templ _cas_for_cy5 | /Cy5/-ATGGATCCGGTACCACGTTATTTGCTCA |
| 3 | templ _cas__rew | ATGCGGCCGCAACCGCTTTCTGGTT |
| 4 | rnaCas9_1 | UUUCGGUUGAGCAAAUAACGGUUUUAGAGCUAGAAAUAGCAAGUUAAAAUAAGGCUAGUCCGUUAUCAACUUGAAAAAGUGGCACCGAGUCGGUGCUUUU |
| 5 | rnaCas9_2 | CUAUCCAAUUCUUCCCAUAAGUUUUAGAGCUAGAAAUAGCAAGUUAAAAUAAGGCUAGUCCGUUAUCAACUUGAAAAAGUGGCACCGAGUCGGUGCUUUU |
| 6 | rnaCas9_3 | UAAUUAUGGUAAAUUAUGCCGUUUUAGAGCUAGAAAUAGCAAGUUAAAAUAAGGCUAGUCCGUUAUCAACUUGAAAAAGUGGCACCGAGUCGGUGCUUUU |
| 7 | rnaCasX | GGCGCGUUUAUUCCAUUACUUUGGAGCCAGUCCCAGCGACUAUGUCGUAUGGACGAAGCGCUUAUUUAUCGGAGAGAAACCGAUAAGUAAAACGCAUCAAAGUUCGGUUGAGCAAAUAACGU |
| 8 | rnaCas12a | UAAUUCCCACUGUUGUGGGUCUGUGCAGACUGGACCAAA |
| 9 | T7RevLongC9 | AAAAAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTCAACTTGCTATGCTGTTTCCAGCATAGCTCTGA |
| 10 | T7FwdAmp | GAAATTAATACGACTCACTATAG |
| 11 | T7RevAmpC9 | AAAAAAAGCACCGACTCGGTGC |
| 12 | T7FC9sg1 | TAATACGACTCACTATAGTTTCGGTTGAGCAAATAACGGTTTCAGAGCTATGCTGGAAAC |
| 13 | T7FC9sg3 | TAATACGACTCACTATAGCTATCCAATTCTTCCCATAAGTTTCAGAGCTATGCTGGAAAC |
| 14 | T7FC9sg2 | TAATACGACTCACTATAGTAATTATGGTAAATTATGCCGTTTCAGAGCTATGCTGGAAAC |
| 15 | T7FCXsg1 | taatacgactcactataggcgcgtttattccattactttggagccagtcccagcgactatgtcgtatggacgaagcgctt |
| 16 | T7RevLongCХsg1 | ACGTTATTTGCTCAACCGAActttgatgcgttttacttatcggtttctctccgataaataagcgcttcgtccatacgac |
| 17 | T7RevCХsg1 | ACGTTATTTGCTCAACCGAA |
Bacterial Strains
In the study, chemically competent E.coli Top 10, Bl21 De3, Rosseta, Dh5a.