On this page, we describe the genetic engineering design consisting of a few subprojects: RNA extraction, reverse transcription, PCR amplification, recovery of target gene, construction of recombinant plasmid, plasmid transformation, screening and expansion, and expression of target gene.
The designed robot tumor targeting bacteria (TuTaBa, 兔大巴) are engineered from E. coli nissle 1917 strain. The TuTaBa has five distinguished features as an immunotherapeutic robot. (1) Its intrinsic facultative anaerobic characteristics makes it move towards the hypoxic areas of tumor cells with self-propelling flagella. (2) It can also induce innate or adaptive immune responses of the host, secret toxins and form biofilms that are helpful for the treatment of tumors.1 (3) With the expression of receptor signal-regulatory protein alpha (SIRPα) gene, it should recognize CD 47 proteins present on surfaces of tumor cells. Then it can specifically move towards and attack on tumor cells. (4) CD 47 can also function as the engulfment signal and dominant macrophage immune checkpoint that represent a signal “do not eat me” to the immune system. The interaction of CD 47 with SIRPα can cause the suppression of phagocytic activities. The expression of SIRPα on bacteria should compete with the SIRPα on macrophage to interact with CD 47 on TNBC tumor cells. Then the phagocytic and cytotoxic activities of macrophages against TNBC tumor cells would be enhanced. (5) It has intrinsic MCT 1 expressed on surfaces that can deplete lactic acid in tumor microenvironment (TME). Figure 1 shows the major parts and functions of the robot TuTaBa.
Figure 1. Genetic engineering of E coli Nissle 1917.
pET-28a (+) plasmid was chosen as the expression vector. The pET-28a (+) plasmid is a prokaryotic expression vector that contains a His tag at the C-terminal end and a His tag, thrombin cleavage site, and T7 tag at the N-terminal end.2It contains several commonly used enzymatic cleavage sites, which facilitates the cloning of different genes. Unique sites are shown on the circle map in Figure 2 (A). We inserted the SIRPα gene in the MCS region of the pET-28a (+) plasmid as shown in Figure 2 (B).
Figure 2. pET-28a (+) plasmid mapping. (A) Without SIRPα gene. (B) With SIRPα gene.
The secondary structure of the protein is predicted by using SOPMA as shown in Figure 3 (A). SOPMA is the abbreviation of Self-Optimized Prediction Method with Alignment that is a tool to predict the secondary structure of a protein.3It is shown that the protein contains 48.12% random coil, 7.90% beta turn, 31.11% extended strand and 12.88% alpha helix. Random coils represent the regions of the protein chain that do not form regular secondary structure and are not characterized by any regular hydrogen bonding pattern. Such regions are usually found in terminal arms and loops. The transmembrane topology is predicted by using DeepTMHMM as shown in Figure 3 (B). DeepTMHMM is a deep learning protein language model-based algorithm that can detect and predict the topology of both alpha helical and beta barrels proteins with unprecedented accuracy.4 The target SIRPα gene sequence and primers are listed as follows.
Figure 3. Prediction of secondary structure and transmembrane. (A) SOPMA. (B) DeepTMHMM.
Figure 4 shows the procedures used for the genetic engineering of E. coli Nissle 1917. MCF 7 cells have been used for the extraction of full-length RNA samples that are further subjected to reverse transcriptase-polymerase chain reaction (RT-PCR) and generate cDNA for gene cloning.
Figure 4. Engineering processes starting from the full-length RNA extraction from cells.
Most eukaryotic genes contain introns and we usually don't know how they are spliced without their mRNA. For this is the reason you need to extract the full-length mRNA and generate cDNA for gene cloning and expression. Cultured MCF 7 cells were used for the RNA extraction.
Figure 5. Gel electrophoresis of extracted RNA.
Primers were designed according to the gene sequence and PCR was performed with the following PCR system.Primers used in the experiment is summarized in Table 1 and gel electrophoresis of DNA is shown in Figure 6.
| Table 1. Primers used for cloning of target gene SIRPα | |
|---|---|
| Primers | Sequences |
| F- SIRPa | ATGGGTCGCGGATCCGAATTCATGGAGCCCGCCGGCCCGGC |
| R-SIRPa | TCGAGTGCGGCCGCAAGCTTTCAGTGGTGATGGTGATGATGCTTCCTCGGGACCTGGACGCTGGCGTACT |
Figure 6. Gel electrophoresis of DNA.
Gel pieces containing intended DNA were excised and subjected to further experiments for the recovery of DNA from gel slices with a gel dissolution method. Then conduct a DNA ligation to fuse the insert to the recipient plasmid. Competent cells DH5alpha are used for the transformation. Figure 7 shows bacterial clones on the plate.
Figure 7. Engineered E coli Nissle 1917 bacterial clones.
Quality control (QC) refers to the process through which the team can ensure the quality of the whole experiments and activities to be under control. We have established experimental and safety protocols that help standardize both experiments and behaviors.
✓ Carefully-designed experiments. In accordance with national and institutional safety regulations and guidelines, all experiments are carefully designed so as to minimal risk of dangerous operations and exposures. Team members can obtain immediate help from PIs, instructors and advisors who have different educational backgrounds in chemistry, microbiology and medicine.
✓ Trained personnel. Before the starting of the project, team members must be trained not only in techniques but also in general label safety, biosafety, ethics, and norms.
✓ Standardized experimental protocols. Various experimental procedures including step-by-step reactions, instrumental operations are optimized and finalized as written protocols that can be shared in lab.
✓ Characterization of intermediate and final products. Starting from the extraction of full-length RNA from tumor cells, intermediate products and final products should be characterized with electrophoretic, chromatographic and mass spectrometric analysis.
1. Gurbatri, C. R., Arpaia, N., Danino, T., Engineering bacteria as interactive cancer therapies. Science 2022, 378, 858-864.
2. Tham, H. Y., Song, A. A., Yusoff, K., & Tan, G. H. (2020). Effect of different cloning strategies in pET-28a on solubility and functionality of a staphylococcal phage endolysin. Biotechniques, 69(3), 161-170.
3. Geourjon, C., Deleage, G. (1995). SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics, 11(6), 681-684.
4. Hallgren, J., Tsirigos, K. D., Pedersen, M. D., Almagro Armenteros, J. J., Marcatili, P., Nielsen, H., Winther, O. (2022). DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. BioRxiv, 2022-04.