Part:BBa_K4737001

OmpA

OmpA is the gene of outer membrane protein A of Escherichia coli str. K-12 substr. MG1655

Usage and Biology

Although genetically modified S. typhimurium VNP20009 is a useful vehicle for cancer therapy and vaccine development, it still exhibits limited tumor targeting in vivo. This implies that it is necessary to enhance the ability of engineered bacteria to target tumor cells. Carcinoembryonic antigen (CEA) is abundantly expressed in a wide range of human carcinomas, including gastrointestinal tract, pancreatic, non-small cell lung and breast cancers, thus constituting a common therapeutic target. The OmpA protein is one of the main outer membrane proteins of Gram-negative bacteria, which can serve as a carrier for the expression of foreign antigens on the surface of Gram-negative bacteria including Salmonella spp. A method that takes advantage of efficient targeting of OmpA to the outer membrane and allows C-terminal fusion of passenger proteins to be displayed is the Lpp-OmpA expression system. Based on the aforementioned studies, we proposed the expression of high-affinity CEA-specific single chain antibody fragments (scFv) into use on the surface of the bacteria.

Figure 1: The Lpp-OmpA-scFv expression pathway.

Western blotting proved that Lpp-OmpA-scFv-GFP fusion protein could be expressed in VNP20009 (Fig.4). A GFP tag was added to the C-terminus of Lpp-OmpA-scFv on the plasmid, in order to characterize the expression of anti-CEA scFv.

Figure 2: WB analysis of the expression of specific single chain antibody fragments(scFv). Lpp-OmpA-scFv-GFP molecular weight is about 66 kDa and GAPDH is a reference protein in cells with a molecular weight of 36 kDa.

We chose human gastric cancer cell line NUGC-3 with high-CEA-expression as experimental group, and a human gastric cancer cell line BGC-823 with low-CEA-expression was used as CEA negative cell lines. The engineered bacteria with GFP tag and the negative control with RFP tag were used to infect the above two types of cells simultaneously, and the function of anti-CEA scFv was verified by the infection efficiency of the bacteria. PRACTICE 1 Firstly, we infected cells by engineered bacteria with pFPV25.1-[Lpp-OmpA-scFv]-GFP vector and found that the green fluorescence of the engineered bacteria was very weak.

Figure 3: Diagrammatic sketch of Practice 1.

DEBUG 1 By reviewing the literature, we found that the VH and VL of scFv are connected by a linker, and the transient dissociation of VH and VL leads to the instability of scFv and the downstream GFP protein1. PRACTICE 2 During the second attempt, the constructed plasmids were electroporated into VNP20009 competent cells which were stably transfected with GFP. We infected NUGC-3 and BGC-823 with the constructed engineered bacteria and negative control simultaneously. For NUGC-3 with high-CEA-expression, the infection efficiency of engineered bacteria (green fluorescence) was higher than that of negative control (red fluorescence), but there was no significant difference in the infection efficiency of BGC-823 which was with low-CEA-expression (Fig.4).

Figure 4: Fluorescent microscopy results of BGC-823 and NUGC-3 cells co-infected by engineered bacteria and VNP2009 90 minutes later. Both the engineered bacteria and negative control infections were at the MOI of 1:50. Red frames outlined in the figure were Engineered bacteria that infected cells.

DEBUG 2 In this infection, we wanted to determine whether scFv really worked by comparing the efficiency of different bacteria in infecting NUGC-3 and BGC-823. However, we could not exclude the possibility that there was an interaction between the engineered bacteria and the negative control that affected their infection efficiency, which meant that further improvement of our experiments was necessary.

PRACTICE 3 In this attempt, we added control experiments in which the engineered bacteria and the negative control infected the cells separately while infecting the cells with the two bacteria simultaneously. However, NUGC-3 grew slowly, was in poor condition, and died in large numbers after a short time of infection with engineered bacteria. This resulted in a large number of engineered bacteria being washed off with PBS along with the dead cells during the infection process, which was very inconvenient for our observation. Therefore, we selected the human colorectal cancer cell line LS174T, which also has a high expression of CEA, as the experimental group. By analyzing the results of bacterial infection of LS174T cells and BGC-823 cells , we got the same conclusions as above, whether the engineered bacteria and the negative control infected the cells separately or simultaneously (Fig.5-7).

Figure 5: Fluorescent microscopy results of LS174T cells with scFv delivery by bacterial infection 2 hours later. Both the engineered bacteria and negative control infections were at the MOI of 1:50.

Figure 6: Microphotographs Results of BGC-823 cells with scFv delivery by bacterial infection 2 hours later. Both the engineered bacteria and negative control infections were at the MOI of 1:50.

Figure 7: Microphotographs Results of BGC-823 and NUGC-3 cells co-infected by engineered bacteria and VNP20009 2 hours later. Both the engineered bacteria and negative control infections were at the MOI of 1:50.

To conclude, anti-CEA single chain antibody fragments (scFv) can be expressed on the surface of VNP20009 and effectively enhance the targeting of tumor cells by engineered bacteria.

Protein Molecular Modeling

1. Introduction
We conducted molecular modeling analysis on the single-chain antibody fragment variable (scFv) employed in this project to investigate the interaction between the scFv and the carcinoembryonic antigen (CEA) present on the surface of tumor cells. We employed molecular docking and corresponding calculations to predict the structure of the antibody and its affinity for CEACAM5. To assess the practical value of our antibody, we thoroughly examined the exploitability of scFv, including aggregation and stability. Traditionally, researchers have reduced the immunogenicity of mouse antibodies by grafting the CDR region from mouse antibodies onto the human variable region framework, thus creating humanized antibodies[1]. Using the original sequence as a reference, we utilized modeling techniques to identify the key amino acid sites involved in antigen binding. Through virtual mutation of these sites, we aimed to enhance the affinity of the anti-CEA scFv and enable the engineered bacteria to exhibit stronger tumor cell targeting ability. In general, the protein molecular modeling process encompasses structural prediction of Lpp-OmpA-scFv and molecular docking with CEACAM5, followed by the modification of the antibody proteins based on an understanding of their complex structure (Figure.8).

Figure 8: Protein molecular modeling workflow.

2. Structure prediction of Lpp-OmpA-scFv

(i) Background
Since the structure of the Lpp-OmpA-scFv is unknown, our first step is to predict the structure of the antibody. AlphaFold2 (AF2), an advanced deep learning model, has achieved unprecedented performance in predicting the structure of single-chain proteins[2]. UCSF ChimeraX is the excellent molecular visualization tool launched by Resource for Biocomputing, Visualization, and Informatics (RBVI) following UCSF Chimera. We attempted to open the AlphaFold tool in ChimeraX and use ColabFold to make new protein predictions[3].

(ii) Methodology
We obtained the amino acid sequences of the Lpp-OmpA-scFv protein in a FASTA file. Additionally, we opened the AlphaFold panel in ChimeraX and copied the protein's amino acid sequence into the designated box. In the Options, we selected "Energy-minimize predicted structures," "Trim fetched structure to the aligned structure sequence," and "Use PDB templates when predicting structures." We clicked on the predict button to initiate the execution. Selecting all options may cause longer processing time, but it yields more accurate predictions. The 3D structure of the protein was predicted using ColabFold, a free computational environment provided by Google.

(iii) Results Upon analyzing the prediction results, we downloaded the optimal prediction (Figure 9.A).We assessed the reliability of each segment of the predicted structure by analyzing the additional generated images(Figure 9.B, C, D). The AlphaFold prediction provides expected position error values for each residue pair (X.Y),showing the predicted position error at residue X when aligned with residue Y in the true structure. These residue-residue "predicted aligned error" values can be visualized with an error plot (Figure 9.B).Additionally, a sequence coverage plot was generated to examine the number of similar sequences found at different positions in the Lpp-OmpA-scFv (Figure 9.C).The predicted structures include atomic coordinates and confidence estimates for each residue, with scores ranging from 0 to 100. Higher scores indicate higher confidence. This confidence measure is called pLDDT and corresponds to the model's predicted per-residue scores on the IDDT-Ca metric (Figure 9.D).

Figure 9.A: Prediction of Lpp-OmpA-scFv protein structure based on Alphafold2. Best structural prediction of proteins of Lpp-OmpA-scFv.

Figure 9.B: Prediction of Lpp-OmpA-scFv protein structure based on Alphafold2. Predicted aligned error plot.

Figure 9.C: Prediction of Lpp-OmpA-scFv protein structure based on Alphafold2. Sequence coverage plot.

Figure 9.D: Prediction of Lpp-OmpA-scFv protein structure based on Alphafold2. IDDT prediction per position plot.

(iv) Analysis
To predict the transmembrane protein regions of the aforementioned structure, we incorporated an implicit membrane into the protein structure. Additionally, we modified the membrane properties by utilizing the Analyze Transmembrane Proteins tools in Discovery Studio. The Hidden Markov Model (HMM) was employed to predict the transmembrane helices based on the amino acid sequence of the protein. Subsequently, a hidden membrane consisting of two parallel planes was introduced to the protein structure (Figure 10). The placement of the membrane was determined by optimizing the simplified solvation energy. If there was a significant charge difference between protein residues located outside the membrane, adjustments were made to the membrane..