Overview

We have designed a combination therapy that integrates an engineered bacterial delivery system of RNAi with nanoparticle-mediated chemotherapy. Our aim is to reduce tumor chemotherapy resistance and enhance the selectivity of chemotherapy drugs, maximizing their therapeutic effects while reducing side effects.

The wet lab experiments primarily involve testing modified attenuated Salmonella strains, validating the functional knockdown of target genes by BIOTARGET, and assessing its effects on drug-resistant tumor cells. Dry lab involves calculating the affinity between the RGD peptide and integrins to ensure targeted action, simulating the release of mPEG-PLGA-PLL drugs to predict treatment efficiency, and modeling the impact of the immune system on the functional implementation of Salmonella.

Technical feasibility

The attenuated Salmonella typhimurium we use

The team of Professor Yang Ming from the College of Basic Medical Sciences at Jilin University has reduced the toxicity and immunogenicity of Salmonella by modifying lipid A-related genes, and found that it can be used as a candidate strategy for the detoxification of engineered bacteria[1]. Professor Yang discussed the feasibility of using recombinant Salmonella as a chassis with us and provided us with a delayed lysis strain χ11802, which has deleted the asd and murA genes, making the mutant bacteria unable to synthesize complete cell walls. At the same time, the murA and asd genes were inserted into a compatible expression plasmid under the control of the araC-PBAD promoter, meaning that the expression of the murA and asd genes can only occur in the presence of arabinose, ensuring the survival of the attenuated Salmonella. This bacterial system can not only serve as a screening system for successful plasmid transformation, but also allows Salmonella to gradually lyse and die after entering the body as the concentration of arabinose decreases, further ensuring the safety of attenuated Salmonella. Based on this, we knocked out msbB gene and insert T7 RNA Polymerase at the same site in the χ11802 genome. The msbB gene encodes an acyltransferase and has been implicated in altering the acylation pattern of lipid A, leading to its decrease in lipopolysaccharides (LPS).

The nanoparticles participating in the construction of BIOTARGET

We chose to use mPEG-PLGA-PLL nanoparticles to load chemotherapy drugs and then load them onto Salmonella bacteria. Polyethylene glycol monomethyl ether (mPEG) helps nanoparticles evade phagocytosis by the reticuloendothelial system. PLGA degrades slowly in the body to achieve sustained drug release, and cationic polypeptide poly-L-lysine (PLL) has good biocompatibility and controllability, which aids in target drug delivery. Finally, the composition of the nanoparticles was verified using electron microscopy.

2.3 The validation of BIOTARGET knocking down the target gene

To validate the efficacy of knocking down target gene, we used Western Blot to examine the expression changes. Results from MCF-7/CLDN6 cell line and drug-resistant cell line MCF-7/MDR concluded that BIOTARGET can efficiently knock down the target gene CLDN6.

2.4 The verification of the impact of BIOTARGET on drug-resistant tumor cells

Due to time constraints, we only validated the role of BIOTARGET in chemotherapy-resistant cells in three cell lines: MCF-7/MDR, SKOV3/CDDP, and A2780/CDDP. This validation only covers breast cancer and ovarian cancers. In future work, we will further validate BIOTARGET in more types of resistant cell lines. PGC-1α was the target gene knocked down for SKOV3/CDDP and A2780/CDDP. After constructing the plasmid pSilencer-PGC-1α, we composed the corresponding BIOTARGET-PGC-1α using the same steps and performed validation of BIOTARGET's ability to reduce tumor cell resistance in SKOV3/CDDP and A2780/CDDP cell lines.

To see the full result, please turn to the RESULT page.

Model

Through discussions with Professor Wang Fang and Professor Yang Ming from the College of Basic Medical Sciences at Jilin University, we have expanded the scope of our experimental studies, building upon the foundation of “Targeted gene screening for tumor drug resistance and analysis of prognostic relevance”.

Model screening and validation of BIOTARGET targeting function

In wet lab, we aim to utilize Salmonella-based bacterial therapy for tumor treatment. Concurrently, we aspire to enhance the targeting of Salmonella to tumors. With this goal in mind, we have designed the first part of the dry experiment: "Targeting".

Preliminary results show that integrins (composed of alpha and beta chains) are highly expressed in most tumor tissues across various types of cancer. Therefore, we hope to use integrins as a target for Salmonella to achieve targeted therapy.

RGD (Arg-Gly-Asp) is a short peptide sequence composed of the amino acids arginine (Arg), glycine (Gly), and aspartic acid (Asp)[2]. With further development, various modified forms of RGD peptides have also been produced, such as cyclo(-RGDfK), iRGD, and etc[3].

In this process, we focused more on the affinity between modified RGD peptide and integrins, thus giving more consideration to binding issues. At the same time, it can be considered that the affinity between the RGD peptide and integrins is positively correlated with the targeting efficiency of Salmonella.

Through structural simulation, molecular docking and molecular dynamics simulation, we verified that the modified Lpp'-OmpA-RGD fusion protein exhibits a better binding effect with integrins.

Targeted gene screening for tumor drug resistance and correlation analysis of prognosis

Using gene expression and clinical data from TCGA and GEO, we conducted a pan-cancer analysis to identify different cancer patients who have undergone chemotherapy. Differential and enrichment analysis were performed on their genes, as well as a prognosis analysis. The severity of different genes' involvement in tumor drug resistance was assessed and these genes were considered as candidate targets for RNAi.

Here we take lung cancer treated with cisplatin/ paclitaxel combination therapy as an example. Firstly, we grouped lung cancer patients based on the usage of these drugs and performed differential analysis on the genes within each group, generating volcano plots and heat maps.

Then, based on the selected genes, we conducted an enrichment analysis to gain a preliminarily understanding of the functions associated with drug resistance.

Then, we combined the survival time and survival state of the patients to analyze the prognosis of different genes.

We then ranked the criticality of the resistance genes according to the risk factor.

Safety and effect analysis of BIOTARGET

Considering the use of bacterial therapy, safety also needs to be considered. In this section, we combined partial experimental data, literature resources, and order-of-magnitude assumptions to first analyze the mathematical modeling of immune system changes following Salmonella injection. In the overall design, we applied anaerobic promoters to control the expression of target genes. For conducting dry experiments for validation, we used a combination of molecular docking and mathematical modeling for conceptual verification. Finally, to validate the overall effect, we first simulated the drug release from nanoparticles and conducted pharmacokinetic modeling based on that to simulate the overall functionality of BIOTARGET.

For more details, please turn to the MODEL page.

Implementation of BIOTARGET

We have developed a modular drug delivery platform designed to reduce drug resistance. Our design does not address the issue of tumor specificity, so theoretically, our treatment platform could be used for any type of tumor. Although we only used cisplatin in our validation process, the nanoparticles in our platform can encapsulate any drug, thereby improving the efficiency of any resistant drug.

Based on the characteristics of our project, our platform can be customized for patients who develop drug resistance. Depending on the type of cancer or the drug that leads to drug resistance, we can modify different modules of our carrier platform to achieve personalized medical treatment. At the same time, we aim to minimize costs by confirming the feasibility of fixed modules in the initial validation stage and creating a "template". By changing only the variable modules, different patients can utilize the platform, thereby reducing the human and material resources required for each customization.

We hope that other iGEM teams will find our project inspiring in the future and be able to build upon the work we were unable to finish this year through testing and design. A key component of synthetic biology is application, and we hope that our project will serve as a catalyst for other teams as they expand and implement our concepts.