Implementation

Implementing the Project

Overview

SHIELD aims to address the rapid rise of antimicrobial resistance (AMR) by helping researchers develop alternatives to modern antibiotics. Our toolkit is designed to rapidly develop novel antimicrobials using a unique CRISPR-interference (CRISPRi) based gene-suppression combined with a toehold-based validation. To rapidly develop effective treatments, we implemented SWORD, a machine learning-optimized design software for toeholds, and Labpilot, an automated liquid handler. Our toolkit comprehensively enables fast production and validation of new antimicrobial therapies, as demonstrated by our proof-of-concept targeting Mycobacterium tuberculosis (M. tb). Implementation of the SHIELD toolbox (see Fig. 1) into the market would require further comprehensive testing and approval from organizations like the Food and Drug Administration (FDA) to ensure its safety and suitability for widespread use against infectious diseases, but we believe this is an important early step to address a wide spread issue.

Figure 1. Elements of our SHIELD toolbox.

SHIELD toolbox

To use the SHIELD toolbox, researchers can use the easy-to-follow pipeline (see Fig. 2):

  1. Identify a pathogen of interest and a critical gene associated with the organism’s pathogenicity or survival (see Implementation: Selecting a Target Gene).
  2. Design sgRNAs (see CRISPRi: Design) and toeholds (see Toeholds: Design) for the selected target gene, using SWORD for more accurate toehold design (see Modeling: SWORD)
  3. Test the CRISPRi system (sgRNA with dCAS9) in a cell-free environment
  4. Test ideal sgRNA candidates in vivo using lipid nanoparticles (LNPs) (see Potential Delivery Mechanism)
  5. Clinical trials involving human testing and conducting safety assessments to identify off-target effects, immune responses, and other potential risks.
  6. Distribute therapeutic into the market after all FDA approvals and regulatory requirements are met.
Figure 2. Potential process for researchers and doctors.

Selecting a Target Gene

When selecting a target gene for CRISPRi-based antimicrobials, researchers should prioritize genes vital to the survival or pathogenicity of the disease, so that silencing the gene will inhibit its ability to spread or cause harm to the patient (Tao et al., 2022). Additionally, a low mutation rate reduces the possibility of quick resistance development, guaranteeing long-term therapeutic efficacy without the need to redesign the CRISPRi and toehold systems (Feng et al., 2022) To apply this approach to the SHIELD toolbox, we chose the inhA gene found in M. tb. The inhA gene encodes for enoyl-ACP reductase, an enzyme important for mycolic acid biosynthesis, which is a critical component of the M. tb cell wall (Prasad et al., 2021). Due to its necessity for the survival of M. tb, as well as its relatively low mutation rate, we selected inhA as a proof-of-concept for the SHIELD toolbox.

Potential Delivery Mechanism

While Lambert iGEM recognized the importance of an effective delivery system for real-world implementation, we did not directly pursue a specific delivery mechanism due to time constraints and the broad scope of potential applications of our project. By prioritizing the development of an adaptable platform, SHIELD can be customized for a disease of interest and easily tailored to fit the specific delivery mechanism required for each unique application. Specifically for our project, we explored the potential of utilizing viral vectors, such as adeno-associated viruses (AAVs) (Butt et al., 2022), or exosomes; however, due to several biosecurity concerns like triggering immune responses and complex purification processes that complicate production, we shifted our focus to a more common and promising delivery mechanism specifically for lung-targeted therapies (Koh et al., 2023).

Lipid nanoparticles (LNPs) have been proven as a viable delivery mechanism for CRISPRi-based systems, with scientists successfully utilizing this technology to combat diseases such as cystic fibrosis (CF) (Omidian et al., 2024). LNPs (see Fig. 3) consist of an outer lipid layer that encapsulates the CRISPRi components, ensuring their stability during transit to the target cells. This lipid shell enhances gene delivery efficacy by regulating cellular absorption while protecting the CRISPRi system from degradation (Kazemian et al., 2022).

LNPs have been proven to work in treating lung diseases, including M. tb infections (Leong & Ge, 2022). Because these particles are small enough to be customized to target specific cell types within the lungs, they overcome difficulties that have limited the efficacy of conventional delivery systems (Feng et al., 2022) Additionally, by optimizing the nanoparticles’ surface characteristics and lipid content, researchers can improve their ability to pass through these barriers and provide long-lasting therapeutic benefits (Jia et al., 2021).

Figure 3. Diagram of a lipid nanoparticle delivery system.

Biosecurity

Experiments involving AMR pose several biosecurity hazards, such as the potential for environmental release of organisms and off-target effects. To reduce these risks, SHIELD incorporates specific design choices that address and minimize these potential hazards. Unlike CRISPR, which causes permanent DNA changes, SHIELD’s use of the CRISPRi system focuses on temporarily inhibiting gene expression without making any lasting changes to the gene. This approach significantly reduces the risk of unintended mutations and irreversible genetic modifications, ensuring a safer and more controlled therapeutic with fewer off-target effects. Additionally, as the SHIELD toolbox includes toehold biosensors and LNPs for targeted lung delivery, it allows for accurate gene silencing monitoring and limits the risk of environmental release.

However, bringing the SHIELD toolbox to the market requires further comprehensive testing and approval processes from organizations such as the Food and Drug Administration (FDA) to ensure its safety and suitability for widespread use against infectious diseases.

References

Butt, M. H., Zaman, M., Ahmad, A., Khan, R., Mallhi, T. H., Hasan, M. M., ... & Cavalu, S. (2022). Appraisal for the potential of viral and nonviral vectors in gene therapy: A review. Genes, 13(8), 1370. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407213/
Feng, S., Wang, Z., Li, A., Xie, X., Liu, J., Li, S., Li, Y., Wang, B., Hu, L., Yang, L., & Guo, T. (2022). Strategies for High-Efficiency Mutation Using the CRISPR/Cas System. Frontiers in cell and developmental biology, 9, 803252. https://doi.org/10.3389/fcell.2021.803252
Jia, L., Zhang, P., Sun, H., Dai, Y., Liang, S., Bai, X., & Feng, L. (2021, October 21). Optimization of nanoparticles for Smart Drug Delivery: A Review. Nanomaterials (Basel, Switzerland). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8622036/
Kazemian, P., Yu, S.-Y., Thomson, S. B., Birkenshaw, A., Leavitt, B. R., & Ross, C. J. D. (2022, June 6). Lipid-nanoparticle-based delivery of CRISPR/Cas9 genome-editing components. Molecular pharmaceutics. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9176214/
Koh, H. B., Kim, H. J., Kang, S.-W., & Yoo, T.-H. (2023, July 29). Exosome-based drug delivery: Translation from bench to Clinic. Pharmaceutics. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459753/
Leong, E. W. X., & Ge, R. (2022). Lipid Nanoparticles as Delivery Vehicles for Inhaled Therapeutics. Biomedicines, 10(9), 2179. https://doi.org/10.3390/biomedicines10092179
Omidian, H., Gill, E. J., & Cubeddu, L. X. (2024). Lipid nanoparticles in lung cancer therapy. Pharmaceutics, 16(5), 644. https://doi.org/10.3390/pharmaceutics16050644
Prasad, M. S., Bhole, R. P., Khedekar, P. B., & Chikhale, R. V. (2021). Mycobacterium enoyl acyl carrier protein reductase (InhA): A key target for antitubercular drug discovery. Bioorganic Chemistry, 115, 105242. https://doi.org/10.1016/j.bioorg.2021.105242
Tao, S., Chen, H., Li, N., & Liang, W. (2022). The Application of the CRISPR-Cas System in Antibiotic Resistance. Infection and drug resistance, 15, 4155–4168. https://doi.org/10.2147/IDR.S370869