Abstract

The World Health Organization has declared antimicrobial resistance (AMR) as the biggest global public health threat of the 21st century. AMR occurs when microorganisms evolve to resist antibiotics, rendering traditional treatments ineffective– a process exacerbated by the current misuse of antibiotics in agriculture and healthcare. To combat AMR, Lambert iGEM created SHIELD: a versatile toolbox for developing novel antimicrobials that combat antibiotic-resistant diseases.

SHIELD uses the CRISPR-interference system to silence critical genes in target organisms, alongside a toehold biosensor to measure the extent of gene silencing. We also developed SWORD: a machine learning software tool to optimize toehold design, and LabPilot: a frugal liquid handler to automate experimentation.

SHIELD enables researchers to rapidly develop multiple antimicrobials for a single disease by targeting several genes, circumventing the emergence of resistance in a single gene without compromising overall treatment effectiveness, thus SHIELD empowers researchers to outpace antibiotic resistance.

Defining The Problem

Antimicrobials have been one of the most transformative medical discoveries of the 20th century. They are responsible for many recent medical breakthroughs including safe surgical procedures, effective cancer therapies, and organ transplants (Davies & Davies, 2010). However, the rapid emergence of antimicrobial resistance (AMR) is rendering even the most advanced antibiotics ineffective. As AMR continues to spread, we risk losing the ability to treat once-manageable infections, potentially returning humanity to an era where minor infections are significant health threats. This revolutionary technology, which previously extended the average human lifespan by 23 years, now faces the stark reality of reversing decades of medical progress (Hutchings, 2019).

AMR directly caused more than 1.14 million deaths in 2021, and is projected to cause more than 2-10 million annual deaths by 2050. AMR is also expected to become the second leading cause of death (Murray et al. 2016; Naghavi et al. 2024).

Antimicrobial resistance occurs when microbes evolve to resist antibiotics, rendering traditional treatments ineffective and unable to treat common infections. AMR develops through natural selection of resistant strains and intensifies through the transfer of resistance genes between bacteria (Prestinaci et al., 2015).

Figure 1. Diagram of how antibiotics contribute to resistance.

Antibiotic resistance is a multifaceted issue fueled by the misuse of antibiotics in both the healthcare and agriculture industries (Ahmed et al., 2024).

Healthcare

The perceived broad-spectrum efficacy of antibiotics in treating bacterial infections frequently contributes to their overuse by both healthcare providers and patients (Llor & Bjerrum, 2014). Despite stricter prescription guidelines, responsibility falls on the patient to prevent misuse of antibiotics. Something as simple as a patient not completing a full prescription of antibiotics can contribute to antimicrobial resistance (Llor & Bjerrum, 2014). This seemingly inconsequential action gradually undermines the effectiveness of modern medicine. Ultimately, emphasizing patient education in healthcare is not just important – but crucial for protecting the future of medicine.

Agriculture

The overuse of antibiotics in agriculture presents a complex, interconnected problem rooted in improper agricultural practices. The soil serves as a reservoir for a vast number of microbial populations. However, when antimicrobials are introduced into this environment (via runoff from watersheds or other sources) bacteria develop resistance genes which then begin to proliferate among microbial communities due to selective pressures. In Georgia, an estimated 102 billion livestock animals annually release 600,000 kg of antibiotics into the soil, contaminating local drinking water through runoff (Tian et al., 2021). Human consumption of poultry and water contaminated by fecal runoff from antibiotic-treated chickens results in exposure to residual drugs, fostering further antibiotic resistance and diminishing the efficacy of existing medications in human populations(Tian et al., 2021).

Current Solutions

Most current approaches to combating antimicrobial resistance involve developing novel antibiotics or optimizing them to keep up with rapidly evolving pathogens (Parmanik et al., 2022). However, developing a new antibiotic can cost over $1 billion USD, potentially yielding a mere 10% in revenue annually (Nature, 2024). In addition to low returns on the investment, the developmental process is a time-consuming 10 to 15 year process, starting from the initial research stage to obtaining FDA approval to finally getting the technology into the hands of doctors and patients. Several novel methods for combating antimicrobial resistance are currently being researched:

Figure 2. Chart displaying the main, current approaches to antimicrobial resistance

While novel antimicrobial approaches show promise, there are still limitations and remain vulnerable to the same resistance issues that plague traditional antimicrobials.

Our Approach

After months of extensive research and conversations with antimicrobial resistance (AMR) experts, Lambert iGEM adopted a multifaceted approach to address this pressing global and local issue.

While traditional antibiotics remain static against evolving microbes, modern vaccines are constantly updated to combat the change in viral strains (Li et al., 2022). Rather than seeking a therapy that is unaffected by resistance, we took inspiration from modern mRNA vaccines – vaccines that can be quickly constructed using only the pathogen’s genetic code– and shifted our focus to developing SHIELD (Silencing Harmful Immunogenic Effector Links to Disease): a versatile toolbox for a rapid response to an ever changing threat.

Inspired by the CRISPR-Cas approach to antimicrobial therapy, SHIELD utilizes a CRISPR-interference (CRISPRi) system to suppress critical infectious genes without permanently altering the genome. To facilitate rapid validation and testing of various CRISPRi therapies, we developed a toehold-based testing pipeline that can swiftly validate new CRISPRi systems, enhancing SHIELD’s capacity for quick adaptation. By using a cell-free system coupled with these assays, linear DNA can be utilized, eliminating the time-consuming process of cloning plasmids. As we faced challenges in designing effective toehold switches, we developed a machine learning-based software to optimize the toehold design process. To further enhance SHIELD’s abilities and ensure precise and consistent execution of our experiments, we completed the development of Labpilot, a frugal automated liquid handler, which we began during the 2023 competition season.

Collectively, our CRISPRi system, toehold testing pipeline, software, and automated liquid handler form a comprehensive toolkit that enables the swift development and testing of new antimicrobial therapies. As a proof of concept for SHIELD, we chose to create an antimicrobial therapy for Mycobacterium tuberculosis and its critical gene inhA.

CRISPRi

Lambert iGEM pursued CRISPR-interference (CRISPRi) to downregulate the critical genes in antibiotic-resistant bacteria. CRISPRi utilizes a deactivated Cas9 (dCas9) endonuclease paired with a specific oligonucleotide single-guide RNA (sgRNA) to create a dCas9-sgRNA complex. This complex inhibits the transcription of the critical gene by binding to a DNA sequence, thereby preventing RNA polymerase from continuing transcription (Marshall et al., 2018).

Toehold Biosensors

To validate the effectiveness of the CRISPRi treatment in vivo, SHIELD uses toehold biosensors. These RNA-based sensors detect the presence of specific mRNA sequences and produce quantitative results, allowing researchers to evaluate whether the antimicrobial treatment silences the gene of interest effectively.

Modeling

Simulations

To choose between CRISPRi and CRISPR, we developed an ordinary differential equation (ODE) model simulating Cas9 and dCas9 systems on target DNA constructs. Furthermore, to optimize our wet lab experiments, we extended our model to simulate varying concentrations of sgRNAs. The simulation provided the optimal concentration for different guide RNAs, maximizing the repressive activity of the CRISPRi mechanism and allowing our wetlab committee to customize their experimental protocols for specific sequences.

SWORD

To effectively design toehold switches for our project, we created SWORD - a machine learning-based software tool that more accurately designs toehold switches in silico. We validated the accuracy of SWORD with experimentation. The efficiency of toehold switch activation we tested in vitro matched the efficiency predicted using SWORD.

LabPilot

To manage the large number of reactions needed for the development of our CRISPRi-based mechanism, Lambert iGEM created LabPilot, a frugal automated liquid handler that allows researchers to focus time elsewhere from running lengthy reactions.

Soil

To assess the prevalence of antimicrobial resistance in the environment, we gathered soil samples along the Chattahoochee River which runs through much of Georgia’s farmland and serves as a natural collector of agricultural runoff. Our analysis focused on detecting the presence of tetracycline and integrons encoding antibiotic resistance, providing crucial insights into how AMR spreads through soil.

References