Detection of antibiotic-resistant bacteria in diverse environments

The discovery and use of antibiotics are two of the most significant breakthroughs in twentieth-century medicine—leading to dramatic reductions in human morbidity and mortality. Antibiotics and other antimicrobial agents are also used extensively in agriculture. Typically, livestock are given antibiotics to encourage growth and prevent illness. There are multiple classes of antibiotics, categorized based on their mechanism of action. Antibiotics can damage the cell wall of a bacterium, block DNA, RNA, or protein synthesis, or even inhibit metabolic growth. Some antibiotics are specific to certain species of bacteria (narrow spectrum), whereas others can affect a wide range of bacteria (broad spectrum).

The remarkable healing power of antibiotics has led to their overuse, which can can cause some strains of bacteria to acquire antibiotic resistance genes (ARGs) over time due to selective pressure. As these strains grow and expand, different antibiotics must be developed to combat them. Ultimately, the current strategy is to prevent excessive use of antibiotics, thereby limiting the exposure of bacteria to these agents and minimizing selective pressures. For human patients, overuse can result from incorrect dosing, over- or mis-prescribing, or improper disposal. Critically, antibiotic use in agriculture can also contribute to bacteria developing ARGs. Not only can antibiotic-resistant bacteria develop in livestock, but manure can act as a vector to transmit antibiotic-resistant strains into the environment via soil, water, and produce. Thus, antibiotic-resistant bacteria can be spread widely, and recent efforts have focused on identifying ARGs and tracking their prevalence in a variety of environments and sample types.

In 2013, a study reported that in the US, >2,000,000 illnesses are caused by antibiotic-resistant bacteria each year, and >23,000 deaths have been linked to these bacteria (Stop the Spread of Superbugs | NIH News in Health 2020). There are currently over 1,000 types of ARGs that have been identified to confer resistance to the hundreds of available antibiotics. Consequently, monitoring human and environmental samples for ARGs requires the ability to perform high-throughput real-time PCR (qPCR) screens for many different target sequences in numerous sample types. Since bacteria are constantly developing new ARGs, it is critical to be able to modify and add new targets to the screen. Thus, ARG research requires a flexible, high-throughput qPCR system to monitor gene expression in a wide range of sample types.

The SmartChip Real-Time PCR System provides both flexibility and increased throughput, which have been instrumental in enabling studies of the antibiotic resistome and monitoring the spread of antibiotic-resistant bacteria. The SmartChip system has been utilized in 75% of the publications using high-throughput qPCR for antibiotic resistance research (Waseem et al. 2019), including the following topics:

• Detection of ARGs in manure and sewage, which can transfer antibiotic-resistant bacteria to crops and produce, as well as to analyze soil, sediment, and sludge samples from crops and areas that received significant organic fertilization or urban runoff from improperly treated wastewater.
• Identification of potential threats to the drinking water supply by detecting ARGs in various environmental water sources such as streams, rivers, lakes, and oceans.
• Screening for ARGs in hospitals, which may contain significant amounts of antibiotic-resistant bacteria that pose a risk to patients and can be detected in various locations, such as air conditioning filters, hospital wastewater, and treatment plants.

References

• Stop the Spread of Superbugs | NIH News in Health. Available at: https://newsinhealth.nih.gov/2014/02/stop-spread-superbugs
• Waseem, H. et al. Contributions and challenges of high throughput qPCR for determining antimicrobial resistance in the environment: a critical review. Molecules 24, 163 (2019). Available at: https://pubmed.ncbi.nlm.nih.gov/30609875/