Establishing a Monitoring Baseline for Antibiotic Resistance in Key environments





Research Project: 2020-01-01 - 2023-12-31
Total sum awarded: €1 426 356

There is a growing recognition that interventions within the healthcare sector are not enough to curb antibiotic resistance development. Instead, a one-health perspective incorporating animal husbandry and external environments is needed. This calls for monitoring antibiotic resistance outside of the healthcare setting. Unfortunately, antimicrobial resistance monitoring lacks comprehensive reference data for the vast majority of environments. Therefore, there is little knowledge on the range of background abundance and prevalence of antibiotic resistance genes (ARGs) occurring naturally. Furthermore, the few milieus where reference data exist are biased towards a small number of environments and there is no standardized methodology or any well-defined set of relevant ARGs that routinely are tested for monitoring purposes. This project will solve or alleviate these problems by integrating several approaches under one umbrella framework. We will 1) establish baseline ranges for background ARG abundances and diversity in different environments, 2) standardize different methods for monitoring ARGs and provide a means for making them comparable, 3) identify sets of priority target ARGs for monitoring, 4) develop methods to detect emerging resistance threats and thereby provide an early-warning system for resistance, and 5) suggest a monitoring scheme that can be used in a modular fashion depending on the available resources. Establishing a coherent monitoring scheme is imperative for efficient monitoring, which in turn is essential for limiting future resistance development.

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  • Johan Bengtsson-Palme, University of Gothenburg, Sweden (Coordinator)
  • Thomas U Berendonk, Technische Universität Dresden, Germany (Partner)
  • Etienne Ruppé, INSERM, French National Institute of Health and Medical Research, France (Partner)
  • Sofia Forslund, Max Delbrück Center for Molecular Medicine, Germany (Partner)
  • Luis Pedro Coelho, Fudan University, China (Partner)
  • Rabaab Zahra, Quaid-i-Azam University, Pakistan (Partner)

Antibiotic resistance is steadily increasing among typical disease-causing bacteria and over time the realization has sunk in that in order to prevent antibiotic resistance development it is not enough to reduce antibiotics use in health care. Antibiotics are also used extensively in animal farming, and bacteria can be exposed to antibiotics in the environment, for example near wastewater treatment plants. These environments can contribute to spreading resistant bacteria. To better understand and prevent these processes, we need a better picture of where resistant bacteria exist in different environments and how many they are. We would also need to know which genes that make these bacteria resistant to antibiotics. However, there are a few things that complicate such monitoring for resistance in the environment. First of all, while humans are responsible for amplifying and spreading antibiotic resistance, it exists at a low level even in pristine habitats. However, we do not know what the normal levels of antibiotic resistance are in non-polluted areas or to what extent human have spread resistant bacteria to our surroundings. This makes it almost impossible to say what would be “safe” levels of resistance in the environment or which environments would be of particular concern. Second, standards for environmental monitoring of antibiotic resistance are currently lacking. It is unclear which bacterial species and resistance genes should be prioritized for such monitoring and which methods that are most suitable for determining resistance levels in environmental samples. Third, a monitoring system should ideally be able to provide an early warning about new emerging threats, which unfortunately cannot be provided using today’s standard methods. The EMBARK program aims to solve these problems by integrating several methods for environmental antibiotic resistance monitoring in one unified framework. Specifically, we will: 1) Establish background levels for antibiotic resistance in various environments through re-analysis of already generated data and collection of new unique samples from relevant environments 2) Standardize methods for environmental antibiotic resistance surveillance and develop ways to normalize and compare data generated using different methods 3) Identify genes, species and environments that should be prioritized for monitoring 4) Develop methods to detect emerging antibiotic resistance threats before they cause major clinical problems, and thus enable an early-warning system for resistance emergence 5) Suggest methods for monitoring that can be used efficiently given the amount of available resources By establishing relevant and consistent comparisons between methods and environments, we will build a framework for environmental antibiotic resistance monitoring that can be adapted to the available budget and yet contribute to a grand picture of the resistance situation globally. This is particularly important in low- and middle-income countries, where much of the environmental spread of resistance is likely happen but monitoring resources is often scarce.