Exchanges between ecosystems were found to drive drug resistance to aminoglycosides, a class of antibiotics.
The spread of antibiotic resistance is not largely driven by antibiotic consumption, according to a study published today in eLife. Rather, the study suggests that the prevalence of antibiotic resistance across Europe between 1997 and 2018 is mostly explained by exchanges between ecosystems, and human exchanges such as merchandise imports or travel.
‘Interventional approaches based on decreasing antibiotic use should be complemented by a stronger control of exchanges, especially between ecosystems.’
Antibiotic resistance represents one of the largest threats to global public health, food security and global development faced today. Due to the spread of antibiotic resistance, a growing number of infections, such as pneumonia and tuberculosis, are becoming harder to treat, leading to longer hospital stays, greater costs and increased mortality. “Many public health agencies have recommended reducing antibiotic use in response to the challenges caused by resistance,” explains co-author Léa Pradier, a former PhD student at University of Montpellier, France. Pradier conducted the study alongside Stéphanie Bedhomme, a researcher at CNRS,. “However, there are cases where developed countries have reduced their antibiotic consumption and not halted the spread of antibiotic resistance genes across bacterial populations, implying other factors are at play,” continues Pradier.
Antibiotic Resistance: New Insights
To explain this, Pradier and Bedhomme set out to describe the genetic, geographical and ecological distribution of resistances to a class of antibiotics called aminoglycosides, and from this information, quantify the relative contribution of different factors driving the spread of antibiotic resistance. Aminoglycosides have limited clinical use in humans, but are often a last resort for treating multi-resistant infections. They are also commonly used in the treatment of farmyard animals, meaning that resistance to them poses a significant threat to global food security.They utilized a computational approach to screen the genetic information of over 160,000 bacteria genomes, looking for genes encoding aminoglycoside-modifying enzymes (AMEs) – the most common mechanism of aminoglycoside resistance. They detected AME genes in around a quarter of genomes screened, and in samples from all continents (excluding Antarctica) and all biomes investigated. The majority of AME-gene-carrying bacteria were found in clinical samples (55.3%), human samples (22.1%) and farm samples (12.3%).
Pradier and Bedhommme then focused on the distribution of AME genes across Europe, from 1997–2018, when the most detailed data was available. During this period, aminoglycoside usage remained relatively constant, but was highly variable between countries. Comparing the prevalence of AME genes between countries with different aminoglycoside usage over time, the team determined that aminoglycoside consumption was only a minor explanatory factor, with few positive or directional effects on AME gene prevalence.
Instead, the dataset implies that human exchanges through trade and migration, and exchanges between biomes, explain most of the spread and maintenance of antibiotic resistance when modelled over time, space and ecology. AME genes can be carried over continents by plant and animal products, and international trade and travellers, and may then spread to local strains of bacteria through a process called horizontal gene transfer – the movement of genetic information between organisms. The pool of AME genes sampled from plants, wild animals and soil had the strongest overlap with other communities, suggesting these biomes are major hubs for AME gene propagation, either by horizontal resistance gene transfer or by resistant bacteria movement.
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These findings are preliminary, as limited by the use of publicly available data, rather than deploying a dedicated sampling method. In addition, the genetic data sourced from multiple different research projects caused a sampling bias towards industrialized countries and biomes with clinical interest, leading to some locations and biomes being over-represented.
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Source-Eurekalert