1. Ecology
  2. Epidemiology and Global Health

Antibiotic Resistance: A mobile target

The global spread of antibiotic resistance could be due to a number of factors, and not just the overuse of antibiotics in agriculture and medicine as previously thought.
https://doi.org/10.7554/eLife.86697
Share this article
Cite this article
  1. Carolina Oliveira de Santana
  2. Pieter Spealman
  3. Gabriel G Perron
(2023)
Antibiotic Resistance: A mobile target
eLife 12:e86697.
https://doi.org/10.7554/eLife.86697
  2. Download BibTeX
  3. Download .RIS
  1. Carolina Oliveira de Santana
  2. Pieter Spealman
  3. Gabriel G Perron  Is a corresponding author
  1. Center for Environment Sciences and Humanities, Bard College, United States
  2. Center for Genomics and Systems Biology, New York University, United States

Antibiotic resistance – the ability of bacteria to survive even the strongest clinical treatments – continues to be a major public health concern around the world (Antimicrobial Resistance Collaborators, 2022). The overuse of antibiotics in medicine and agriculture is often thought to be the driving force behind the emergence and spread of bacteria that are resistant to antibiotics (Cohen, 1992; Levy and Marshall, 2004). Overuse can certainly explain the selection for bacteria with genes for antibiotic resistance in environments that are heavily impacted by human activity (Gaze et al., 2013; Kraemer et al., 2019), but it cannot explain the widespread distribution of genes for resistance to clinically relevant antibiotics well away from hospitals and farms. Indeed, such genes have even been found in environments as remote as the Arctic permafrost (D’Costa et al., 2011; Perron et al., 2015) and Antarctica (Hwengwere et al., 2022; Marcoleta et al., 2022).

Now, in eLife, Léa Pradier and Stéphanie Bedhomme of the University of Montpellier report the results of a study that sheds light on this matter (Pradier and Bedhomme, 2023). Focusing on resistance against aminoglycoside, a widely used family of antibiotics that includes streptomycin (Davies and Wright, 1997), the researchers conducted one of the largest surveys of antibiotic resistance genes to date. They analyzed more than 160,000 bacterial genomes collected from all over the globe, focusing on 27 clusters of genes that code for aminoglycoside-modifying enzymes (also known as AME genes). The bacteria were sampled between 1885–2019, although most were sampled recently, with the first example of a bacterium with an AME gene dating from 1905. In addition to the location and date of sampling, the study also considered the number of antibiotics consumed in each country, commercial trade routes, and human migration. Finally, the samples came from eleven different biomes, representing a range of environments where antibiotic resistance can be found: clinical environments (like hospitals), human habitats, domestic animals, farms, agrosystems, wild plants and animals, freshwater, seawater, sludge and waste, soil, and humans.

The researchers found that the prevalence of genes for aminoglycoside resistance increased between the 1940s and the 1980s, likely following an increase in the use of antibiotics after the discovery of streptomycin in 1943 (Schatz et al., 1944), and then remained at a prevalence of around 30%, despite an overall decrease in consumption. Crucially, they also discovered that around 40% of the resistance genes were potentially mobile, which means they can be easily exchanged between bacteria.

Pradier and Bedhomme also found that antibiotic-resistant bacteria are present in most biomes, and not just in hospitals and farms. Moreover, they found that the prevalence of aminoglycoside resistance genes varied more from biome to biome than it did with human geography or with the quantity of antibiotics used (Figure 1). This means that the antibiotic resistance found in humans in one country is more likely to be related to the antibiotic resistance found in humans in a distant country than it is to the antibiotic resistance found in the soil or animals nearby. Moreover, they also discovered that biomes such as soil and wastewater likely play a key role in spreading the genes for antibiotic resistance across different biomes.

Figure 1
Download asset Open asset
Factors influencing the spread of resistance against aminoglycoside antibiotics.

Pradier and Bedhomme analyzed the variables that influence the prevalence of genes that code for aminoglycoside-modifying enzymes (AME genes) in a sample of 160,000 bacterial genomes collected from all over the globe. The samples were classified as belonging to one of eleven biomes (see main text). It was found that ecology (that is, which of the 11 biomes the sample was collected from) was the biggest influence, followed by human exchange (immigration and material import/export) and aminoglycoside consumption. This scatter plot for samples collected in Europe between 1997 and 2018, which is derived from Figure 5 of Pradier and Bedhomme, 2023, shows how the prevalence of 16 clusters of these genes depends on these three variables, with the thickness of each axis representing how influential that variable was overall (ecology, 80%; human exchange, 13%; aminoglycoside consumption, 7%; interactions between the variables are not included). The clusters of genes represented by circles code for N-acetyltransferases; triangles represent nucleotidyltransferases, and squares represent phosphotransferases.

These findings raise important questions about the mechanisms underlying the spread of antibiotic resistance. What factors promote the spread of antibiotic resistance in environments not impacted by human activities? Can we extrapolate these results from the aminoglycosides to all other classes of antibiotics? Is it possible that antibiotic resistance results from interactions with local microbial communities more than exposure to commercial antibiotics? Do the genes for antibiotic resistance spread in the pathogenic bacteria responsible for human and animal infections in the same way as they spread in non-pathogenic bacteria? Given the extent of the selective pressure exerted by human pollution, what limits the spread of antibiotic-resistance genes between biomes, especially given the large proportion of genes on mobile elements?

It may well be that consumption still has a paramount role when it comes to resistance to the antibiotics that are used to treat infection, especially in humans and clinical biomes. Nevertheless, it is clear that we need to pay more attention to the role of the environment when formulating plans to combat antibiotic resistance on a global scale.

References

Article and author information

Author details

  1. Carolina Oliveira de Santana

    Carolina Oliveira de Santana is in the Center for Environment Sciences and Humanities, Bard College, Annandale-on-Hudson, United States
    Contributed equally with
    Pieter Spealman
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9385-0199

  2. Pieter Spealman

    Pieter Spealman is in the Center for Genomics and Systems Biology, New York University, New York, United States
    Contributed equally with
    Carolina Oliveira de Santana
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7105-284X

  3. Gabriel G Perron

    Gabriel G Perron is in the Center for Environment Sciences and Humanities, Bard College, Annandale-on-Hudson, United States, and the Center for Genomics and Systems Biology, New York University, New York, United States
    For correspondence
    gperron@bard.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3526-5239

Publication history

  1. Version of Record published: March 8, 2023 (version 1)

Copyright

© 2023, Oliveira de Santana, Spealman et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,246
    views
  • 160
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Carolina Oliveira de Santana
  2. Pieter Spealman
  3. Gabriel G Perron
(2023)
Antibiotic Resistance: A mobile target
eLife 12:e86697.
https://doi.org/10.7554/eLife.86697

Categories and tags

    1. Related to

    Ecology, more than antibiotics consumption, is the major predictor for the global distribution of aminoglycoside-modifying enzymes

    Léa Pradier, Stéphanie Bedhomme
  2. Further reading

Further reading

    1. Ecology
    2. Epidemiology and Global Health

    Ecology, more than antibiotics consumption, is the major predictor for the global distribution of aminoglycoside-modifying enzymes

    Léa Pradier, Stéphanie Bedhomme
    Research Article

    Antibiotic consumption and its abuses have been historically and repeatedly pointed out as the major driver of antibiotic resistance emergence and propagation. However, several examples show that resistance may persist despite substantial reductions in antibiotic use, and that other factors are at stake. Here, we study the temporal, spatial, and ecological distribution patterns of aminoglycoside resistance, by screening more than 160,000 publicly available genomes for 27 clusters of genes encoding aminoglycoside-modifying enzymes (AME genes). We find that AME genes display a very ubiquitous pattern: about 25% of sequenced bacteria carry AME genes. These bacteria were sequenced from all the continents (except Antarctica) and terrestrial biomes, and belong to a wide number of phyla. By focusing on European countries between 1997 and 2018, we show that aminoglycoside consumption has little impact on the prevalence of AME-gene-carrying bacteria, whereas most variation in prevalence is observed among biomes. We further analyze the resemblance of resistome compositions across biomes: soil, wildlife, and human samples appear to be central to understand the exchanges of AME genes between different ecological contexts. Together, these results support the idea that interventional strategies based on reducing antibiotic use should be complemented by a stronger control of exchanges, especially between ecosystems.

    1. Ecology
    2. Epidemiology and Global Health

    Landscape drives zoonotic malaria prevalence in non-human primates

    Emilia Johnson, Reuben Sunil Kumar Sharma ... Kimberly Fornace
    Research Article

    Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.

    1. Computational and Systems Biology
    2. Ecology

    Collaborative hunting in artificial agents with deep reinforcement learning

    Kazushi Tsutsui, Ryoya Tanaka ... Keisuke Fujii
    Research Article

    Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.