Category: Recent Publications

Today, I will be giving a talk at the EDAR8 conference in Brisbane, which will partially cover something that I has been cooking in the research group for some time. It started off with a recurring need to see if a particular antibiotic resistance gene (ARG) was truly new or if it had been detected in previous studies. And after having done the same procedures in several studies, we started to think that maybe it would make sense to build a resource that could be used to keep track of both established and latent ARGs (1) and that could be easily updated over time. And the result is CLEVER – a scheme for Classification of Latent and Established Variants of Extant Antibiotic Resistance Genes.

CLEVER is two things. First, it is a set of definitions for ARGs, making it easier to assign them to categories, for example for risk assessment. Second, it is a database based on these criteria, and to the best of our knowledge the first resource to organize both established and latent ARGs into the same coherent database structure, while still keeping them categorized. To achieve this, CLEVER integrates data from ResFinder (2), CARD (3), ResFinderFG (4), as well as ARGs predicted by the fARGene (5) and MUSTARD (6) tools, gathered from published literature (6-13),

Very briefly, CLEVER is built on the following definitions:

• Established ARG (E): An ARG that is experimentally verified to confer antibiotic resistance and is present in human pathogens.
• Latent ARG (L): An ARG that confers a resistance function (or is predicted to do so), but does not exist in pathogens
• Mobile ARG (M): An ARG which is present on a mobile genetic element, which could be plasmids, integrons, transposons or integrative conjugative elements.
• Chromosomal ARG (C): Any ARG that does not meet the criteria for a mobile ARG above.
• Validated ARG (V): An ARG for which the resistance function has been verified in laboratory experiments, by showing that the presence of the gene increases the MIC of the host compared to an otherwise isogenic strain that does not carry the gene, or alternatively that over-expression of the gene induces a higher MIC compared to an isogenic reference strain.
• Predicted ARG (P): An ARG for which its function has not been verified experimentally (see above), but has been predicted to be an ARG by fARGene
• Structure-predicted ARG (S): An ARG for which its function has not been verified experimentally (see above), but has been predicted to be an ARG based on 3D-structure similarity by MUSTARD

Importantly, what CLEVER also does is to give names (CLEVER IDs) to latent ARGs that currently does not have any consistent way to refer to them. This makes it possible to unambiguously reference a particular ARG family across studies, which is crucial for risk assessment and understanding the spread of AMR.

Finally, by classifying ARGs as established or latent, mobile or chromosomal and verified or predicted makes it possible to identity particular latent ARGs that until now has gone under the radar. Particularly, latent ARGs that already appear on mobile genetic elements and have several mobile variants, are likely to be imminent AMR threats to human health, making them “most-wanted” ARGs that should be targeted for phenotypic evaluation and potential inclusion into AMR surveillance efforts.

By specifically looking into potential ARGs against the last-resort antibiotics carbapenems, colistin, tigecycline and plazomicin, and pulling out latent ARGs that belong to classes potentially conferring resistance to these antibiotics that are already present on plasmids, we can already present a list of five ARGs of imminent concern, which we label the first CLEVER list of most-wanted ARGs: ~blaA-145, ~blaD2-129, ~aac6p-147, ~aph2b-14, and ~aph6-89.

Naturally, there is still a lot work to be done on CLEVER, but I am very proud of what we have already, and I think that the current version (2.0) already has potential to be very useful for AMR studies. We have already started using this internally in the lab and within SEARCHER quite extensively in the last few months, so I am happy to see the use of this resource in the larger AMR community!

References:

1. Inda-Díaz JS, Lund D, Parras-Moltó M, Johnning A, Bengtsson-Palme J, Kristiansson E. Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome 2023;11:44. https://doi.org/10.1186/s40168-023-01479-0.
2. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. Journal of Antimicrobial Chemotherapy 2020;75:3491–500. https://doi.org/10.1093/jac/dkaa345.
3. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Research 2016:gkw1004. https://doi.org/10.1093/nar/gkw1004.
4. Gschwind R, Ugarcina Perovic S, Weiss M, Petitjean M, Lao J, Coelho LP, et al. ResFinderFG v2.0: a database of antibiotic resistance genes obtained by functional metagenomics. Nucleic Acids Research 2023:gkad384. https://doi.org/10.1093/nar/gkad384.
5. Berglund F, Österlund T, Boulund F, Marathe NP, Larsson DGJ, Kristiansson E. Identification and reconstruction of novel antibiotic resistance genes from metagenomes. Microbiome 2019;7, 52. https://doi.org/10.1186/s40168-019-0670-1
6. Ruppé E, Ghozlane A, Tap J, Pons N, Alvarez A-S, Maziers N, et al. Prediction of the intestinal resistome by a three-dimensional structure-based method. Nature Microbiology 2019;4:112–23. https://doi.org/10.1038/s41564-018-0292-6.
7. Inda-Díaz JS, Lund D, Parras-Moltó M, Johnning A, Bengtsson-Palme J, Kristiansson E. Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome 2023;11:44. https://doi.org/10.1186/s40168-023-01479-0.
8. Victor MP, Radisic V, Grevskott DH, Marathe NP. Hospital effluent in a low-resistance setting is responsible for dissemination of novel antibiotic resistance genes into the marine environment. Ecotoxicology and Environmental Safety 2025;301:118390. https://doi.org/10.1016/j.ecoenv.2025.118390.
9. Victor MP, Øvreås L, Marathe NP. Characterization of known and novel clinically important antibiotic resistance genes and novel microbes from wastewater-impacted high Arctic fjord sediments. Science of The Total Environment 2025;985:179699. https://doi.org/10.1016/j.scitotenv.2025.179699.
10. Li B, Jiang L, Johnson T, Wang G, Sun W, Wei G, et al. Global health risks lurking in livestock resistome. Sci Adv 2025;11:eadt8073. https://doi.org/10.1126/sciadv.adt8073.
11. Somerville V, Meola M, Nunes-Richards A, Bengtsson-Palme J, Neukamm J, Majander K, et al. Microbial community dynamics in a traditional Swiss mountain cheese over 142 years of cheesemaking 2026. https://doi.org/10.64898/2026.02.26.708305.
12. Coche‐Miranda J, Arros P, Canales N, Berríos‐Pastén C, Azziz G, Lagos R, et al. Antarctic soil microbiomes encode structurally conserved and phylogenetically diverse beta‐lactamases. iMetaOmics 2026;e70118. https://doi.org/10.1002/imo2.70118.
13. Wang K, Xu J, Li X, Zhu P, Suo R, Lu X, et al. Evolutionary selection of trimethoprim-resistant dfrA genes in lytic phages affects phage and host fitness during infection. Sci Adv 2025;11:eadt4817. https://doi.org/10.1126/sciadv.adt4817.

I am happy to (finally) report that the second part of Emil Burman‘s PhD thesis was published over the weekend in FEMS Microbiology Letters! This paper has spent almost a year in peer review, but that’s even more reason to celebrate that it is finally out.

In this paper, we have studied how Pseudomonas aeruginosa compete with other members in microbial communities (1). To study this, we introduced two different P. aeruginosa strains into the microbial model community THOR (2): the relatively non-virulent PAO1 and the model strain for virulent P. aeruginosa PA14. First of all, we could show that P. aeruginosa invasion significantly disrupted the biofilms formed by THOR. In addition, this disruption was greater, both in terms of reduction of biofilm biomass and changes in microbial community composition, during invasion with PA14 than for PAO1. This suggests that the virulence traits expressed by P. aeruginosa can also play a role in its ability to outcompete other microbes, i.e. that virulence and competition traits are linked.

Along these lines, we also studied the invasion ability of an O-antigen-deficient P. aeruginosa strain, which had even further reduced competitive ability. Finally, we investigated two naturally occurring P. aeruginosa isolates for their invasion ability in THOR: the clinical isolates CCUG73475 (from a patient with septic bacteremia) and CCUG71613 (from a patient with chronic pneumonia). Invasion with the pneumonia-derived isolate CCUG71613 resulted in minimal changes to the THOR community, while invasion with the sepsis-derived isolate CCUG73475 led to a reduction in the CFU counts of all THOR community members, suggesting differences in virulence and competition traits between different clinical isolates.

The paper was a collaboration with Lars Jelsbak and Mikkel Anbo at the Technical University of Denmark, and was conceived during a visit that Emil and I did there in 2022. Emil actually looked at a whole bunch of strains with their O-antigens replaced, but in the end the virulence story was the most coherent one we could tell, and the rest of the O-antigen data will likely be sitting on a shelf never to be released. Sometimes academia works like that. But I am happy about the outcome of our collaboration with Lars and Mikkel!

The study builds on our work on different disturbances to community interactions in THOR, where Emil has previous work on how temperature changes the interactions between the community members (3). We also have data on genes in P. aeruginosa that are important for community invasion in THOR which is in the pipeline to be submitted, along with a study on antibiotic exposure on P. aeruginosa. So even if Emil has defended his thesis the work on his work lives on!

References

1. Burman E, Anbo M, Jelsbak L, Bengtsson-Palme J: Virulent strains of Pseudomonas aeruginosa are more disruptive during invasion of a microbial model community. FEMS Microbiology Letters, fnag090 (Advance article) (2026). doi: 10.1093/femsle/fnag090
2. Lozano GL, Bravo JI, Garavito Diago MF, Park HB, Hurley A, Peterson SB, Stabb EV, Crawford JM, Broderick NA, Handelsman J: Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior. mBio, 10, 2, e02846-18 (2019). doi: 10.1128/mBio.02846-18
3. Burman E, Bengtsson-Palme J: Microbial community interactions are sensitive to small differences in temperature. Frontiers in Microbiology, 12, 672910 (2021). doi: 10.3389/fmicb.2021.672910

I am very happy to report on our most recently published paper – and the first one I have co-authored with my PhD supervisor Joakim Larsson in more than six years – which is a product of the BIOCIDE project. In the paper, published in FEMS Microbiology Ecology, Daniel Jaén-Luchoro has been collecting hundreds of MIC data points for biocides and metals to create the first complete and relatively comparable (more on that in a bit) dataset of bacterial sensitivity data for biocides (1).

This was a much bigger undertaking that in might sound like, but it is important for out understanding of how potent metals and biocides are for inhibiting bacterial growth. Basically, Daniel has been putting together an entire EUCAST (2,3) for biocides and metals, which is potentially extremely useful. This in turn can be used to better estimate the risks for co-selection of resistance to antibiotics.

Daniel was able to collect data for 53 antibacterial biocides, 21 metals, and 17 related compounds, resulting in 20,378 MIC values across 164 bacterial species. Of course the data is very overrepresented by clinically relevant organisms, including Staphylococcus aureus and Escherichia coli. Also, certain biocides were studied more than others, such as chlorhexidine and benzalkonium chloride, which was also true for metals that were mostly represented by copper, zinc, arsenic, cadmium and silver.

Importantly, we saw that there is a variety of method to measure MICs for biocides and metals, which means that a lot of the data is not directly comparable. Also, some authors did not even report the exact conditions they measured MICs under. This emphasizes the urgent need for standardized susceptibility testing methodology and consistent terminology for research on biocide and metal resistance. By centralizing MIC data, we provide an important foundation for future efforts to assess ecological risks and co-selection with antibiotic resistance. We aim to integrate this into an update to the BacMet database (4,5) that hopefully will be come available in the near future!

References

1. Jaén-Luchoro D, Larsson DGJ, Bengtsson-Palme J: Bacterial sensitivity distributions for biocides and metals. FEMS Microbiology Ecology, Advance article fiag075 (2026). http://dx.doi.org/10.1093/femsec/fiag075
2. https://mic.eucast.org
3. Kahlmeter G, Turnidge J: Wild-type distributions of minimum inhibitory concentrations and epidemiological cut-off values—laboratory and clinical utility. Clinical Microbiology Reviews 36:e00100-22. https://doi.org/10.1128/cmr.00100-22
4. http://bacmet.biomedicine.gu.se
5. Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DGJ: BacMet: Antibacterial biocide and metal resistance genes database. Nucleic Acids Research, 42, D1, D737–D743 (2014). http://dx.doi.org/10.1093/nar/gkt1252

I’ve been terribly bad at keeping updates on what we have published in the last year or so – there have just been a bit too much other things to do. So I thought it was time to take a look at what we have published in the last year before some really cool stuff hits the press this summer and fall (hopefully more on that soon!!)

Let’s start with some EMBARK/SEARCHER output. Several lab members (Anna, Marcus and I) have been involved in a paper using functional metagenomics to find novel cefiderocol resistance genes (1). We found four resistance genes, including three ꞵ-lactamases (VEB-3, an OXA-372 homolog, and a YbxI homolog) and a partial penicillin-binding protein homolog, none of which had been previously reported as a cefiderocol resistance gene. The blaVEB-3 gene was associated with a mobile genetic element. We could find three of them using shotgun metagenomics, showing that the blaVEB-3 gene was widespread across France, Sweden, Germany and Pakistan, hinting at efficient dissemination of this gene.

I have also been involved in a collaboration paper with Thomas Berendonk and Uli Klümper‘s labs, where we investigate if fish can be sentinels of environmental antibiotic resistance, and it turns out that they are… not great for that (2).

On the topic of antibiotic resistance gene (ARG) dissemination, Máté Vass (now at SLU) lead a study published in Communications Biology investigating how water stratification affects horizontal gene transfer, with a focus on ARGs (3). The main finding of this paper is that water stratification is a constraint on horizontal gene transfer, which may have implications on how we think about ARG spread through water environments.

While we are at the topic of large-scale quantifications of ARGs in big data sets, I was super-happy to be part of a collaboration with Katariina Pärnänen on how gender (and other factors) impact ARGs in the human microbiome (4). I kept telling Katariina that this would probably yield nothing – the microbiome data was too noisy, and the signal will get lost. Yet, she persisted, and indeed it turned out we are at the point where there is enough human microbiome data to get a signal even if there is a lot of noise. So hats off to Katariina, this was your “what did I say” moment with me!

Then we have a set of mechanistic AMR studies on ARG evolution. First, Lisa Teichmann published parts of her PhD thesis, first on the gradual evolution of fluoroquinolone resistance in E. coli (5) and how this is related to the SOS response in bacteria. She then followed up with a somewhat similar paper on amoxicillin evolution in E. coli (6). The general picture of these two papers on how E. coli adapts genetically to antibiotic stress is that resistance evolution is highly antibiotic-specific and that canonical stress-response or mutagenic pathways do not uniformly explain adaptive trajectories.

Somewhat connected, Nathália Abichabki recently published a paper where we propose screening cut-off values and tolerance disk tests (TDtests) for detection of tolerance/persistence to ceftazidime-avibactam in Klebsiella pneumoniae (7). This is also related to a bunch of papers on tolerance and low-level resistance to antibiotics that will be coming out of Nathália’s thesis, so there is more coming on this front soon!

Finally – on the AMR front – Anna Abramova led an effort together with Veronika Pettersen to investigate possibility for integration of AMR surveillance systems in the Nordic countries that recently got published in Public Health (8). Anna and Veronika did a huge amount of work on this paper, but this was largely the outcome of several meetings on the NoMoReAMR consortium, where we pinpointed missed opportunities for surveillance in the otherwise relatively homogenous Nordic countries. I hope to get to work more with this consortium in the future, as I think that we have had very fruitful discussions on both AMR research and monitoring and when and where it is useful.

And so two papers not related to AMR: We had a very nice collaboration with Daniel Bojar‘s group coming out late last year in Nature Communications, looking – from many different angles – at seal milk oligosaccharides and their potential uses. While the cool finding in this paper is that seal milk seems even more complex than human breast milk in terms of milk oligosaccharides (9), we did not contribute too much in that part. Instead, Mirjam Dannborg was studying the effects of these oligosaccharides on pathogen biofilms, work that will also be part of her PhD thesis when she defends this fall!

Finally, in a collaboration with colleagues in Brazil, we published a review article on the outlook for combining 3D organoid cultures and high-throughput analysis techniques to better understand host-pathogen interactions (10). This was the result of a cross-visit collaboration between Brazil and Sweden, where me and Mirjam visited the lab of Elaine de Martinis, and Elaine, Leonardo Andrade and Nathália Abichabki visited our lab back in 2023. It’s nice to see our discussions take paper form and I hope to be working more with this wonderful team in Brazil!

Papers mentioned:

1. Gschwind R, Bonnet M, Abramova A, Jarquín-Díaz VH, Wenne M, Löber U, Godron N, Kampouris ID, Tskhay F, Nahid F, Debroucker C, Bui-Hai M, El Aiba I, Klümper U, Berendonk TU, Forslund-Startceva SK, Zahra R, Bengtsson-Palme J, Ruppé E: Cefiderocol resistance genes identified in environmental samples using functional metagenomics. ISME Journal, 20, 1, wrag010 (2026). doi: 10.1093/ismejo/wrag010 [Paper link]
2. Tskhay F, Köbsch C, Elena AX, Bengtsson-Palme J, Berendonk TU, Klümper U: Fish are poor sentinels for surveillance of riverine antimicrobial resistance. One Health, 20, 101026 (2025). doi: 10.1016/j.onehlt.2025.101026 [Paper link]
3. Vass M, Abramova A, Bengtsson-Palme J: Antimicrobial resistance dissemination via horizontal gene transfer is constrained in stratified waters. Communications Biology, 9, 435 (2026). doi: 10.1038/s42003-026-09857-8 [Paper link]
4. Salehi M, Laitinen V, Bhanushali S, Bengtsson-Palme J, Collignon P, Beggs JJ, Pärnänen K, Lahti L: Gender differences in global antimicrobial resistance. npj Biofilms and Microbiomes, 11, 79 (2025). doi: 10.1038/s41522-025-00715-9 [Paper link]
5. Teichmann L, Luitwieler SH, Bengtsson-Palme J, ter Kuile BH: Fluoroquinolone-specific resistance trajectories in E. coli and their dependence on the SOS-response. BMC Microbiology, 27, 37 (2025). doi: 10.1186/s12866-025-03771-5 [Paper link]
6. Teichmann L, Wenne M, Luitweiler S, Dugar G, Bengtsson-Palme J, ter Kuile B: Genetic Adaptation to Amoxicillin in Escherichia coli: The Limited Role of dinB and katE. PLoS ONE, 20, 2, e0312223 (2025). doi: 10.1371/journal.pone.0312223 [Paper link]
7. Abichabki N, Bellissimo-Rodrigues F, Gaspar GG, Pocente RHC, Lima DAFS, Bollela VR, Braga GUL, De Martinis ECP, Ferreira JC, Darini ALC, Bengtsson-Palme J, Andrade LN: Proposal for screening cut-off values and use of Tolerance Disk Test (TDtest) for detection of tolerance/persistence to ceftazidime-avibactam in Klebsiella pneumoniae. Diagnostic Microbiology and Infectious Disease, 116, 3, 117515 (2026). doi: 10.1016/j.diagmicrobio.2026.117515 [Paper link]
8. Abramova A, Baral A, Osińska AD, Metsä-Simola N, Räisänen K, Ribeiro Duarte AS, Helgason KO, Halldórsdóttir AM, Pärnänen K, Skov Simonsen G, Sariola S, Lahti L, Bengtsson-Palme J, Wasteson Y, Munk P, Pettersen VK: Roadmap for integrated One Health AMR surveillance in Nordic countries. Public Health, 255, 106285 (2026). doi: 10.1016/j.puhe.2026.106285 [Paper link]
9. Jin C, Lundstrøm J, Cori CR, Guu S-Y, Bennett AR, Dannborg M, Bengtsson-Palme J, Hevey R, Khoo K-H, Bojar D: Seal milk oligosaccharides rival human milk complexity and exhibit functional dynamics during lactation. Nature Communications, 16, 10067 (2025). doi: 10.1038/s41467-025-66075-2 [Paper link]
10. de Martinis ECP, Alves VF, Pereira MG, Andrade LN, Abichabki N, Abramova A, Dannborg M, Bengtsson-Palme J: Applying 3D cultures and high-throughput technologies to study host-pathogen interactions. Frontiers in Immunology, 16 (2025). doi: 10.3389/fimmu.2025.1488699[Paper link]

I am happy to share the news that my first doctoral student – Emil Burman – successfully defended his thesis yesterday, and can now introduce himself as Dr. Burman.

And in what a way he defended! During the three hour defense, he was asked all the hard questions from his opponent – Akos Kovács – who did an amazing job bringing out Emil’s vast and diverse knowledge of the field. In fact, the committee noted afterwards that it took more than one and a half hours of questioning before Emil had to admit “I don’t know the answer to that”.

Emil’s thesis, titled “Genetic Contributions to Invasion and Biofilm Disruption in a Microbial Model Community“, used the microbial model community THOR (1) to investigate community responses to environmental stress and microbial invasion. The thesis (2) consists of five papers, the first dealing with how temperature affects THOR (3), the second with how pathogenicity is related to competition ability in a community setting, the third about the genetic determinants of antibiotic susceptibility in Pseudomonas aeruginosa, the fourth about invasion with P. aeruginosa into THOR, and the last one is a proteomics study about one of the strongest hits in paper IV.

Emil has used a range of techniques, including traditional microbiological assays, transposon mutagenesis (INSeq), and proteomics, to identify genetic determinants of community stability and disruption. This has allowed him to explore how cooperative traits emerge and how pathogens like Pseudomonas aeruginosa interfere with community dynamics. His thesis can be found in an online version here.

References

1. Lozano GL, Bravo JI, Garavito Diago MF, Park HB, Hurley A, Peterson SB, Stabb EV, Crawford JM, Broderick NA, Handelsman J: Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior. mBio, 10, 2, e02846-18 (2019). doi: 10.1128/mBio.02846-18
2. Burman E: Genetic Contributions to Invasion and Biofilm Disruption in a Microbial Model Community. PhD Thesis, University of Gothenburg (2025). https://gupea.ub.gu.se/handle/2077/87262
3. Burman E, Bengtsson-Palme J: Microbial community interactions are sensitive to small differences in temperature. Frontiers in Microbiology, 12, 672910 (2021). doi: 10.3389/fmicb.2021.672910

I have written an editorial piece for the Swedish Pathogens Portal in which I reflect a bit on the upcoming EU legislation requiring monitoring of AMR in major wastewater treatment plants (1). I also veer a bit into where environmental monitoring outside of sewage may play a role, using our review paper resulting from EMBARK as the starting point (2).

This is timed to coincide with the registration deadlines for two upcoming workshops on AMR surveillance in the environment; the first being the DDLS Symposium on Data-Driven Environmental Monitoring of Infectious Disease on 7th-8th October in Uppsala, which I have been part of organising. The second is a workshop organised by CARe in Gothenburg on 28th October on the theme of sewage surveillance of antibiotic resistance, focusing on the new EU requirements.

My hope is that you will be a bit provoked by this and come to one of these workshops to discuss AMR surveillance and where to go next!

1. Bengtsson-Palme J: Surveillance of antimicrobial resistance – flying blind or flying behind? The Swedish Pathogens Portal, Editorial (2024). doi: 10.17044/scilifelab.27045433 [Link]
2. Bengtsson-Palme J, Abramova A, Berendonk TU, Coelho LP, Forslund SK, Gschwind R, Heikinheimo A, Jarquin-Diaz VH, Khan AA, Klümper U, Löber U, Nekoro M, Osińska AD, Ugarcina Perovic S, Pitkänen T, Rødland EK, Ruppé E, Wasteson Y, Wester AL, Zahra R: Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement it, and what are the data needs? Environment International, 178, 108089 (2023). doi: 10.1016/j.envint.2023.108089 [Paper link]

Last week, FEMS Microbes published our most recent work on the genomes of Escherichia coli in coastal marine sediments from the Helsingborg area in Sweden (1). Part of our sampled area was next to the discharge point of the city’s wastewater treatment plant (WWTP) effluent. We discovered that the E. coli population in these sediment is diverse, containing serotypes typically associated with both humans, livestock and other animals. We also found that virulence genes were more common among the isolates collected closer to the WWTP discharge site. Only one isolate was phenotypically antibiotic resistant, and carried corresponding tetracycline resistance genes on a plasmid. All isolates were halotolerant, growing at 3.5% NaCl. Since most isolates were also good at forming biofilm, this suggests that marine sediments can select for E. coli with increased survival properties and could be a potential reservoir for E. coli that could be spread to humans when the sediments are disturbed. Furthermore, the naturalisation of these E. coli questions it as an indicator for faecal contamination of marine sediments.

The paper is primarily the work of Isabel Erb, Carolina Suarez, and Catherine Paul at Lund University, and they have made a terrific job on this while I have mostly provided some input on the bioinformatics and genomics analyses. The study is a nice example of how genomics analysis could nuance monitoring for pathogens and antibiotic resistance in environments close to human activities. Since these sediments are also closely connected to humans in terms of exposure – the Helsingborg beach is in the neighbouring area – this highlight potential exposure routes for pathogens and antibiotic resistance (2).

The finding of a single antibiotic resistant isolate highlights the issue of comparing between different monitoring methods (2). While a single isolates might be consider a small number, it is really hard to compare if this is outside of the normal range of resistance (3) as measured by, e.g., qPCR. This further points to the importance of standardisation of antibiotic resistance monitoring in the environment, in a way that is both reliable, feasible and economic. That said, it also shows the potential in monitoring, for example, public beaches for pathogens and resistance, and how this could be used to better design and implement mitigation strategies, including the temporary closing of public beaches in contaminated areas. For this to work, however, a better knowledge of the background levels of resistance is required, as we have been working on in the EMBARK program.

References

1. Erb IK, Suarez C, Frank EM, Bengtsson-Palme J, Lindberg E, Paul CJ: Escherichia coli in urban marine sediments: interpreting virulence, biofilm formation, halotolerance and antibiotic resistance to infer contamination or naturalisation. FEMS Microbes (advance article) xtae024 (2024). doi: 10.1093/femsmc/xtae024
2. Bengtsson-Palme J, Abramova A, Berendonk TU, Coelho LP, Forslund SK, Gschwind R, Heikinheimo A, Jarquin-Diaz VH, Khan AA, Klümper U, Löber U, Nekoro M, Osińska AD, Ugarcina Perovic S, Pitkänen T, Rødland EK, Ruppé E, Wasteson Y, Wester AL, Zahra R: Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement it, and what are the data needs? Environment International, 178, 108089 (2023). doi: 10.1016/j.envint.2023.108089
3. Abramova A, Berendonk TU, Bengtsson-Palme J: A global baseline for qPCR-determined antimicrobial resistance gene prevalence across environments. Environment International, 178, 108084 (2023). doi: 10.1016/j.envint.2023.108084

This paper came out just about the same time as the PhD position with Erik Kristiansson where I will be co-supervisor was announced, and I did not want to steal that thunder with another news item, but it is now time to highlight the fantastic work of Víctor Hugo Jarquín-Díaz on antibiotic resistance genes in the gut microbiomes of mice across a gradient of pure and hybrid genotypes in the European house mouse hybrid zone. This came out in mid-April in ISME Communications and presents the interesting hypothesis that hybridisation not only shapes bacterial communities, but also antibiotic resistance gene occurrences (1).

This study is based on 16S rRNA amplicon sequencing of gut bacteria in natural populations of house mice. From this we have predicted the antibiotic resistance gene composition in the microbiomes, and found a significant increase in the predicted antibiotic resistance gene richness in hybrid mice. In other words, more and different antibiotic resistance genes were found in the hybrid mice than in the non-hybridised individuals. We believe that this could be due to a disruption of the microbiome composition in hybrid mice. The aberrant microbiomes in hybrids represent less complex communities, potentially promoting selection for resistance.

It deserves to be mentioned that this is more of a pilot study, which we hope to follow up with a more proper study targeting the resistance genes in the mice microbiomes. That said, our work suggests that host genetic variation impacts the gut microbiome and antibiotic resistance gene, at least in mice. This raises further questions on how the mammalian host genetics impact antibiotic resistance carriage in bacteria via microbiome dynamics or interaction with the environment.

I am very happy to have been part of this EMBARK collaboration with the Sofia Forslund-Startceva and Emanuel Heitlinger labs! And I am especially thankful to Víctor who pulled off this very thought-inducing study!

Reference

1. Jarquín-Díaz VH, Ferreira SCM, Balard A, Ďureje Ľ, Macholán M, Piálek J, Bengtsson-Palme J, Kramer-Schadt S, Forslund-Startceva SK, Heitlinger E: Aberrant microbiomes are associated with increased antibiotic resistance gene load in hybrid mice. ISME Communications, ycae053 (2024). doi: 10.1093/ismeco/ycae053 [Paper link]

I have had the fortune to be involved in a study on the quality of reference material for mosquito barcoding for biodiversity studies. The study, which was led by Maurício Moraes Zenker at the Universidade Federal de São Carlos in Brazil, looked at the availability of public data for mosquitoes in online databases for two widely used DNA barcoding markers in Culicidae: the COI and ITS2 regions (1). Last week, this study was published in Scientific Reports.

The paper shows that around 30% of known species were covered for the COI gene in BOLD  and  GenBank, and 12% of species for ITS2 in GenBank. The Afrotropical, Australian and Oriental biogeographic regions had the lowest coverages, while the Nearctic, Palearctic and Oceanian regions had the highest. Countries with a higher diversity of mosquitoes tended to have lower coverage, which was surprisingly also the case for countries with higher numbers of medically important species. At the same time, countries with a higher number of endemic species tended to have a higher species coverage in the databases.

With this study, we would like to advocate for better curatorship of voucher specimens representing sequences in the databases. Also, an integrative taxonomic approach that combines various genetic markers with morphological analyses is important to allow a better use of DNA barcoding and metabarcoding in a diverse array of applications, including vector species detection and biodiversity monitoring.

Importantly, this work underscores how reliant DNA barcoding is on proper taxonomic foundations, including morphological characterisations. Molecular identification of species cannot happen in a vacuum! I would like to extend a big thanks for Maurício who invited me to take part in this study and who have done an excellent job putting it all together!

Reference

• Moraes Zenker M, Pineda Portella T, Costa Pessoa FA, Bengtsson-Palme J, Galetti PM: Low coverage of species constrains the use of DNA barcoding to assess mosquito biodiversity. Scientific Reports, 14, 7432 (2024). doi: 10.1038/s41598-024-58071-1 [Paper link]

In just a few days, Environment International has published two papers coming out from the EMBARK consortium which are somewhat connected to each other.

The first (or technically the second, but the other order makes more sense when explaining this…) is the first paper involving most of the people who have been working in the EMBARK consortium for an extended period of time. It’s an overview paper titled “Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement Itit, and what are the data needs?” (1) and I think the title describes the topic pretty well. Basically, we go through why it would be interesting to monitor for antibiotic resistance in the environments, how that could be implemented and what we would need to know to get there.

The very condensed story is that if one is considering implementing monitoring for environmental resistance, these are a few things that should be considered:

• The purpose of monitoring: What is the motivation? What should be achieved? What type of risk should be assessed? What type of action would monitoring enable?
• Choice of methods: Which methods are economically feasible? Which methods would deliver results within a useful timeframe for taking appropriate actions?
• Targeted environments: In what type of environment would monitoring for a given purpose be worthwhile?
• Intended users: Who would be able to use, implement and act upon this strategy?
• Integration potential: How does this monitoring integrate with other monitoring efforts? How can the resulting data be communicated?

We then dive into the knowledge gaps we are currently facing, and particularly highlight the following areas:

• Establish how different existing methods for monitoring resistance compare to each other
• Extend pathogen-centric databases for resistance genes with latent resistance genes (2)
• Determine the locations and type of environments relevant for resistance monitoring
  To reduce costs, utilizing already existing environmental monitoring should be prioritized, as should locations integrated into operating or planned surveillance programs. More efforts should also be made to identify additional pathways for resistance transmission through the environment.
• Study the environment as a source and transmission route for antibiotic resistance
  Stratify risks associated with resistance genes found in the environment. Define typical levels of antibiotic resistance in different environments (3), and define how these levels change over time.
• Identify settings where the relationship between fecal bacteria and antibiotic resistance is absent
  Usually, these levels follow each other, but the environments where they don’t are important as they deviate from the expected baseline of resistance. This knowledge can aid in identifying situations in which it would be helpful to investigate a microbial community for resistance to specific antibiotics.
• Identify the origins for more antibiotic resistance genes (4)
  This knowledge will be instrumental in preventing the emergence of new forms of resistance in pathogens in the future.

An important outcome of this paper is that we realise that we are still not at a level of understanding where routine monitoring for resistance in the environment can be easily justified or implemented. Still, there is a need for monitoring data in natural environments to even get started, and therefore we support the implementation of national, regional and global of initiatives without having all the scientific answers. The lack of comprehensive understanding should not be an obstacle to starting environmental monitoring for AMR, nor for action against environmental development and spread of AMR.

The second paper is very much related to the first, in that it actually tries to address one of these knowledge gaps: the need for normal background levels of antibiotic resistance in different environments. In this paper, Anna Abramova did an herculean effort collecting (we hope) all qPCR data on antibiotic resistance gene abundances in the environment for the past two decades. All in all, she collected data for more than 1500 samples across 150 studies and integrated these into an analys of what we could consider normal levels of resistance in different environments.

For an ‘average’ resistance gene, we found that the normal relative abundance range was form 10^-5 to 10^-3 copies per bacterial 16S rRNA, or that around one in 1,000 bacteria would carry a given resistance gene. This level varied quite a bit between different resistance genes, however, but not so much between environmental types (except for in human and animal feces, where some resistance genes were clearly more abundant, most prominently tetracycline resistance genes). What was more striking was that there was a clear difference between environments impacted or likely impacted by human activities, as opposed to more pristine environments with little to none human impact. Some resistance genes, such as tetA, tetG, blaTEM and blaCTX-M, showed very marked differences between these impacted and non-impacted environments, making them great markers of human-activity-associated resistance.

Our final recommendations with regards to monitoring include:

• Include the intI1, sul1, blaTEM, blaCTX-M and qnrS genes in environmental monitoring, along with a selection of tetracycline resistance genes, including either tetA or tetG.
• Other potential target genes could be sul3, vanA, tetH, aadA2, floR, ereA and mexF, which are abundant in some environments, but are not often included in qPCR studies of environmental AMR
• If a gene deviates from the expected 10^-5 to 10^-3 interval, this warrants further investigation of the causes.
• Maximum acceptable levels need to be determined not only taking relative abundances of genes into account, but also risks to human health as well as the numbers of bacteria in a given volume of sample into account (5,6) and transmission routes to humans (7)
• The different standards of reporting DNA abundances constituted a complicating factor for this study. Both abundances of resistance genes relative to the 16S rRNA gene and to the sample volume or weight should be reported.
• The absence of clear trends of increases or decreases in resistance gene abundances over time indicates a need for more systematic time series data in a variety of environments.

Our results also highlighted the scarcity of resistance gene data from parts of the world, particularly from Africa and South America, and underscores the need for a concerted effort to quantify typical background levels of resistance in the environment more broadly to enable efficient environmental surveillance schemes akin to those that exist in clinical and veterinary settings.

I encourage anyone with an interesting these topics to at least skim the full papers [Monitoring overview paper here, Normal qPCR resistance abundances here]. These will be great resources and I am very proud of them both. I would really like to thank the entire EMBARK team and our collaborators in CORNELIA, WastPAN and in other organisations. I would also like to thank Anna for her hard work on collecting and analysing the qPCR data for around two years. It has been a long ride, and I think we are both happy, proud and a bit relieved to finally see this paper published!

References

1. Bengtsson-Palme J, Abramova A, Berendonk TU, Coelho LP, Forslund SK, Gschwind R, Heikinheimo A, Jarquin-Diaz VH, Khan AA, Klümper U, Löber U, Nekoro M, Osińska AD, Ugarcina Perovic S, Pitkänen T, Rødland EK, Ruppé E, Wasteson Y, Wester AL, Zahra R: Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement it, and what are the data needs? Environment International, 108089 (2023). doi: 10.1016/j.envint.2023.108089
2. Inda-Díaz JS, Lund D, Parras-Moltó M, Johnning A, Bengtsson-Palme J, Kristiansson E: Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome, 11, 44 (2023). doi: 10.1186/s40168-023-01479-0
3. Abramova A, Berendonk TU, Bengtsson-Palme J: A global baseline for qPCR-determined antimicrobial resistance gene prevalence across environments. Environment International, 178, 108084 (2023). doi: 10.1016/j.envint.2023.108084
4. Ebmeyer S, Kristiansson E, Larsson DGJ: A framework for identifying the recent origins of mobile antibiotic resistance genes. Communications Biology, 4 (2021). doi:10.1038/s42003-020-01545-5
5. Larsson DGJ, Andremont A, Bengtsson-Palme J, Brandt KK, de Roda Husman AM, Fagerstedt P, Fick J, Flach C-F, Gaze WH, Kuroda M, Kvint K, Laxminarayan R, Manaia CM, Nielsen KM, Ploy M-C, Segovia C, Simonet P, Smalla K, Snape J, Topp E, van Hengel A, Verner-Jeffreys DW, Virta MPJ, Wellington EM, Wernersson A-S: Critical knowledge gaps and research needs related to the environmental dimensions of antibiotic resistance. Environment International, 117, 132–138 (2018). doi: 10.1016/j.envint.2018.04.041
6. Pruden A, Larsson DGJ, Amézquita A, Collignon P, Brandt KK, Graham DW, et al. Management options for reducing the release of antibiotics and antibiotic resistance genes to the environment. Environmental Health Perspectives, 121, 878–885 (2013). doi:10.1289/ehp.1206446
7. Bengtsson-Palme J, Kristiansson E, Larsson DGJ: Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews, 42, 1, 68–80 (2018). doi: 10.1093/femsre/fux053